no code implementations • ICML 2020 • Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang
There are existing efforts that model the training dynamics of GANs in the parameter space but the analysis cannot directly motivate practically effective stabilizing methods.
no code implementations • ICML 2020 • Michael Zhu, Chang Liu, Jun Zhu
Particle-based Variational Inference methods (ParVIs), like Stein Variational Gradient Descent, are nonparametric variational inference methods that optimize a set of particles to best approximate a target distribution.
no code implementations • ECCV 2020 • Yueru Li, Shuyu Cheng, Hang Su, Jun Zhu
Based on our investigation, we further present a new robust learning algorithm which encourages a larger gradient component in the tangent space of data manifold, suppressing the gradient leaking phenomenon consequently.
1 code implementation • 17 Apr 2024 • Yichi Zhang, Yinpeng Dong, Siyuan Zhang, Tianzan Min, Hang Su, Jun Zhu
To achieve this, we propose Transferable Visual Prompting (TVP), a simple and effective approach to generate visual prompts that can transfer to different models and improve their performance on downstream tasks after trained on only one model.
no code implementations • 16 Apr 2024 • Kafeng Wang, Jianfei Chen, He Li, Zhenpeng Mi, Jun Zhu
Diffusion models have been extensively used in data generation tasks and are recognized as one of the best generative models.
no code implementations • 2 Apr 2024 • Yuezhou Hu, Kang Zhao, Weiyu Huang, Jianfei Chen, Jun Zhu
Training large Transformers is slow, but recent innovations on GPU architecture gives us an advantage.
1 code implementation • 1 Apr 2024 • Ruowen Zhao, Zhengyi Wang, Yikai Wang, Zihan Zhou, Jun Zhu
However, due to the challenge of directly deforming the mesh representation to approach the target topology, most methodologies learn an implicit representation (such as NeRF) during the sparse-view reconstruction and acquire the target mesh by a post-processing extraction.
no code implementations • 31 Mar 2024 • Lingxuan Wu, Xiao Yang, Yinpeng Dong, Liuwei Xie, Hang Su, Jun Zhu
The vulnerability of deep neural networks to adversarial patches has motivated numerous defense strategies for boosting model robustness.
no code implementations • 21 Mar 2024 • Junliang Ye, Fangfu Liu, Qixiu Li, Zhengyi Wang, Yikai Wang, Xinzhou Wang, Yueqi Duan, Jun Zhu
Building upon the 3D reward model, we finally perform theoretical analysis and present the Reward3D Feedback Learning (DreamFL), a direct tuning algorithm to optimize the multi-view diffusion models with a redefined scorer.
no code implementations • 19 Mar 2024 • Haocheng Xi, Yuxiang Chen, Kang Zhao, Kaijun Zheng, Jianfei Chen, Jun Zhu
Moreover, for a standard transformer block, our method offers an end-to-end training speedup of 1. 42x and a 1. 49x memory reduction compared to the FP16 baseline.
no code implementations • 8 Mar 2024 • Zhengyi Wang, Yikai Wang, Yifei Chen, Chendong Xiang, Shuo Chen, Dajiang Yu, Chongxuan Li, Hang Su, Jun Zhu
In this work, we present the Convolutional Reconstruction Model (CRM), a high-fidelity feed-forward single image-to-3D generative model.
1 code implementation • 6 Mar 2024 • Zhongkai Hao, Chang Su, Songming Liu, Julius Berner, Chengyang Ying, Hang Su, Anima Anandkumar, Jian Song, Jun Zhu
Pre-training has been investigated to improve the efficiency and performance of training neural operators in data-scarce settings.
1 code implementation • 27 Feb 2024 • Ziteng Wang, Jianfei Chen, Jun Zhu
On all the tasks, VCAS can preserve the original training loss trajectory and validation accuracy with an up to 73. 87% FLOPs reduction of BP and 49. 58% FLOPs reduction of the whole training process.
1 code implementation • 26 Feb 2024 • Haoyang Li, Jing Zhang, Hanbing Liu, Ju Fan, Xiaokang Zhang, Jun Zhu, Renjie Wei, Hongyan Pan, Cuiping Li, Hong Chen
To address the limitations, we introduce CodeS, a series of pre-trained language models with parameters ranging from 1B to 15B, specifically designed for the text-to-SQL task.
no code implementations • 26 Feb 2024 • Tianjiao Luo, Tim Pearce, Huayu Chen, Jianfei Chen, Jun Zhu
Generative Adversarial Imitation Learning (GAIL) trains a generative policy to mimic a demonstrator.
no code implementations • 23 Feb 2024 • Yu Tian, Xiao Yang, Yinpeng Dong, Heming Yang, Hang Su, Jun Zhu
It allows users to design specific prompts to generate realistic images through some black-box APIs.
1 code implementation • 8 Feb 2024 • Huayu Chen, Guande He, Hang Su, Jun Zhu
Existing alignment methods, such as Direct Preference Optimization (DPO), are mainly tailored for pairwise preference data where rewards are implicitly defined rather than explicitly given.
1 code implementation • 4 Feb 2024 • Huanran Chen, Yinpeng Dong, Shitong Shao, Zhongkai Hao, Xiao Yang, Hang Su, Jun Zhu
Diffusion models are recently employed as generative classifiers for robust classification.
no code implementations • 1 Feb 2024 • Songming Liu, Chang Su, Jiachen Yao, Zhongkai Hao, Hang Su, Youjia Wu, Jun Zhu
Physics-informed neural networks (PINNs) have shown promise in solving various partial differential equations (PDEs).
2 code implementations • 1 Feb 2024 • Haozhe Ji, Cheng Lu, Yilin Niu, Pei Ke, Hongning Wang, Jun Zhu, Jie Tang, Minlie Huang
We prove that EXO is guaranteed to optimize in the same direction as the RL algorithms asymptotically for arbitary parametrization of the policy, while enables efficient optimization by circumventing the complexities associated with RL algorithms.
1 code implementation • 13 Dec 2023 • Ziqi Yuan, Liyuan Wang, Wenbo Ding, Xingxing Zhang, Jiachen Zhong, Jianyong Ai, Jianmin Li, Jun Zhu
A commonly-used strategy for supervised IOD is to encourage the current model (as a student) to mimic the behavior of the old model (as a teacher), but it generally fails in SSIOD because a dominant number of object instances from old and new classes are coexisting and unlabelled, with the teacher only recognizing a fraction of them.
no code implementations • 6 Dec 2023 • Zehua Chen, Guande He, Kaiwen Zheng, Xu Tan, Jun Zhu
Specifically, we leverage the latent representation obtained from text input as our prior, and build a fully tractable Schrodinger bridge between it and the ground-truth mel-spectrogram, leading to a data-to-data process.
no code implementations • 1 Dec 2023 • Kehui Yao, Jingyi Huang, Jun Zhu
Effective management of environmental resources and agricultural sustainability heavily depends on accurate soil moisture data.
1 code implementation • 9 Nov 2023 • Shilong Liu, Hao Cheng, Haotian Liu, Hao Zhang, Feng Li, Tianhe Ren, Xueyan Zou, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang, Jianfeng Gao, Chunyuan Li
LLaVA-Plus is a general-purpose multimodal assistant that expands the capabilities of large multimodal models.
Ranked #1 on LMM real-life tasks on Leaderboard
1 code implementation • 6 Nov 2023 • Jianhui Li, Shilong Liu, Zidong Liu, Yikai Wang, Kaiwen Zheng, Jinghui Xu, Jianmin Li, Jun Zhu
With the success of Neural Radiance Field (NeRF) in 3D-aware portrait editing, a variety of works have achieved promising results regarding both quality and 3D consistency.
1 code implementation • 21 Oct 2023 • Liyuan Wang, Jingyi Xie, Xingxing Zhang, Hang Su, Jun Zhu
In this work, we present a general framework for continual learning of sequentially arrived tasks with the use of pre-training, which has emerged as a promising direction for artificial intelligence systems to accommodate real-world dynamics.
1 code implementation • NeurIPS 2023 • Kaiwen Zheng, Cheng Lu, Jianfei Chen, Jun Zhu
In this work, we propose a novel formulation towards the optimal parameterization during sampling that minimizes the first-order discretization error of the ODE solution.
no code implementations • 18 Oct 2023 • Guande He, Peng Cui, Jianfei Chen, WenBo Hu, Jun Zhu
Despite the significant progress made in practical applications of aligned language models (LMs), they tend to be overconfident in output answers compared to the corresponding pre-trained LMs.
1 code implementation • 11 Oct 2023 • Huayu Chen, Cheng Lu, Zhengyi Wang, Hang Su, Jun Zhu
Recent developments in offline reinforcement learning have uncovered the immense potential of diffusion modeling, which excels at representing heterogeneous behavior policies.
1 code implementation • NeurIPS 2023 • Liyuan Wang, Jingyi Xie, Xingxing Zhang, Mingyi Huang, Hang Su, Jun Zhu
Following these empirical and theoretical insights, we propose Hierarchical Decomposition (HiDe-)Prompt, an innovative approach that explicitly optimizes the hierarchical components with an ensemble of task-specific prompts and statistics of both uninstructed and instructed representations, further with the coordination of a contrastive regularization strategy.
1 code implementation • 21 Sep 2023 • Yinpeng Dong, Huanran Chen, Jiawei Chen, Zhengwei Fang, Xiao Yang, Yichi Zhang, Yu Tian, Hang Su, Jun Zhu
By attacking white-box surrogate vision encoders or MLLMs, the generated adversarial examples can mislead Bard to output wrong image descriptions with a 22% success rate based solely on the transferability.
1 code implementation • 5 Sep 2023 • Xu Si, Xinming Wu, Hanlin Sheng, Jun Zhu, Zefeng Li
Training specific deep learning models for particular tasks is common across various domains within seismology.
1 code implementation • 29 Aug 2023 • Feng Zhou, Quyu Kong, Zhijie Deng, Fengxiang He, Peng Cui, Jun Zhu
This paper presents a novel extension of multi-task Gaussian Cox processes for modeling multiple heterogeneous correlated tasks jointly, e. g., classification and regression, via multi-output Gaussian processes (MOGP).
1 code implementation • 29 Aug 2023 • Liyuan Wang, Xingxing Zhang, Qian Li, Mingtian Zhang, Hang Su, Jun Zhu, Yi Zhong
Continual learning aims to empower artificial intelligence (AI) with strong adaptability to the real world.
no code implementations • 19 Jul 2023 • Ye Ouyang, Yaqin Zhang, Peng Wang, Yunxin Liu, Wen Qiao, Jun Zhu, Yang Liu, Feng Zhang, Shuling Wang, Xidong Wang
6G is the next-generation intelligent and integrated digital information infrastructure, characterized by ubiquitous interconnection, native intelligence, multi-dimensional perception, global coverage, green and low-carbon, native network security, etc.
no code implementations • 24 Jun 2023 • Xu Si, Xinming Wu, Zefeng Li, Shenghou Wang, Jun Zhu
Overall, our study provides for the first time a prototype self-consistent all-in-one system of simultaneous seismic phase picking, association, and location, which has the potential for next-generation autonomous earthquake monitoring.
1 code implementation • NeurIPS 2023 • Haocheng Xi, Changhao Li, Jianfei Chen, Jun Zhu
To achieve this, we carefully analyze the specific structures of activation and gradients in transformers to propose dedicated quantizers for them.
no code implementations • 18 Jun 2023 • Tianjiao Luo, Ziyu Zhu, Jianfei Chen, Jun Zhu
We theoretically prove that the training process of DiracGANs-BMC is globally exponential stable and derive bounds on the rate of convergence.
1 code implementation • 15 Jun 2023 • Zhongkai Hao, Jiachen Yao, Chang Su, Hang Su, Ziao Wang, Fanzhi Lu, Zeyu Xia, Yichi Zhang, Songming Liu, Lu Lu, Jun Zhu
In addition to providing a standardized means of assessing performance, PINNacle also offers an in-depth analysis to guide future research, particularly in areas such as domain decomposition methods and loss reweighting for handling multi-scale problems and complex geometry.
no code implementations • 5 Jun 2023 • Jiachen Yao, Chang Su, Zhongkai Hao, Songming Liu, Hang Su, Jun Zhu
Physics-informed Neural Networks (PINNs) have recently achieved remarkable progress in solving Partial Differential Equations (PDEs) in various fields by minimizing a weighted sum of PDE loss and boundary loss.
1 code implementation • 30 May 2023 • Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Ze Cheng, Jun Zhu
The neural operator has emerged as a powerful tool in learning mappings between function spaces in PDEs.
1 code implementation • 30 May 2023 • Guande He, Jianfei Chen, Jun Zhu
In light of these observations, we evaluate the calibration of several methods that preserve pre-trained features and show that preserving pre-trained features can improve the calibration of fine-tuned language models.
1 code implementation • 28 May 2023 • Zhanhao Hu, Jun Zhu, Bo Zhang, Xiaolin Hu
Recent works found that deep neural networks (DNNs) can be fooled by adversarial examples, which are crafted by adding adversarial noise on clean inputs.
1 code implementation • 26 May 2023 • Min Zhao, Rongzhen Wang, Fan Bao, Chongxuan Li, Jun Zhu
This paper presents \emph{ControlVideo} for text-driven video editing -- generating a video that aligns with a given text while preserving the structure of the source video.
2 code implementations • NeurIPS 2023 • Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, Jun Zhu
In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i. e., $7. 5$).
2 code implementations • 24 May 2023 • Huanran Chen, Yinpeng Dong, Zhengyi Wang, Xiao Yang, Chengqi Duan, Hang Su, Jun Zhu
Since our method does not require training on particular adversarial attacks, we demonstrate that it is more generalizable to defend against multiple unseen threats.
Ranked #2 on Adversarial Defense on CIFAR-10
1 code implementation • 6 May 2023 • Kaiwen Zheng, Cheng Lu, Jianfei Chen, Jun Zhu
The probability flow ordinary differential equation (ODE) of diffusion models (i. e., diffusion ODEs) is a particular case of continuous normalizing flows (CNFs), which enables deterministic inference and exact likelihood evaluation.
Ranked #1 on Image Generation on ImageNet 32x32 (bpd metric)
3 code implementations • 25 Apr 2023 • Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, Jun Zhu
The main challenge for this setting is that the intermediate guidance during the diffusion sampling procedure, which is jointly defined by the sampling distribution and the energy function, is unknown and is hard to estimate.
1 code implementation • 20 Apr 2023 • Jun Zhu, Jiandong Jin, Zihan Yang, Xiaohao Wu, Xiao Wang
The averaged visual tokens and text tokens are concatenated and fed into a fusion Transformer for multi-modal interactive learning.
1 code implementation • CVPR 2023 • Jianhui Li, Jianmin Li, Haoji Zhang, Shilong Liu, Zhengyi Wang, Zihao Xiao, Kaiwen Zheng, Jun Zhu
As for imprecise image editing, we attribute the problem to the gap between the latent space of real images and that of generated images.
1 code implementation • ICCV 2023 • Shilong Liu, Tianhe Ren, Jiayu Chen, Zhaoyang Zeng, Hao Zhang, Feng Li, Hongyang Li, Jun Huang, Hang Su, Jun Zhu, Lei Zhang
We point out that the unstable matching in DETR is caused by a multi-optimization path problem, which is highlighted by the one-to-one matching design in DETR.
1 code implementation • 31 Mar 2023 • Chendong Xiang, Fan Bao, Chongxuan Li, Hang Su, Jun Zhu
Large-scale diffusion models like Stable Diffusion are powerful and find various real-world applications while customizing such models by fine-tuning is both memory and time inefficient.
1 code implementation • CVPR 2023 • Xiao Yang, Chang Liu, Longlong Xu, Yikai Wang, Yinpeng Dong, Ning Chen, Hang Su, Jun Zhu
The goal of this work is to develop a more reliable technique that can carry out an end-to-end evaluation of adversarial robustness for commercial systems.
no code implementations • 20 Mar 2023 • Yinpeng Dong, Caixin Kang, Jinlai Zhang, Zijian Zhu, Yikai Wang, Xiao Yang, Hang Su, Xingxing Wei, Jun Zhu
3D object detection is an important task in autonomous driving to perceive the surroundings.
2 code implementations • 16 Mar 2023 • Huanran Chen, Yichi Zhang, Yinpeng Dong, Xiao Yang, Hang Su, Jun Zhu
It is widely recognized that deep learning models lack robustness to adversarial examples.
3 code implementations • 12 Mar 2023 • Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu
Inspired by the unified view, UniDiffuser learns all distributions simultaneously with a minimal modification to the original diffusion model -- perturbs data in all modalities instead of a single modality, inputs individual timesteps in different modalities, and predicts the noise of all modalities instead of a single modality.
7 code implementations • 9 Mar 2023 • Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang
To effectively fuse language and vision modalities, we conceptually divide a closed-set detector into three phases and propose a tight fusion solution, which includes a feature enhancer, a language-guided query selection, and a cross-modality decoder for cross-modality fusion.
Ranked #1 on Zero-Shot Object Detection on MSCOCO
no code implementations • 9 Mar 2023 • Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Songming Liu, Dong Yan, Jun Zhu
Extensive experiments in both image-based and state-based tasks show that TAD can significantly improve the performance of handling different tasks simultaneously, especially for those with high TDR, and display a strong generalization ability to unseen tasks.
1 code implementation • 6 Mar 2023 • Dejun Feng, Hongyu Chen, Suning Liu, Ziyang Liao, Xingyu Shen, Yakun Xie, Jun Zhu
Finally, to obtain more complete saliency maps, we consider the uniqueness of the last layer of the decoder for the first time and propose the adaptive feedback refinement (AFR) module, which further refines feature representation and eliminates differences between features through a unique feedback mechanism.
no code implementations • 2 Mar 2023 • Shuai Xiao, Le Guo, Zaifan Jiang, Lei Lv, Yuanbo Chen, Jun Zhu, Shuang Yang
Furthermore, we show that the dual problem can be solved by policy learning, with the optimal dual variable being found efficiently via bisection search (i. e., by taking advantage of the monotonicity).
no code implementations • 1 Mar 2023 • Yichi Zhang, Zijian Zhu, Hang Su, Jun Zhu, Shibao Zheng, Yuan He, Hui Xue
In this paper, we propose Adversarial Semantic Contour (ASC), an MAP estimate of a Bayesian formulation of sparse attack with a deceived prior of object contour.
no code implementations • 28 Feb 2023 • Chang Liu, Yinpeng Dong, Wenzhao Xiang, Xiao Yang, Hang Su, Jun Zhu, Yuefeng Chen, Yuan He, Hui Xue, Shibao Zheng
In our benchmark, we evaluate the robustness of 55 typical deep learning models on ImageNet with diverse architectures (e. g., CNNs, Transformers) and learning algorithms (e. g., normal supervised training, pre-training, adversarial training) under numerous adversarial attacks and out-of-distribution (OOD) datasets.
2 code implementations • 28 Feb 2023 • Zhongkai Hao, Zhengyi Wang, Hang Su, Chengyang Ying, Yinpeng Dong, Songming Liu, Ze Cheng, Jian Song, Jun Zhu
However, there are several challenges for learning operators in practical applications like the irregular mesh, multiple input functions, and complexity of the PDEs' solution.
2 code implementations • NeurIPS 2023 • Zebin You, Yong Zhong, Fan Bao, Jiacheng Sun, Chongxuan Li, Jun Zhu
In an effort to further advance semi-supervised generative and classification tasks, we propose a simple yet effective training strategy called dual pseudo training (DPT), built upon strong semi-supervised learners and diffusion models.
no code implementations • 10 Feb 2023 • Peng Cui, Yang Yue, Zhijie Deng, Jun Zhu
Deep neural networks (DNNs) have achieved remarkable success in a variety of computer vision tasks, where massive labeled images are routinely required for model optimization.
1 code implementation • 5 Feb 2023 • Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li, Jun Zhu
Theoretically, the paper considers the surrogate loss instead of the zero-one loss in analyses and generalizes the classical results from binary cases to multiclass ones.
1 code implementation • 31 Jan 2023 • Liyuan Wang, Xingxing Zhang, Hang Su, Jun Zhu
To cope with real-world dynamics, an intelligent system needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime.
no code implementations • 10 Jan 2023 • Kexuan Li, Jun Zhu, Anthony R. Ives, Volker C. Radeloff, Fangfang Wang
To be specific, we use a sparsely connected deep neural network with rectified linear unit (ReLU) activation function to estimate the unknown regression function that describes the relationship between response and covariates in the presence of spatial dependence.
1 code implementation • CVPR 2023 • Yinpeng Dong, Caixin Kang, Jinlai Zhang, Zijian Zhu, Yikai Wang, Xiao Yang, Hang Su, Xingxing Wei, Jun Zhu
3D object detection is an important task in autonomous driving to perceive the surroundings.
no code implementations • 1 Dec 2022 • Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu
Extensive empirical evidence demonstrates that conditional generative models are easier to train and perform better than unconditional ones by exploiting the labels of data.
1 code implementation • 28 Nov 2022 • Shilong Liu, Yaoyuan Liang, Feng Li, Shijia Huang, Hao Zhang, Hang Su, Jun Zhu, Lei Zhang
As phrase extraction can be regarded as a $1$D text segmentation problem, we formulate PEG as a dual detection problem and propose a novel DQ-DETR model, which introduces dual queries to probe different features from image and text for object prediction and phrase mask prediction.
Ranked #7 on Referring Expression Comprehension on RefCOCO
1 code implementation • 15 Nov 2022 • Zhongkai Hao, Songming Liu, Yichi Zhang, Chengyang Ying, Yao Feng, Hang Su, Jun Zhu
Recent work shows that it provides potential benefits for machine learning models by incorporating the physical prior and collected data, which makes the intersection of machine learning and physics become a prevailing paradigm.
1 code implementation • 2 Nov 2022 • Jinali Zhang, Yinpeng Dong, Jun Zhu, Jihong Zhu, Minchi Kuang, Xiaming Yuan
Extensive experiments show that the SS attack proposed in this paper can be seamlessly combined with the existing state-of-the-art (SOTA) 3D point cloud attack methods to form more powerful attack methods, and the SS attack improves the transferability over 3. 6 times compare to the baseline.
no code implementations • 2 Nov 2022 • Yao Feng, Yuhong Jiang, Hang Su, Dong Yan, Jun Zhu
Model-based reinforcement learning usually suffers from a high sample complexity in training the world model, especially for the environments with complex dynamics.
Model-based Reinforcement Learning reinforcement-learning +1
1 code implementation • 2 Nov 2022 • Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu
The commonly-used fast sampler for guided sampling is DDIM, a first-order diffusion ODE solver that generally needs 100 to 250 steps for high-quality samples.
no code implementations • 29 Oct 2022 • Ziyu Wang, Yucen Luo, Yueru Li, Jun Zhu, Bernhard Schölkopf
For nonparametric conditional moment models, efficient estimation often relies on preimposed conditions on various measures of ill-posedness of the hypothesis space, which are hard to validate when flexible models are used.
no code implementations • 27 Oct 2022 • Yibo Miao, Yinpeng Dong, Jun Zhu, Xiao-Shan Gao
For naturalness, we constrain the adversarial example to be $\epsilon$-isometric to the original one by adopting the Gaussian curvature as a surrogate metric guaranteed by a theoretical analysis.
1 code implementation • 23 Oct 2022 • Zhijie Deng, Jiaxin Shi, Hao Zhang, Peng Cui, Cewu Lu, Jun Zhu
Unlike prior spectral methods such as Laplacian Eigenmap that operate in a nonparametric manner, Neural Eigenmap leverages NeuralEF to parametrically model eigenfunctions using a neural network.
1 code implementation • 23 Oct 2022 • Zhijie Deng, Feng Zhou, Jun Zhu
Laplace approximation (LA) and its linearized variant (LLA) enable effortless adaptation of pretrained deep neural networks to Bayesian neural networks.
1 code implementation • 8 Oct 2022 • Yinpeng Dong, Shouwei Ruan, Hang Su, Caixin Kang, Xingxing Wei, Jun Zhu
Recent studies have demonstrated that visual recognition models lack robustness to distribution shift.
1 code implementation • 6 Oct 2022 • Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Ze Cheng
We present a unified hard-constraint framework for solving geometrically complex PDEs with neural networks, where the most commonly used Dirichlet, Neumann, and Robin boundary conditions (BCs) are considered.
1 code implementation • 30 Sep 2022 • Jianyun Xu, Zhenwei Miao, Da Zhang, Hongyu Pan, Kaixuan Liu, Peihan Hao, Jun Zhu, Zhengyang Sun, Hongmin Li, Xin Zhan
By employing INT on CenterPoint, we can get around 7% (Waymo) and 15% (nuScenes) performance boost with only 2~4ms latency overhead, and currently SOTA on the Waymo 3D Detection leaderboard.
2 code implementations • 30 Sep 2022 • Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li, Jun Zhu
Inverse molecular design is critical in material science and drug discovery, where the generated molecules should satisfy certain desirable properties.
1 code implementation • 29 Sep 2022 • Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, Jun Zhu
To address this problem, we adopt a generative approach by decoupling the learned policy into two parts: an expressive generative behavior model and an action evaluation model.
3 code implementations • CVPR 2023 • Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, Jun Zhu
We evaluate U-ViT in unconditional and class-conditional image generation, as well as text-to-image generation tasks, where U-ViT is comparable if not superior to a CNN-based U-Net of a similar size.
Ranked #4 on Text-to-Image Generation on MS COCO
1 code implementation • 15 Sep 2022 • Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Dong Yan, Jun Zhu
In this paper, we reveal that the instability is also related to a new notion of Reuse Bias of IS -- the bias in off-policy evaluation caused by the reuse of the replay buffer for evaluation and optimization.
no code implementations • 15 Sep 2022 • Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Jian Song, Ze Cheng
In this paper, we present a novel bi-level optimization framework to resolve the challenge by decoupling the optimization of the targets and constraints.
no code implementations • 11 Aug 2022 • Qihan Guo, Siwei Wang, Jun Zhu
We study an extension of standard bandit problem in which there are R layers of experts.
no code implementations • 3 Aug 2022 • Wenkai Li, Cheng Feng, Ting Chen, Jun Zhu
In this work, to tackle this important challenge, we firstly investigate the robustness of commonly used deep TSAD methods with contaminated training data which provides a guideline for applying these methods when the provided training data are not guaranteed to be anomaly-free.
1 code implementation • 14 Jul 2022 • Min Zhao, Fan Bao, Chongxuan Li, Jun Zhu
Further, we provide an alternative explanation of the EGSDE as a product of experts, where each of the three experts (corresponding to the SDE and two feature extractors) solely contributes to faithfulness or realism.
Ranked #1 on Image-to-Image Translation on AFHQ (Wild to Dog)
2 code implementations • 13 Jul 2022 • Liyuan Wang, Xingxing Zhang, Qian Li, Jun Zhu, Yi Zhong
Continual learning requires incremental compatibility with a sequence of tasks.
1 code implementation • 12 Jul 2022 • Wentse Chen, Shiyu Huang, Yuan Chiang, Tim Pearce, Wei-Wei Tu, Ting Chen, Jun Zhu
We propose Diversity-Guided Policy Optimization (DGPO), an on-policy algorithm that discovers multiple strategies for solving a given task.
no code implementations • 18 Jun 2022 • Siwei Wang, Jun Zhu
To make the algorithm efficient, they usually use the sum of upper confidence bounds within arm set $S$ to represent the upper confidence bound of $S$, which can be much larger than the tight upper confidence bound of $S$ and leads to a much higher complexity than necessary, since the empirical means of different arms in $S$ are independent.
1 code implementation • 17 Jun 2022 • Siyu Wang, Jianfei Chen, Chongxuan Li, Jun Zhu, Bo Zhang
In this work, we propose Integer-only Discrete Flows (IODF), an efficient neural compressor with integer-only arithmetic.
1 code implementation • 16 Jun 2022 • Cheng Lu, Kaiwen Zheng, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu
To fill up this gap, we show that the negative likelihood of the ODE can be bounded by controlling the first, second, and third-order score matching errors; and we further present a novel high-order denoising score matching method to enable maximum likelihood training of score-based diffusion ODEs.
1 code implementation • 15 Jun 2022 • Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu, Bo Zhang
Thus, the generation performance on a subset of timesteps is crucial, which is greatly influenced by the covariance design in DPMs.
no code implementations • 9 Jun 2022 • Weikai Yang, Xi Ye, Xingxing Zhang, Lanxi Xiao, Jiazhi Xia, Zhongyuan Wang, Jun Zhu, Hanspeter Pfister, Shixia Liu
The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly affect the model performance.
1 code implementation • 9 Jun 2022 • Chengyang Ying, Xinning Zhou, Hang Su, Dong Yan, Ning Chen, Jun Zhu
Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty of both transition and observation.
no code implementations • 9 Jun 2022 • Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jun Zhu, Jian Song
Under the GSmooth framework, we present a scalable algorithm that uses a surrogate image-to-image network to approximate the complex transformation.
2 code implementations • 2 Jun 2022 • Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu
In this work, we propose an exact formulation of the solution of diffusion ODEs.
no code implementations • 28 May 2022 • Shih-Han Chan, Yinpeng Dong, Jun Zhu, Xiaolu Zhang, Jun Zhou
We propose four kinds of backdoor attacks for object detection task: 1) Object Generation Attack: a trigger can falsely generate an object of the target class; 2) Regional Misclassification Attack: a trigger can change the prediction of a surrounding object to the target class; 3) Global Misclassification Attack: a single trigger can change the predictions of all objects in an image to the target class; and 4) Object Disappearance Attack: a trigger can make the detector fail to detect the object of the target class.
1 code implementation • 26 May 2022 • Tim Pearce, Jong-Hyeon Jeong, Yichen Jia, Jun Zhu
To offer theoretical insight into our algorithm, we show firstly that it can be interpreted as a form of expectation-maximisation, and secondly that it exhibits a desirable `self-correcting' property.
1 code implementation • 22 May 2022 • Ziyu Wang, Yuhao Zhou, Jun Zhu
We investigate nonlinear instrumental variable (IV) regression given high-dimensional instruments.
1 code implementation • Findings (NAACL) 2022 • Jun Zhu, Céline Hudelot
Works on learning job title representation are mainly based on \textit{Job-Transition Graph}, built from the working history of talents.
no code implementations • 30 Apr 2022 • Zhijie Deng, Feng Zhou, Jianfei Chen, Guoqiang Wu, Jun Zhu
In this way, we relate DE to Bayesian inference to enjoy reliable Bayesian uncertainty.
2 code implementations • 30 Apr 2022 • Zhijie Deng, Jiaxin Shi, Jun Zhu
Learning the principal eigenfunctions of an integral operator defined by a kernel and a data distribution is at the core of many machine learning problems.
no code implementations • 26 Mar 2022 • Sha Yuan, Hanyu Zhao, Shuai Zhao, Jiahong Leng, Yangxiao Liang, Xiaozhi Wang, Jifan Yu, Xin Lv, Zhou Shao, Jiaao He, Yankai Lin, Xu Han, Zhenghao Liu, Ning Ding, Yongming Rao, Yizhao Gao, Liang Zhang, Ming Ding, Cong Fang, Yisen Wang, Mingsheng Long, Jing Zhang, Yinpeng Dong, Tianyu Pang, Peng Cui, Lingxiao Huang, Zheng Liang, HuaWei Shen, HUI ZHANG, Quanshi Zhang, Qingxiu Dong, Zhixing Tan, Mingxuan Wang, Shuo Wang, Long Zhou, Haoran Li, Junwei Bao, Yingwei Pan, Weinan Zhang, Zhou Yu, Rui Yan, Chence Shi, Minghao Xu, Zuobai Zhang, Guoqiang Wang, Xiang Pan, Mengjie Li, Xiaoyu Chu, Zijun Yao, Fangwei Zhu, Shulin Cao, Weicheng Xue, Zixuan Ma, Zhengyan Zhang, Shengding Hu, Yujia Qin, Chaojun Xiao, Zheni Zeng, Ganqu Cui, Weize Chen, Weilin Zhao, Yuan YAO, Peng Li, Wenzhao Zheng, Wenliang Zhao, Ziyi Wang, Borui Zhang, Nanyi Fei, Anwen Hu, Zenan Ling, Haoyang Li, Boxi Cao, Xianpei Han, Weidong Zhan, Baobao Chang, Hao Sun, Jiawen Deng, Chujie Zheng, Juanzi Li, Lei Hou, Xigang Cao, Jidong Zhai, Zhiyuan Liu, Maosong Sun, Jiwen Lu, Zhiwu Lu, Qin Jin, Ruihua Song, Ji-Rong Wen, Zhouchen Lin, LiWei Wang, Hang Su, Jun Zhu, Zhifang Sui, Jiajun Zhang, Yang Liu, Xiaodong He, Minlie Huang, Jian Tang, Jie Tang
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
no code implementations • 13 Mar 2022 • Jialian Li, Tongzheng Ren, Dong Yan, Hang Su, Jun Zhu
Our goal is to identify a near-optimal robust policy for the perturbed testing environment, which introduces additional technical difficulties as we need to simultaneously estimate the training environment uncertainty from samples and find the worst-case perturbation for testing.
1 code implementation • 13 Mar 2022 • Yinpeng Dong, Shuyu Cheng, Tianyu Pang, Hang Su, Jun Zhu
However, the existing methods inevitably suffer from low attack success rates or poor query efficiency since it is difficult to estimate the gradient in a high-dimensional input space with limited information.
no code implementations • 9 Mar 2022 • Xiao Yang, Yinpeng Dong, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu
It is therefore imperative to develop a framework that can enable a comprehensive evaluation of the vulnerability of face recognition in the physical world.
15 code implementations • 7 Mar 2022 • Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M. Ni, Heung-Yeung Shum
Compared to other models on the leaderboard, DINO significantly reduces its model size and pre-training data size while achieving better results.
Ranked #1 on Real-Time Object Detection on COCO 2017 val
1 code implementation • 21 Feb 2022 • Tianyu Pang, Min Lin, Xiao Yang, Jun Zhu, Shuicheng Yan
The trade-off between robustness and accuracy has been widely studied in the adversarial literature.
1 code implementation • ICLR 2022 • Liyuan Wang, Xingxing Zhang, Kuo Yang, Longhui Yu, Chongxuan Li, Lanqing Hong, Shifeng Zhang, Zhenguo Li, Yi Zhong, Jun Zhu
In this work, we propose memory replay with data compression (MRDC) to reduce the storage cost of old training samples and thus increase their amount that can be stored in the memory buffer.
7 code implementations • ICLR 2022 • Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, Lei Zhang
We present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR.
Ranked #11 on 2D Object Detection on SARDet-100K
2 code implementations • ICLR 2022 • Fan Bao, Chongxuan Li, Jun Zhu, Bo Zhang
In this work, we present a surprising result that both the optimal reverse variance and the corresponding optimal KL divergence of a DPM have analytic forms w. r. t.
no code implementations • CVPR 2022 • Hongyang Gu, Jianmin Li, Guangyuan Fu, Chifong Wong, Xinghao Chen, Jun Zhu
In this paper, we propose a novel method, AutoLoss-GMS, to search the better loss function in the space of generalized margin-based softmax loss function for person re-identification automatically.
no code implementations • CVPR 2022 • Yunlong Wang, Hongyu Pan, Jun Zhu, Yu-Huan Wu, Xin Zhan, Kun Jiang, Diange Yang
In this paper, we propose a novel Spatial-Temporal Integrated network with Bidirectional Enhancement, BE-STI, to improve the temporal motion prediction performance by spatial semantic features, which points out an efficient way to combine semantic segmentation and motion prediction.
no code implementations • 9 Nov 2021 • Jun Zhu, Gautier Viaud, Céline Hudelot
The second module learns job seeker representations.
1 code implementation • NeurIPS 2021 • Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, Yi Zhong
Without accessing to the old training samples, knowledge transfer from the old tasks to each new task is difficult to determine, which might be either positive or negative.
1 code implementation • 17 Oct 2021 • Yuefeng Chen, Xiaofeng Mao, Yuan He, Hui Xue, Chao Li, Yinpeng Dong, Qi-An Fu, Xiao Yang, Tianyu Pang, Hang Su, Jun Zhu, Fangcheng Liu, Chao Zhang, Hongyang Zhang, Yichi Zhang, Shilong Liu, Chang Liu, Wenzhao Xiang, Yajie Wang, Huipeng Zhou, Haoran Lyu, Yidan Xu, Zixuan Xu, Taoyu Zhu, Wenjun Li, Xianfeng Gao, Guoqiu Wang, Huanqian Yan, Ying Guo, Chaoning Zhang, Zheng Fang, Yang Wang, Bingyang Fu, Yunfei Zheng, Yekui Wang, Haorong Luo, Zhen Yang
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input.
1 code implementation • 15 Oct 2021 • Yinpeng Dong, Qi-An Fu, Xiao Yang, Wenzhao Xiang, Tianyu Pang, Hang Su, Jun Zhu, Jiayu Tang, Yuefeng Chen, Xiaofeng Mao, Yuan He, Hui Xue, Chao Li, Ye Liu, Qilong Zhang, Lianli Gao, Yunrui Yu, Xitong Gao, Zhe Zhao, Daquan Lin, Jiadong Lin, Chuanbiao Song, ZiHao Wang, Zhennan Wu, Yang Guo, Jiequan Cui, Xiaogang Xu, Pengguang Chen
Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.
no code implementations • 13 Oct 2021 • Xiao Yang, Yinpeng Dong, Wenzhao Xiang, Tianyu Pang, Hang Su, Jun Zhu
The vulnerability of deep neural networks to adversarial examples has motivated an increasing number of defense strategies for promoting model robustness.
1 code implementation • 9 Oct 2021 • Shiyu Huang, Wenze Chen, Longfei Zhang, Shizhen Xu, Ziyang Li, Fengming Zhu, Deheng Ye, Ting Chen, Jun Zhu
To the best of our knowledge, Tikick is the first learning-based AI system that can take over the multi-agent Google Research Football full game, while previous work could either control a single agent or experiment on toy academic scenarios.
1 code implementation • 8 Oct 2021 • Shiyu Huang, Bin Wang, Dong Li, Jianye Hao, Ting Chen, Jun Zhu
In this work, we propose a new algorithm for circuit routing, named Ranking Cost, which innovatively combines search-based methods (i. e., A* algorithm) and learning-based methods (i. e., Evolution Strategies) to form an efficient and trainable router.
no code implementations • ICML Workshop AML 2021 • Yichi Zhang, Zijian Zhu, Xiao Yang, Jun Zhu
To address this issue, we propose a novel method of Adversarial Semantic Contour (ASC) guided by object contour as prior.
no code implementations • 29 Sep 2021 • Zhijie Deng, Feng Zhou, Jianfei Chen, Guoqiang Wu, Jun Zhu
Deep Ensemble (DE) is a flexible, feasible, and effective alternative to Bayesian neural networks (BNNs) for uncertainty estimation in deep learning.
no code implementations • 29 Sep 2021 • Yichi Zhou, Shihong Song, Huishuai Zhang, Jun Zhu, Wei Chen, Tie-Yan Liu
In contextual bandit, one major challenge is to develop theoretically solid and empirically efficient algorithms for general function classes.
1 code implementation • ICML Workshop AML 2021 • Zhengyi Wang, Zhongkai Hao, Ziqiao Wang, Hang Su, Jun Zhu
In this work, we propose Cluster Attack -- a Graph Injection Attack (GIA) on node classification, which injects fake nodes into the original graph to degenerate the performance of graph neural networks (GNNs) on certain victim nodes while affecting the other nodes as little as possible.
1 code implementation • 29 Jul 2021 • Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Yi Su, Hang Su, Jun Zhu
In this paper, we present Tianshou, a highly modularized Python library for deep reinforcement learning (DRL) that uses PyTorch as its backend.
2 code implementations • 22 Jul 2021 • Shilong Liu, Lei Zhang, Xiao Yang, Hang Su, Jun Zhu
The use of Transformer is rooted in the need of extracting local discriminative features adaptively for different labels, which is a strongly desired property due to the existence of multiple objects in one image.
Ranked #1 on Multi-Label Classification on PASCAL VOC 2012
1 code implementation • NeurIPS 2021 • Shuyu Cheng, Guoqiang Wu, Jun Zhu
Finally, our theoretical results are confirmed by experiments on several numerical benchmarks as well as adversarial attacks.
1 code implementation • ICML Workshop AML 2021 • Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu
Transfer-based adversarial attacks can evaluate model robustness in the black-box setting.
no code implementations • 30 Jun 2021 • You Qiaoben, Chengyang Ying, Xinning Zhou, Hang Su, Jun Zhu, Bo Zhang
In this paper, we provide a framework to better understand the existing methods by reformulating the problem of adversarial attacks on reinforcement learning in the function space.
no code implementations • 29 Jun 2021 • Yichi Zhou, Shihong Song, Huishuai Zhang, Jun Zhu, Wei Chen, Tie-Yan Liu
However, it is in general unknown how to deriveefficient and effective EE trade-off methods for non-linearcomplex tasks, suchas contextual bandit with deep neural network as the reward function.
no code implementations • CVPR 2021 • Zihao Xiao, Xianfeng Gao, Chilin Fu, Yinpeng Dong, Wei Gao, Xiaolu Zhang, Jun Zhou, Jun Zhu
However, deep CNNs are vulnerable to adversarial patches, which are physically realizable and stealthy, raising new security concerns on the real-world applications of these models.
no code implementations • CVPR 2021 • Zhenwei Miao, Jikai Chen, Hongyu Pan, Ruiwen Zhang, Kaixuan Liu, Peihan Hao, Jun Zhu, Yang Wang, Xin Zhan
Quantization-based methods are widely used in LiDAR points 3D object detection for its efficiency in extracting context information.
1 code implementation • NeurIPS 2021 • Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu
Collecting training data from untrusted sources exposes machine learning services to poisoning adversaries, who maliciously manipulate training data to degrade the model accuracy.
no code implementations • ICML Workshop AML 2021 • You Qiaoben, Xinning Zhou, Chengyang Ying, Jun Zhu
Deep reinforcement learning (DRL) policies are vulnerable to the adversarial attack on their observations, which may mislead real-world RL agents to catastrophic failures.
no code implementations • ICML Workshop AML 2021 • Chengyang Ying, Xinning Zhou, Dong Yan, Jun Zhu
Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty caused by stochastic policies and environment variability.
1 code implementation • NeurIPS 2021 • Ziyu Wang, Yuhao Zhou, Tongzheng Ren, Jun Zhu
Recent years have witnessed an upsurge of interest in employing flexible machine learning models for instrumental variable (IV) regression, but the development of uncertainty quantification methodology is still lacking.
no code implementations • 14 Jun 2021 • Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Yuan YAO, Ao Zhang, Liang Zhang, Wentao Han, Minlie Huang, Qin Jin, Yanyan Lan, Yang Liu, Zhiyuan Liu, Zhiwu Lu, Xipeng Qiu, Ruihua Song, Jie Tang, Ji-Rong Wen, Jinhui Yuan, Wayne Xin Zhao, Jun Zhu
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI).
no code implementations • 9 Jun 2021 • Tim Pearce, Alexandra Brintrup, Jun Zhu
It is often remarked that neural networks fail to increase their uncertainty when predicting on data far from the training distribution.
no code implementations • 9 Jun 2021 • Feng Zhou, Quyu Kong, Yixuan Zhang, Cheng Feng, Jun Zhu
Hawkes processes are a class of point processes that have the ability to model the self- and mutual-exciting phenomena.
1 code implementation • NeurIPS 2021 • Fan Bao, Guoqiang Wu, Chongxuan Li, Jun Zhu, Bo Zhang
Our results can explain some mysterious behaviours of the bilevel programming in practice, for instance, overfitting to the validation set.
1 code implementation • ICLR 2022 • Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, Jun Zhu
In this paper, we explore the memorization effect in adversarial training (AT) for promoting a deeper understanding of model capacity, convergence, generalization, and especially robust overfitting of the adversarially trained models.
no code implementations • 2 Jun 2021 • Yingtao Luo, Qiang Liu, Yuntian Chen, WenBo Hu, Tian Tian, Jun Zhu
Especially, the discovery of PDEs with highly nonlinear coefficients from low-quality data remains largely under-addressed.
1 code implementation • CVPR 2022 • Tianyu Pang, Huishuai Zhang, Di He, Yinpeng Dong, Hang Su, Wei Chen, Jun Zhu, Tie-Yan Liu
Along with this routine, we find that confidence and a rectified confidence (R-Con) can form two coupled rejection metrics, which could provably distinguish wrongly classified inputs from correctly classified ones.
no code implementations • CVPR 2021 • Shilong Liu, Lei Zhang, Xiao Yang, Hang Su, Jun Zhu
We study the problem of unsupervised discovery and segmentation of object parts, which, as an intermediate local representation, are capable of finding intrinsic object structure and providing more explainable recognition results.
no code implementations • NeurIPS 2021 • Ziyu Wang, Yuhao Zhou, Tongzheng Ren, Jun Zhu
Recent years have witnessed an upsurge of interest in employing flexible machine learning models for instrumental variable (IV) regression, but the development of uncertainty quantification methodology is still lacking.
no code implementations • NeurIPS 2021 • Guoqiang Wu, Chongxuan Li, Kun Xu, Jun Zhu
Our results show that learning algorithms with the consistent univariate loss have an error bound of $O(c)$ ($c$ is the number of labels), while algorithms with the inconsistent pairwise loss depend on $O(\sqrt{c})$ as shown in prior work.
no code implementations • 9 May 2021 • Qi-An Fu, Yinpeng Dong, Hang Su, Jun Zhu
Deep learning models are vulnerable to adversarial examples, which can fool a target classifier by imposing imperceptible perturbations onto natural examples.
1 code implementation • ICLR 2021 • Tsung Wei Tsai, Chongxuan Li, Jun Zhu
We present Mixture of Contrastive Experts (MiCE), a unified probabilistic clustering framework that simultaneously exploits the discriminative representations learned by contrastive learning and the semantic structures captured by a latent mixture model.
Ranked #9 on Image Clustering on Imagenet-dog-15
no code implementations • 19 Apr 2021 • Liyuan Wang, Qian Li, Yi Zhong, Jun Zhu
Our solution is based on the observation that continual learning of a task sequence inevitably interferes few-shot generalization, which makes it highly nontrivial to extend few-shot learning strategies to continual learning scenarios.
2 code implementations • 9 Apr 2021 • Tim Pearce, Jun Zhu
This paper describes an AI agent that plays the popular first-person-shooter (FPS) video game `Counter-Strike; Global Offensive' (CSGO) from pixel input.
no code implementations • 28 Mar 2021 • Peng Cui, Zhijie Deng, WenBo Hu, Jun Zhu
It is critical yet challenging for deep learning models to properly characterize uncertainty that is pervasive in real-world environments.
1 code implementation • CVPR 2021 • Zhijie Deng, Xiao Yang, Shizhen Xu, Hang Su, Jun Zhu
Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against adversarial examples.
no code implementations • ICCV 2021 • Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu
Although deep neural networks (DNNs) have made rapid progress in recent years, they are vulnerable in adversarial environments.
1 code implementation • ICLR 2021 • Cheng Lu, Jianfei Chen, Chongxuan Li, Qiuhao Wang, Jun Zhu
Through theoretical analysis, we show that the function space of ImpFlow is strictly richer than that of ResFlows.
no code implementations • 24 Feb 2021 • Qiang Liu, Zhaocheng Liu, Haoli Zhang, Yuntian Chen, Jun Zhu
Accordingly, we can design an automatic feature crossing method to find feature interactions in DNN, and use them as cross features in LR.
1 code implementation • 23 Feb 2021 • Xiao Li, Jianmin Li, Ting Dai, Jie Shi, Jun Zhu, Xiaolin Hu
A detection model based on the classification model EfficientNet-B7 achieved a top-1 accuracy of 53. 95%, surpassing previous state-of-the-art classification models trained on ImageNet, suggesting that accurate localization information can significantly boost the performance of classification models on ImageNet-A.
no code implementations • 25 Jan 2021 • Jun Zhu, Ye Chen, Frank Brinker, Winfried Decking, Sergey Tomin, Holger Schlarb
We also show the scalability and interpretability of the model by sharing the same decoder with more than one encoder used for different setups of the photoinjector, and propose a pragmatic way to model a facility with various diagnostics and working points.
no code implementations • 11 Jan 2021 • Yuanyuan Ding, Junchi Yan, Guoqiang Hu, Jun Zhu
This paper discloses a novel visual inspection system for liquid crystal display (LCD), which is currently a dominant type in the FPD industry.
no code implementations • 5 Jan 2021 • Qijun Luo, Zhili Liu, Lanqing Hong, Chongxuan Li, Kuo Yang, Liyuan Wang, Fengwei Zhou, Guilin Li, Zhenguo Li, Jun Zhu
Semi-supervised domain adaptation (SSDA), which aims to learn models in a partially labeled target domain with the assistance of the fully labeled source domain, attracts increasing attention in recent years.
no code implementations • CVPR 2021 • Liyuan Wang, Kuo Yang, Chongxuan Li, Lanqing Hong, Zhenguo Li, Jun Zhu
Continual learning usually assumes the incoming data are fully labeled, which might not be applicable in real applications.
no code implementations • 1 Jan 2021 • Guan Wang, Dong Yan, Hang Su, Jun Zhu
In this work, we point out that the optimal value of n actually differs on each data point, while the fixed value n is a rough average of them.
no code implementations • 1 Jan 2021 • Shiyu Huang, Bin Wang, Dong Li, Jianye Hao, Jun Zhu, Ting Chen
In our method, we introduce a new set of variables called cost maps, which can help the A* router to find out proper paths to achieve the global object.
no code implementations • 16 Dec 2020 • Qingyi Pan, WenBo Hu, Jun Zhu
Though deep learning methods have recently been developed to give superior forecasting results, it is crucial to improve the interpretability of time series models.
1 code implementation • 14 Dec 2020 • Qipeng Guo, Zhijing Jin, Ziyu Wang, Xipeng Qiu, Weinan Zhang, Jun Zhu, Zheng Zhang, David Wipf
Cycle-consistent training is widely used for jointly learning a forward and inverse mapping between two domains of interest without the cumbersome requirement of collecting matched pairs within each domain.
1 code implementation • NeurIPS 2020 • Zhijie Deng, Yinpeng Dong, Shifeng Zhang, Jun Zhu
In this work, we decouple the training of a network with stochastic architectures (NSA) from NAS and provide a first systematical investigation on it as a stand-alone problem.
1 code implementation • NeurIPS 2020 • Guoqiang Wu, Jun Zhu
On the other hand, when directly optimizing SA with its surrogate loss, it has learning guarantees that depend on $O(\sqrt{c})$ for both HL and SA measures.
1 code implementation • NeurIPS 2020 • Ziyu Wang, Bin Dai, David Wipf, Jun Zhu
The recent, counter-intuitive discovery that deep generative models (DGMs) can frequently assign a higher likelihood to outliers has implications for both outlier detection applications as well as our overall understanding of generative modeling.
1 code implementation • NeurIPS Workshop ICBINB 2020 • Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong, Jun Zhu, Bo Zhang
The learning and evaluation of energy-based latent variable models (EBLVMs) without any structural assumptions are highly challenging, because the true posteriors and the partition functions in such models are generally intractable.
1 code implementation • NeurIPS 2020 • Fan Bao, Chongxuan Li, Kun Xu, Hang Su, Jun Zhu, Bo Zhang
This paper presents a bi-level score matching (BiSM) method to learn EBLVMs with general structures by reformulating SM as a bi-level optimization problem.
1 code implementation • 5 Oct 2020 • Zhijie Deng, Jun Zhu
Despite their theoretical appealingness, Bayesian neural networks (BNNs) are left behind in real-world adoption, mainly due to persistent concerns on their scalability, accessibility, and reliability.
2 code implementations • ICLR 2021 • Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu
Adversarial training (AT) is one of the most effective strategies for promoting model robustness.
no code implementations • 28 Sep 2020 • Zhijie Deng, Xiao Yang, Hao Zhang, Yinpeng Dong, Jun Zhu
Despite their theoretical appealingness, Bayesian neural networks (BNNs) are falling far behind in terms of adoption in real-world applications compared with normal NNs, mainly due to their limited scalability in training, and low fidelity in their uncertainty estimates.
no code implementations • 15 Sep 2020 • Chen Ma, Shuyu Cheng, Li Chen, Jun Zhu, Junhai Yong
In each iteration, SWITCH first tries to update the current sample along the direction of $\hat{\mathbf{g}}$, but considers switching to its opposite direction $-\hat{\mathbf{g}}$ if our algorithm detects that it does not increase the value of the attack objective function.
1 code implementation • ECCV 2020 • Haoyu Liang, Zhihao Ouyang, Yuyuan Zeng, Hang Su, Zihao He, Shu-Tao Xia, Jun Zhu, Bo Zhang
Most existing works attempt post-hoc interpretation on a pre-trained model, while neglecting to reduce the entanglement underlying the model.
2 code implementations • 8 Jul 2020 • Xiao Yang, Dingcheng Yang, Yinpeng Dong, Hang Su, Wenjian Yu, Jun Zhu
Based on large-scale evaluations, the commercial FR API services fail to exhibit acceptable performance on robustness evaluation, and we also draw several important conclusions for understanding the adversarial robustness of FR models and providing insights for the design of robust FR models.
1 code implementation • NeurIPS 2020 • Tianyu Pang, Kun Xu, Chongxuan Li, Yang song, Stefano Ermon, Jun Zhu
Several machine learning applications involve the optimization of higher-order derivatives (e. g., gradients of gradients) during training, which can be expensive in respect to memory and computation even with automatic differentiation.
no code implementations • ICLR 2021 • Feng Zhou, Yixuan Zhang, Jun Zhu
Hawkes process provides an effective statistical framework for analyzing the time-dependent interaction of neuronal spiking activities.
no code implementations • NeurIPS 2020 • Peng Cui, Wen-Bo Hu, Jun Zhu
Accurate quantification of uncertainty is crucial for real-world applications of machine learning.
no code implementations • 14 Jun 2020 • Zhiheng Zhang, Wen-Bo Hu, Tian Tian, Jun Zhu
In this paper, we present the dynamic window-level Granger causality method (DWGC) for multi-channel time series data.
no code implementations • 5 Jun 2020 • Yujie Wu, Rong Zhao, Jun Zhu, Feng Chen, Mingkun Xu, Guoqi Li, Sen Song, Lei Deng, Guanrui Wang, Hao Zheng, Jing Pei, Youhui Zhang, Mingguo Zhao, Luping Shi
We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors.
1 code implementation • ICML 2020 • Yuhao Zhou, Jiaxin Shi, Jun Zhu
Estimating the score, i. e., the gradient of log density function, from a set of samples generated by an unknown distribution is a fundamental task in inference and learning of probabilistic models that involve flexible yet intractable densities.
no code implementations • ICLR 2020 • Yichi Zhou, Jialian Li, Jun Zhu
Posterior sampling for reinforcement learning (PSRL) is a useful framework for making decisions in an unknown environment.
no code implementations • ICLR 2020 • Yichi Zhou, Tongzheng Ren, Jialian Li, Dong Yan, Jun Zhu
In this paper, we present Lazy-CFR, a CFR algorithm that adopts a lazy update strategy to avoid traversing the whole game tree in each round.
no code implementations • ICLR 2020 • Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen
Standard variational lower bounds used to train latent variable models produce biased estimates of most quantities of interest.
1 code implementation • ICCV 2021 • Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu, Yuefeng Chen, Hui Xue
As billions of personal data being shared through social media and network, the data privacy and security have drawn an increasing attention.
1 code implementation • 6 Mar 2020 • Liyuan Wang, Bo Lei, Qian Li, Hang Su, Jun Zhu, Yi Zhong
Continual acquisition of novel experience without interfering previously learned knowledge, i. e. continual learning, is critical for artificial neural networks, but limited by catastrophic forgetting.
1 code implementation • ICML 2020 • Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, Tian Tian
Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations.
Ranked #30 on Image Generation on CIFAR-10 (bits/dimension metric)
1 code implementation • NeurIPS 2020 • Tianyu Pang, Xiao Yang, Yinpeng Dong, Kun Xu, Jun Zhu, Hang Su
Adversarial training (AT) is one of the most effective defenses against adversarial attacks for deep learning models.
1 code implementation • pproximateinference AABI Symposium 2019 • Ziyu Wang, Shuyu Cheng, Yueru Li, Jun Zhu, Bo Zhang
Score matching provides an effective approach to learning flexible unnormalized models, but its scalability is limited by the need to evaluate a second-order derivative.
1 code implementation • NeurIPS 2020 • Yinpeng Dong, Zhijie Deng, Tianyu Pang, Hang Su, Jun Zhu
Adversarial training (AT) is among the most effective techniques to improve model robustness by augmenting training data with adversarial examples.
no code implementations • 26 Jan 2020 • Kelei Cao, Mengchen Liu, Hang Su, Jing Wu, Jun Zhu, Shixia Liu
The key is to compare and analyze the datapaths of both the adversarial and normal examples.
no code implementations • ICLR 2020 • Zhaocheng Liu, Qiang Liu, Haoli Zhang, Jun Zhu
In recent years, substantial progress has been made on graph convolutional networks (GCN).
no code implementations • ICLR 2020 • Shiyu Huang, Hang Su, Jun Zhu, Ting Chen
Partially Observable Markov Decision Processes (POMDPs) are popular and flexible models for real-world decision-making applications that demand the information from past observations to make optimal decisions.
no code implementations • 26 Dec 2019 • Yinpeng Dong, Qi-An Fu, Xiao Yang, Tianyu Pang, Hang Su, Zihao Xiao, Jun Zhu
Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning.
1 code implementation • 20 Dec 2019 • Chongxuan Li, Kun Xu, Jiashuo Liu, Jun Zhu, Bo Zhang
It is formulated as a three-player minimax game consisting of a generator, a classifier and a discriminator, and therefore is referred to as Triple Generative Adversarial Network (Triple-GAN).