14 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
5 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
2 code implementations • ICML 2018 • Jianfei Chen, Jun Zhu, Le Song
Previous attempts on reducing the receptive field size by subsampling neighbors do not have a convergence guarantee, and their receptive field size per node is still in the order of hundreds.
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.
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.
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., $7. 5$).
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 • 18 Sep 2017 • Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong Gu, Yuhao Zhou
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning.
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 • 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.
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.
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
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.
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.
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
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 • 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.
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
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.
7 code implementations • CVPR 2018 • Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, Jianguo Li
To further improve the success rates for black-box attacks, we apply momentum iterative algorithms to an ensemble of models, and show that the adversarially trained models with a strong defense ability are also vulnerable to our black-box attacks.
1 code implementation • NeurIPS 2017 • Chongxuan Li, Kun Xu, Jun Zhu, Bo Zhang
Generative Adversarial Nets (GANs) have shown promise in image generation and semi-supervised learning (SSL).
2 code implementations • CVPR 2018 • Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, Jun Zhu
First, with HGD as a defense, the target model is more robust to either white-box or black-box adversarial attacks.
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.
1 code implementation • 9 Jun 2016 • Shaohua Li, Tat-Seng Chua, Jun Zhu, Chunyan Miao
Word embedding maps words into a low-dimensional continuous embedding space by exploiting the local word collocation patterns in a small context window.
1 code implementation • EMNLP 2015 • Shaohua Li, Jun Zhu, Chunyan Miao
Most existing word embedding methods can be categorized into Neural Embedding Models and Matrix Factorization (MF)-based methods.
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 • 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 • 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.
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.
1 code implementation • 31 Mar 2018 • Alexey Kurakin, Ian Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, Jian-Yu Wang, Zhishuai Zhang, Zhou Ren, Alan Yuille, Sangxia Huang, Yao Zhao, Yuzhe Zhao, Zhonglin Han, Junjiajia Long, Yerkebulan Berdibekov, Takuya Akiba, Seiya Tokui, Motoki Abe
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them.
2 code implementations • CVPR 2019 • Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu
In this paper, we propose a translation-invariant attack method to generate more transferable adversarial examples against the defense models.
1 code implementation • ICML 2018 • Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song
Deep learning on graph structures has shown exciting results in various applications.
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.
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 • 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.
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 • 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.
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.
2 code implementations • ICML 2018 • Tianyu Pang, Chao Du, Jun Zhu
In this paper, we show that a properly designed classifier can improve robustness to adversarial attacks and lead to better prediction results.
2 code implementations • ICLR 2020 • Tianyu Pang, Kun Xu, Yinpeng Dong, Chao Du, Ning Chen, Jun Zhu
Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e. g., CIFAR-10, which enables good standard accuracy may not suffice to train robust 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 • 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 • 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.
1 code implementation • 3 Aug 2017 • Yinpeng Dong, Renkun Ni, Jianguo Li, Yurong Chen, Jun Zhu, Hang Su
This procedure can greatly compensate the quantization error and thus yield better accuracy for low-bit DNNs.
1 code implementation • NeurIPS 2018 • Chongxuan Li, Max Welling, Jun Zhu, Bo Zhang
We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model structured data.
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.
6 code implementations • 25 Jan 2019 • Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu
Though deep neural networks have achieved significant progress on various tasks, often enhanced by model ensemble, existing high-performance models can be vulnerable to adversarial attacks.
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.
1 code implementation • ICLR 2020 • Tianyu Pang, Kun Xu, Jun Zhu
Our experiments on CIFAR-10 and CIFAR-100 demonstrate that MI can further improve the adversarial robustness for the models trained by mixup and its variants.
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 • 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 • 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 • 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
1 code implementation • NeurIPS 2018 • Tianyu Pang, Chao Du, Yinpeng Dong, Jun Zhu
Although the recent progress is substantial, deep learning methods can be vulnerable to the maliciously generated adversarial examples.
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 • CVPR 2018 • Yucen Luo, Jun Zhu, Mengxi Li, Yong Ren, Bo Zhang
In SNTG, a graph is constructed based on the predictions of the teacher model, i. e., the implicit self-ensemble of models.
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.
2 code implementations • 27 May 2019 • Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu
Inference in Gaussian process (GP) models is computationally challenging for large data, and often difficult to approximate with a small number of inducing points.
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 2019 • Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu
We consider the black-box adversarial setting, where the adversary has to generate adversarial perturbations without access to the target models to compute gradients.
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.
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.
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).
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.
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 • 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 • 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.
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.
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.
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.
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.
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.
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
3 code implementations • ICML 2018 • Jiaxin Shi, Shengyang Sun, Jun Zhu
Recently there have been increasing interests in learning and inference with implicit distributions (i. e., distributions without tractable densities).
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.
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.
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.
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.
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.
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 • NeurIPS 2017 • Zhijie Deng, Hao Zhang, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, Eric P. Xing
We study the problem of conditional generative modeling based on designated semantics or structures.
1 code implementation • ICCV 2019 • Zhijie Deng, Yucen Luo, Jun Zhu
Deep learning methods have shown promise in unsupervised domain adaptation, which aims to leverage a labeled source domain to learn a classifier for the unlabeled target domain with a different distribution.
Ranked #3 on Domain Adaptation on SVNH-to-MNIST
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 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 • 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 • 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 • 4 Jul 2018 • Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu, Lawrence Carin
Particle-based variational inference methods (ParVIs) have gained attention in the Bayesian inference literature, for their capacity to yield flexible and accurate approximations.
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.
2 code implementations • 14 Dec 2017 • Jian Wu, Changran Hu, Yulong Wang, Xiaolin Hu, Jun Zhu
In this paper, we present a hierarchical recurrent neural network for melody generation, which consists of three Long-Short-Term-Memory (LSTM) subnetworks working in a coarse-to-fine manner along time.
Sound Multimedia
2 code implementations • NeurIPS 2015 • Chongxuan Li, Jun Zhu, Tianlin Shi, Bo Zhang
Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability.
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.
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.
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 • 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.
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 • 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 • 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 • 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.
1 code implementation • 21 Dec 2016 • Chao Du, Chongxuan Li, Yin Zheng, Jun Zhu, Bo Zhang
Deep neural networks have shown promise in collaborative filtering (CF).
1 code implementation • ICLR 2019 • Ziyu Wang, Tongzheng Ren, Jun Zhu, Bo Zhang
While Bayesian neural networks (BNNs) have drawn increasing attention, their posterior inference remains challenging, due to the high-dimensional and over-parameterized nature.
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.
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.
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 • 7 Mar 2018 • Xingxing Wei, Jun Zhu, Hang Su
Although adversarial samples of deep neural networks (DNNs) have been intensively studied on static images, their extensions in videos are never explored.
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.
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 • 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 • NeurIPS 2018 • Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu, Bo Zhang
We propose a new approach that includes a deep generative model (DGM) to characterize low-level features of the data, and a statistical relational model for noisy pairwise annotations on its subset.
1 code implementation • 1 Feb 2019 • Chang Liu, Jingwei Zhuo, Jun Zhu
It is known that the Langevin dynamics used in MCMC is the gradient flow of the KL divergence on the Wasserstein space, which helps convergence analysis and inspires recent particle-based variational inference methods (ParVIs).
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.
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.
1 code implementation • 8 Jun 2017 • Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Luping Shi
By simultaneously considering the layer-by-layer spatial domain (SD) and the timing-dependent temporal domain (TD) in the training phase, as well as an approximated derivative for the spike activity, we propose a spatio-temporal backpropagation (STBP) training framework without using any complicated technology.
1 code implementation • 22 Nov 2016 • Chongxuan Li, Jun Zhu, Bo Zhang
Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability.
1 code implementation • 5 Dec 2019 • Justin Cosentino, Federico Zaiter, Dan Pei, Jun Zhu
Recent work on deep neural network pruning has shown there exist sparse subnetworks that achieve equal or improved accuracy, training time, and loss using fewer network parameters when compared to their dense counterparts.
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 • 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 • 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 • 29 Sep 2019 • 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.
Ranked #37 on Image Generation on CIFAR-10 (Inception score metric)
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 • CVPR 2018 • Juzheng Li, Hang Su, Jun Zhu, Siyu Wang, Bo Zhang
The machine thus performs as an instructor to extract the essay-level contradictions as the Guidance.
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 • 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 • 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.
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 • 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 • 30 Nov 2017 • Chang Liu, Jun Zhu
The benefits are two-folds: (i) for inference tasks in Euclidean spaces, RSVGD has the advantage over SVGD of utilizing information geometry, and (ii) for inference tasks on Riemann manifolds, RSVGD brings the unique advantages of SVGD to the Riemannian world.
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 • 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 • 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.
1 code implementation • 24 Feb 2016 • Chongxuan Li, Jun Zhu, Bo Zhang
Memory units have been widely used to enrich the capabilities of deep networks on capturing long-term dependencies in reasoning and prediction tasks, but little investigation exists on deep generative models (DGMs) which are good at inferring high-level invariant representations from unlabeled data.
1 code implementation • 3 Dec 2015 • Yang Song, Jun Zhu
Bayesian matrix completion has been studied based on a low-rank matrix factorization formulation with promising results.
1 code implementation • NeurIPS 2019 • Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang
Deep generative models (DGMs) have shown promise in image generation.
1 code implementation • 22 Nov 2019 • Zhijie Deng, Yucen Luo, Jun Zhu, Bo Zhang
Bayesian neural networks (BNNs) augment deep networks with uncertainty quantification by Bayesian treatment of the network weights.
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.
1 code implementation • 25 Sep 2019 • Zhijie Deng, Yucen Luo, Jun Zhu, Bo Zhang
Bayesian neural networks (BNNs) introduce uncertainty estimation to deep networks by performing Bayesian inference on network weights.
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.
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)
1 code implementation • 28 Jun 2017 • Haosheng Zou, Kun Xu, Jialian Li, Jun Zhu
We took part in the YouTube-8M Video Understanding Challenge hosted on Kaggle, and achieved the 10th place within less than one month's time.
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 • 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 • 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 • 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.
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 • 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 • 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.
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.
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.
1 code implementation • 19 Feb 2016 • Arnab Bhadury, Jianfei Chen, Jun Zhu, Shixia Liu
Dynamic topic models (DTMs) are very effective in discovering topics and capturing their evolution trends in time series data.
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 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 • 6 Dec 2017 • Danyang Sun, Tongzheng Ren, Chongxun Li, Hang Su, Jun Zhu
Automatically writing stylized Chinese characters is an attractive yet challenging task due to its wide applicabilities.
no code implementations • ICML 2018 • Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, Bo Zhang
Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation ability and particle efficiency compared to traditional variational inference and Markov Chain Monte Carlo methods.
no code implementations • 10 Apr 2018 • Zihao Xiao, Jianfei Chen, Jun Zhu
We also propose an extension to train pLSI and a method to prune the network to obey the limited fan-in of some NMSs.
no code implementations • 29 Mar 2018 • Zhize Li, Tianyi Zhang, Shuyu Cheng, Jun Zhu, Jian Li
In this paper, we apply the variance reduction tricks on Hamiltonian Monte Carlo and achieve better theoretical convergence results compared with the variance-reduced Langevin dynamics.
no code implementations • ICLR 2018 • Jiaxin Shi, Shengyang Sun, Jun Zhu
Recent progress in variational inference has paid much attention to the flexibility of variational posteriors.
no code implementations • 25 Jan 2018 • Haosheng Zou, Hang Su, Shihong Song, Jun Zhu
Crowd behavior understanding is crucial yet challenging across a wide range of applications, since crowd behavior is inherently determined by a sequential decision-making process based on various factors, such as the pedestrians' own destinations, interaction with nearby pedestrians and anticipation of upcoming events.
no code implementations • 7 Dec 2017 • Jianqiao Wangni, Jingwei Zhuo, Jun Zhu
Since the algorithm consumes a major computation cost in the testing phase, we propose a novel teacher-learner framework of learning computation-efficient kernel embeddings from specific data.
no code implementations • 23 Nov 2017 • Pengtao Xie, Jun Zhu, Eric P. Xing
We also extend our approach to "diversify" Bayesian nonparametric models where the number of components is infinite.
no code implementations • 13 Nov 2017 • Jianyu Wang, Zhishuai Zhang, Cihang Xie, Yuyin Zhou, Vittal Premachandran, Jun Zhu, Lingxi Xie, Alan Yuille
We use clustering algorithms to study the population activities of the features and extract a set of visual concepts which we show are visually tight and correspond to semantic parts of vehicles.
no code implementations • 18 Aug 2017 • Yinpeng Dong, Hang Su, Jun Zhu, Fan Bao
We find that: (1) the neurons in DNNs do not truly detect semantic objects/parts, but respond to objects/parts only as recurrent discriminative patches; (2) deep visual representations are not robust distributed codes of visual concepts because the representations of adversarial images are largely not consistent with those of real images, although they have similar visual appearance, both of which are different from previous findings.
no code implementations • ICML 2018 • Yichi Zhou, Jun Zhu, Jingwei Zhuo
Thompson sampling has impressive empirical performance for many multi-armed bandit problems.
no code implementations • 25 Jul 2017 • Jianyu Wang, Cihang Xie, Zhishuai Zhang, Jun Zhu, Lingxi Xie, Alan Yuille
Our approach detects semantic parts by accumulating the confidence of local visual cues.
no code implementations • CVPR 2017 • Yinpeng Dong, Hang Su, Jun Zhu, Bo Zhang
Interpretability of deep neural networks (DNNs) is essential since it enables users to understand the overall strengths and weaknesses of the models, conveys an understanding of how the models will behave in the future, and how to diagnose and correct potential problems.
no code implementations • 24 Nov 2014 • Jun Zhu, Jianfei Chen, Wen-Bo Hu, Bo Zhang
Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications with Big Data.
no code implementations • 23 Feb 2017 • Jianfei Chen, Jun Zhu, Jie Lu, Shixia Liu
Finally, we propose an efficient distributed implementation of PCGS through vectorization, pre-processing, and a careful design of the concurrent data structures and communication strategy.
no code implementations • 4 Feb 2017 • Shixia Liu, Xiting Wang, Mengchen Liu, Jun Zhu
Interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization, is very important for users to efficiently solve real-world artificial intelligence and data mining problems.
no code implementations • 7 Dec 2016 • Binghong Chen, Jun Zhu
Group-Lasso (gLasso) identifies important explanatory factors in predicting the response variable by considering the grouping structure over input variables.
no code implementations • 29 Nov 2016 • Jingkang Yang, Haohan Wang, Jun Zhu, Eric P. Xing
In this paper, we propose an extension of State Space Model to work with different sources of information together with its learning and inference algorithms.
no code implementations • NeurIPS 2016 • Yang Song, Jun Zhu, Yong Ren
We propose a vector-valued regression problem whose solution is equivalent to the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior distribution.
no code implementations • 27 Apr 2015 • Wenbo Hu, Jun Zhu, Bo Zhang
Bayesian max-margin models have shown superiority in various practical applications, such as text categorization, collaborative prediction, social network link prediction and crowdsourcing, and they conjoin the flexibility of Bayesian modeling and predictive strengths of max-margin learning.
no code implementations • 8 Oct 2016 • Kaiwei Li, Jianfei Chen, WenGuang Chen, Jun Zhu
Latent Dirichlet Allocation (LDA) is a popular tool for analyzing discrete count data such as text and images.
no code implementations • NeurIPS 2016 • Yong Ren, Jialian Li, Yucen Luo, Jun Zhu
Maximum mean discrepancy (MMD) has been successfully applied to learn deep generative models for characterizing a joint distribution of variables via kernel mean embedding.
no code implementations • 10 Jun 2016 • Shaohua Li, Jun Zhu, Chunyan Miao
PSDVec is a Python/Perl toolbox that learns word embeddings, i. e. the mapping of words in a natural language to continuous vectors which encode the semantic/syntactic regularities between the words.
no code implementations • 24 Apr 2016 • Mengchen Liu, Jiaxin Shi, Zhen Li, Chongxuan Li, Jun Zhu, Shixia Liu
Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification.
no code implementations • 15 Jun 2015 • Chao Du, Jun Zhu, Bo Zhang
We present doubly stochastic gradient MCMC, a simple and generic method for (approximate) Bayesian inference of deep generative models (DGMs) in a collapsed continuous parameter space.
no code implementations • 29 Oct 2015 • Jianfei Chen, Kaiwei Li, Jun Zhu, WenGuang Chen
We then develop WarpLDA, an LDA sampler which achieves both the best O(1) time complexity per token and the best O(K) scope of random access.
no code implementations • 24 Feb 2016 • Jun Zhu, Jiaming Song, Bei Chen
Our approach attempts to unite the ideas of max-margin learning and Bayesian nonparametrics to discover discriminative latent features for link prediction.
no code implementations • 19 Feb 2016 • Yong Ren, Yining Wang, Jun Zhu
Spectral methods have been applied to learn unsupervised topic models, such as latent Dirichlet allocation (LDA), with provable guarantees.
no code implementations • 23 Nov 2015 • Jun Zhu, Xianjie Chen, Alan L. Yuille
In this paper, we propose a deep part-based model (DeePM) for symbiotic object detection and semantic part localization.
no code implementations • 6 Jan 2016 • Yang Gao, Jianfei Chen, Jun Zhu
Streaming variational Bayes (SVB) is successful in learning LDA models in an online manner.
no code implementations • 24 Dec 2015 • Hugh Perkins, Minjie Xu, Jun Zhu, Bo Zhang
As one of the most popular classifiers, linear SVMs still have challenges in dealing with very large-scale problems, even though linear or sub-linear algorithms have been developed recently on single machines.
no code implementations • 7 Dec 2015 • Bei Chen, Ning Chen, Jun Zhu, Jiaming Song, Bo Zhang
We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features.
no code implementations • 7 Dec 2015 • Bei Chen, Jun Zhu, Nan Yang, Tian Tian, Ming Zhou, Bo Zhang
Modeling document structure is of great importance for discourse analysis and related applications.
no code implementations • 3 Dec 2015 • Jiaxin Shi, Jun Zhu
We present a new perspective on neural knowledge base (KB) embeddings, from which we build a framework that can model symbolic knowledge in the KB together with its learning process.
no code implementations • 17 Aug 2015 • Fangting Xia, Jun Zhu, Peng Wang, Alan Yuille
Parsing human body into semantic regions is crucial to human-centric analysis.
no code implementations • 10 Aug 2015 • Ning Chen, Jun Zhu, Jianfei Chen, Ting Chen
Empirical results on several real datasets demonstrate the effectiveness of dropout training on significantly boosting the classification accuracy of both linear and nonlinear SVMs.
no code implementations • 10 May 2015 • Renjie Liao, Jianping Shi, Ziyang Ma, Jun Zhu, Jiaya Jia
Metric learning aims to embed one metric space into another to benefit tasks like classification and clustering.
no code implementations • 28 Jun 2014 • Ni Lao, Jun Zhu
We prove that the gradient of candidate features can be represented solely as a function of signals and errors, and that CFI is an efficient approximation of gradient-based evaluation methods.
no code implementations • 16 Apr 2014 • Ning Chen, Jun Zhu, Jianfei Chen, Bo Zhang
To deal with the intractable expectation of the non-smooth hinge loss under corrupting distributions, we develop an iteratively re-weighted least square (IRLS) algorithm by exploring data augmentation techniques.
no code implementations • 5 Oct 2012 • Jun Zhu, Ning Chen, Eric P. Xing
When the regularization is induced from a linear operator on the posterior distributions, such as the expectation operator, we present a general convex-analysis theorem to characterize the solution of RegBayes.
no code implementations • 12 Dec 2013 • Tianlin Shi, Jun Zhu
Online Passive-Aggressive (PA) learning is an effective framework for performing max-margin online learning.
no code implementations • 10 Oct 2013 • Jun Zhu, Ning Chen, Hugh Perkins, Bo Zhang
Gibbs max-margin supervised topic models minimize an expected margin loss, which is an upper bound of the existing margin loss derived from an expected prediction rule.
no code implementations • 9 Oct 2013 • Ning Chen, Jun Zhu, Fei Xia, Bo Zhang
Many scientific and engineering fields involve analyzing network data.
no code implementations • ACL 2013 • Jun Zhu, Xun Zheng, Bo Zhang
Supervised topic models with a logistic likelihood have two issues that potentially limit their practical use: 1) response variables are usually over-weighted by document word counts; and 2) existing variational inference methods make strict mean-field assumptions.
no code implementations • 10 Jul 2018 • Kun Xu, Haoyu Liang, Jun Zhu, Hang Su, Bo Zhang
Deep generative models have shown promising results in generating realistic images, but it is still non-trivial to generate images with complicated structures.
no code implementations • ICLR 2019 • Chao Du, Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang
Implicit generative models are difficult to train as no explicit density functions are defined.
no code implementations • 16 Sep 2018 • Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Luping Shi
Spiking neural networks (SNNs) that enables energy efficient implementation on emerging neuromorphic hardware are gaining more attention.