Search Results for author: Tianlong Chen

Found 81 papers, 54 papers with code

HALO: Hardware-Aware Learning to Optimize

1 code implementation ECCV 2020 Chaojian Li, Tianlong Chen, Haoran You, Zhangyang Wang, Yingyan Lin

There has been an explosive demand for bringing machine learning (ML) powered intelligence into numerous Internet-of-Things (IoT) devices.

APP: Anytime Progressive Pruning

1 code implementation4 Apr 2022 Diganta Misra, Bharat Runwal, Tianlong Chen, Zhangyang Wang, Irina Rish

With the latest advances in deep learning, there has been a lot of focus on the online learning paradigm due to its relevance in practical settings.

Network Pruning online learning

Symbolic Learning to Optimize: Towards Interpretability and Scalability

1 code implementation ICLR 2022 Wenqing Zheng, Tianlong Chen, Ting-Kuei Hu, Zhangyang Wang

Recent studies on Learning to Optimize (L2O) suggest a promising path to automating and accelerating the optimization procedure for complicated tasks.

Optimizer Amalgamation

1 code implementation ICLR 2022 Tianshu Huang, Tianlong Chen, Sijia Liu, Shiyu Chang, Lisa Amini, Zhangyang Wang

Selecting an appropriate optimizer for a given problem is of major interest for researchers and practitioners.

The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy

1 code implementation12 Mar 2022 Tianlong Chen, Zhenyu Zhang, Yu Cheng, Ahmed Awadallah, Zhangyang Wang

However, a "head-to-toe assessment" regarding the extent of redundancy in ViTs, and how much we could gain by thoroughly mitigating such, has been absent for this field.

Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice

1 code implementation9 Mar 2022 Peihao Wang, Wenqing Zheng, Tianlong Chen, Zhangyang Wang

The first technique, termed AttnScale, decomposes a self-attention block into low-pass and high-pass components, then rescales and combines these two filters to produce an all-pass self-attention matrix.

Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance

no code implementations5 Mar 2022 Shiwei Liu, Yuesong Tian, Tianlong Chen, Li Shen

Even more unconventionally, our proposed method enables directly training sparse unbalanced GANs with an extremely sparse generator from scratch.

Model Compression

Sparsity Winning Twice: Better Robust Generalization from More Efficient Training

1 code implementation ICLR 2022 Tianlong Chen, Zhenyu Zhang, Pengjun Wang, Santosh Balachandra, Haoyu Ma, Zehao Wang, Zhangyang Wang

We introduce two alternatives for sparse adversarial training: (i) static sparsity, by leveraging recent results from the lottery ticket hypothesis to identify critical sparse subnetworks arising from the early training; (ii) dynamic sparsity, by allowing the sparse subnetwork to adaptively adjust its connectivity pattern (while sticking to the same sparsity ratio) throughout training.

Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets

1 code implementation9 Feb 2022 Tianlong Chen, Xuxi Chen, Xiaolong Ma, Yanzhi Wang, Zhangyang Wang

The lottery ticket hypothesis (LTH) has shown that dense models contain highly sparse subnetworks (i. e., winning tickets) that can be trained in isolation to match full accuracy.

The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

1 code implementation ICLR 2022 Shiwei Liu, Tianlong Chen, Xiaohan Chen, Li Shen, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy

In this paper, we focus on sparse training and highlight a perhaps counter-intuitive finding, that random pruning at initialization can be quite powerful for the sparse training of modern neural networks.

Adversarial Robustness Out-of-Distribution Detection

VAQF: Fully Automatic Software-Hardware Co-Design Framework for Low-Bit Vision Transformer

no code implementations17 Jan 2022 Mengshu Sun, Haoyu Ma, Guoliang Kang, Yifan Jiang, Tianlong Chen, Xiaolong Ma, Zhangyang Wang, Yanzhi Wang

To the best of our knowledge, this is the first time quantization has been incorporated into ViT acceleration on FPGAs with the help of a fully automatic framework to guide the quantization strategy on the software side and the accelerator implementations on the hardware side given the target frame rate.

Frame Quantization

Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations

1 code implementation4 Jan 2022 Yuning You, Tianlong Chen, Zhangyang Wang, Yang shen

Accordingly, we have extended the prefabricated discrete prior in the augmentation set, to a learnable continuous prior in the parameter space of graph generators, assuming that graph priors per se, similar to the concept of image manifolds, can be learned by data generation.

Contrastive Learning Graph Learning

Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling

1 code implementation NeurIPS 2021 Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang

Contrastive learning approaches have achieved great success in learning visual representations with few labels of the target classes.

Contrastive Learning

DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models

1 code implementation30 Oct 2021 Xuxi Chen, Tianlong Chen, Yu Cheng, Weizhu Chen, Zhangyang Wang, Ahmed Hassan Awadallah

To address these pain points, we propose a framework for resource- and parameter-efficient fine-tuning by leveraging the sparsity prior in both weight updates and the final model weights.

You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership

1 code implementation NeurIPS 2021 Xuxi Chen, Tianlong Chen, Zhenyu Zhang, Zhangyang Wang

The lottery ticket hypothesis (LTH) emerges as a promising framework to leverage a special sparse subnetwork (i. e., winning ticket) instead of a full model for both training and inference, that can lower both costs without sacrificing the performance.

CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks

no code implementations28 Oct 2021 Haotian Xue, Kaixiong Zhou, Tianlong Chen, Kai Guo, Xia Hu, Yi Chang, Xin Wang

In this paper, we investigate GNNs from the lens of weight and feature loss landscapes, i. e., the loss changes with respect to model weights and node features, respectively.

Towards Lifelong Learning of Multilingual Text-To-Speech Synthesis

1 code implementation9 Oct 2021 Mu Yang, Shaojin Ding, Tianlong Chen, Tong Wang, Zhangyang Wang

This work presents a lifelong learning approach to train a multilingual Text-To-Speech (TTS) system, where each language was seen as an individual task and was learned sequentially and continually.

Speech Synthesis Text-To-Speech Synthesis

Universality of Winning Tickets: A Renormalization Group Perspective

no code implementations7 Oct 2021 William T. Redman, Tianlong Chen, Zhangyang Wang, Akshunna S. Dogra

We make use of renormalization group theory, a powerful tool from theoretical physics, to address this need.

Scaling the Depth of Vision Transformers via the Fourier Domain Analysis

no code implementations ICLR 2022 Peihao Wang, Wenqing Zheng, Tianlong Chen, Zhangyang Wang

The first technique, termed AttnScale, decomposes a self-attention block into low-pass and high-pass components, then rescales and combines these two filters to produce an all-pass self-attention matrix.

Generalizable Learning to Optimize into Wide Valleys

no code implementations29 Sep 2021 Junjie Yang, Tianlong Chen, Mingkang Zhu, Fengxiang He, DaCheng Tao, Yingbin Liang, Zhangyang Wang

Learning to optimize (L2O) has gained increasing popularity in various optimization tasks, since classical optimizers usually require laborious, problem-specific design and hyperparameter tuning.

Stingy Teacher: Sparse Logits Suffice to Fail Knowledge Distillation

no code implementations29 Sep 2021 Haoyu Ma, Yifan Huang, Tianlong Chen, Hao Tang, Chenyu You, Zhangyang Wang, Xiaohui Xie

However, it is unclear why the distorted distribution of the logits is catastrophic to the student model.

Knowledge Distillation

AutoCoG: A Unified Data-Modal Co-Search Framework for Graph Neural Networks

no code implementations29 Sep 2021 Duc N.M Hoang, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang

Despite the preliminary success, we argue that for GNNs, NAS has to be customized further, due to the topological complicacy of GNN input data (graph) as well as the notorious training instability.

Data Augmentation Language Modelling +1

Sparse Unbalanced GAN Training with In-Time Over-Parameterization

no code implementations29 Sep 2021 Shiwei Liu, Yuesong Tian, Tianlong Chen, Li Shen

Perhaps most importantly, we find instead of inheriting parameters from expensive pre-trained GANs, directly training sparse GANs from scratch can be a much more efficient solution.

Model Compression

Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How

no code implementations ICLR 2022 Yuning You, Yue Cao, Tianlong Chen, Zhangyang Wang, Yang shen

Optimizing an objective function with uncertainty awareness is well-known to improve the accuracy and confidence of optimization solutions.

Variational Inference

Inductive Lottery Ticket Learning for Graph Neural Networks

no code implementations29 Sep 2021 Yongduo Sui, Xiang Wang, Tianlong Chen, Xiangnan He, Tat-Seng Chua

In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity.

Graph Classification Node Classification

Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, And No Retraining

no code implementations ICLR 2022 Lu Miao, Xiaolong Luo, Tianlong Chen, Wuyang Chen, Dong Liu, Zhangyang Wang

Conventional methods often require (iterative) pruning followed by re-training, which not only incurs large overhead beyond the original DNN training but also can be sensitive to retraining hyperparameters.

Lottery Tickets can have Structural Sparsity

no code implementations29 Sep 2021 Tianlong Chen, Xuxi Chen, Xiaolong Ma, Yanzhi Wang, Zhangyang Wang

The lottery ticket hypothesis (LTH) has shown that dense models contain highly sparse subnetworks (i. e., $\textit{winning tickets}$) that can be trained in isolation to match full accuracy.

Universality of Deep Neural Network Lottery Tickets: A Renormalization Group Perspective

no code implementations29 Sep 2021 William T Redman, Tianlong Chen, Akshunna S. Dogra, Zhangyang Wang

Foundational work on the Lottery Ticket Hypothesis has suggested an exciting corollary: winning tickets found in the context of one task can be transferred to similar tasks, possibly even across different architectures.

Audio Lottery: Speech Recognition Made Ultra-Lightweight, Noise-Robust, and Transferable

no code implementations ICLR 2022 Shaojin Ding, Tianlong Chen, Zhangyang Wang

In this paper, we investigate the tantalizing possibility of using lottery ticket hypothesis to discover lightweight speech recognition models, that are (1) robust to various noise existing in speech; (2) transferable to fit the open-world personalization; and 3) compatible with structured sparsity.

Speech Recognition

Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study

1 code implementation24 Aug 2021 Tianlong Chen, Kaixiong Zhou, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

In view of those, we present the first fair and reproducible benchmark dedicated to assessing the "tricks" of training deep GNNs.

DANCE: DAta-Network Co-optimization for Efficient Segmentation Model Training and Inference

no code implementations16 Jul 2021 Chaojian Li, Wuyang Chen, Yuchen Gu, Tianlong Chen, Yonggan Fu, Zhangyang Wang, Yingyan Lin

Semantic segmentation for scene understanding is nowadays widely demanded, raising significant challenges for the algorithm efficiency, especially its applications on resource-limited platforms.

Scene Understanding Semantic Segmentation

Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?

1 code implementation NeurIPS 2021 Xiaolong Ma, Geng Yuan, Xuan Shen, Tianlong Chen, Xuxi Chen, Xiaohan Chen, Ning Liu, Minghai Qin, Sijia Liu, Zhangyang Wang, Yanzhi Wang

Based on our analysis, we summarize a guideline for parameter settings in regards of specific architecture characteristics, which we hope to catalyze the research progress on the topic of lottery ticket hypothesis.

Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks

1 code implementation24 Jun 2021 Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Wenqing Zheng, Zhangyang Wang, Alejandro Ribeiro, Brian M. Sadler

Our framework is implemented by a cascade of a convolutional and a graph neural network (CNN / GNN), addressing agent-level visual perception and feature learning, as well as swarm-level communication, local information aggregation and agent action inference, respectively.

Imitation Learning

Sparse Training via Boosting Pruning Plasticity with Neuroregeneration

1 code implementation NeurIPS 2021 Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu

Works on lottery ticket hypothesis (LTH) and single-shot network pruning (SNIP) have raised a lot of attention currently on post-training pruning (iterative magnitude pruning), and before-training pruning (pruning at initialization).

Network Pruning Sparse Learning

Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm

1 code implementation10 Jun 2021 Mingkang Zhu, Tianlong Chen, Zhangyang Wang

Compared to state-of-the-art methods, our homotopy attack leads to significantly fewer perturbations, e. g., reducing 42. 91% on CIFAR-10 and 75. 03% on ImageNet (average case, targeted attack), at similar maximal perturbation magnitudes, when still achieving 100% attack success rates.

Adversarial Attack

Graph Contrastive Learning Automated

2 code implementations10 Jun 2021 Yuning You, Tianlong Chen, Yang shen, Zhangyang Wang

Unfortunately, unlike its counterpart on image data, the effectiveness of GraphCL hinges on ad-hoc data augmentations, which have to be manually picked per dataset, by either rules of thumb or trial-and-errors, owing to the diverse nature of graph data.

Contrastive Learning Representation Learning +1

Chasing Sparsity in Vision Transformers: An End-to-End Exploration

1 code implementation NeurIPS 2021 Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang

For example, our sparsified DeiT-Small at (5%, 50%) sparsity for (data, architecture), improves 0. 28% top-1 accuracy, and meanwhile enjoys 49. 32% FLOPs and 4. 40% running time savings.

Self-Damaging Contrastive Learning

1 code implementation6 Jun 2021 Ziyu Jiang, Tianlong Chen, Bobak Mortazavi, Zhangyang Wang

Hence, the key innovation in SDCLR is to create a dynamic self-competitor model to contrast with the target model, which is a pruned version of the latter.

Contrastive Learning Network Pruning +1

Efficient Lottery Ticket Finding: Less Data is More

1 code implementation6 Jun 2021 Zhenyu Zhang, Xuxi Chen, Tianlong Chen, Zhangyang Wang

We observe that a high-quality winning ticket can be found with training and pruning the dense network on the very compact PrAC set, which can substantially save training iterations for the ticket finding process.

GANs Can Play Lottery Tickets Too

1 code implementation ICLR 2021 Xuxi Chen, Zhenyu Zhang, Yongduo Sui, Tianlong Chen

In this work, we for the first time study the existence of such trainable matching subnetworks in deep GANs.

Image-to-Image Translation

Improving Contrastive Learning on Imbalanced Data via Open-World Sampling

1 code implementation NeurIPS 2021 Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang

Contrastive learning approaches have achieved great success in learning visual representations with few labels of the target classes.

Contrastive Learning

Undistillable: Making A Nasty Teacher That CANNOT teach students

1 code implementation ICLR 2021 Haoyu Ma, Tianlong Chen, Ting-Kuei Hu, Chenyu You, Xiaohui Xie, Zhangyang Wang

Knowledge Distillation (KD) is a widely used technique to transfer knowledge from pre-trained teacher models to (usually more lightweight) student models.

Knowledge Distillation

Troubleshooting Blind Image Quality Models in the Wild

no code implementations CVPR 2021 Zhihua Wang, Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma

Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics.

Blind Image Quality Assessment Network Pruning

Learning Transferable 3D Adversarial Cloaks for Deep Trained Detectors

1 code implementation22 Apr 2021 Arman Maesumi, Mingkang Zhu, Yi Wang, Tianlong Chen, Zhangyang Wang, Chandrajit Bajaj

This paper presents a novel patch-based adversarial attack pipeline that trains adversarial patches on 3D human meshes.

Adversarial Attack

"BNN - BN = ?": Training Binary Neural Networks without Batch Normalization

1 code implementation16 Apr 2021 Tianlong Chen, Zhenyu Zhang, Xu Ouyang, Zechun Liu, Zhiqiang Shen, Zhangyang Wang

However, the BN layer is costly to calculate and is typically implemented with non-binary parameters, leaving a hurdle for the efficient implementation of BNN training.

Image Classification

Learning to Optimize: A Primer and A Benchmark

1 code implementation23 Mar 2021 Tianlong Chen, Xiaohan Chen, Wuyang Chen, Howard Heaton, Jialin Liu, Zhangyang Wang, Wotao Yin

It automates the design of an optimization method based on its performance on a set of training problems.

Adversarial Feature Augmentation and Normalization for Visual Recognition

1 code implementation22 Mar 2021 Tianlong Chen, Yu Cheng, Zhe Gan, JianFeng Wang, Lijuan Wang, Zhangyang Wang, Jingjing Liu

Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models.

Classification Data Augmentation +1

Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective

1 code implementation NeurIPS 2021 Tianlong Chen, Yu Cheng, Zhe Gan, Jingjing Liu, Zhangyang Wang

Training generative adversarial networks (GANs) with limited real image data generally results in deteriorated performance and collapsed models.

Data Augmentation

A Unified Lottery Ticket Hypothesis for Graph Neural Networks

1 code implementation12 Feb 2021 Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, Zhangyang Wang

With graphs rapidly growing in size and deeper graph neural networks (GNNs) emerging, the training and inference of GNNs become increasingly expensive.

Link Prediction Node Classification

Spending Your Winning Lottery Better After Drawing It

1 code implementation8 Jan 2021 Ajay Kumar Jaiswal, Haoyu Ma, Tianlong Chen, Ying Ding, Zhangyang Wang

In this paper, we demonstrate that it is unnecessary for spare retraining to strictly inherit those properties from the dense network.

Knowledge Distillation

Efficiently Troubleshooting Image Segmentation Models with Human-In-The-Loop

no code implementations1 Jan 2021 Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma

Image segmentation lays the foundation for many high-stakes vision applications such as autonomous driving and medical image analysis.

Autonomous Driving Semantic Segmentation

Bayesian Learning to Optimize: Quantifying the Optimizer Uncertainty

no code implementations1 Jan 2021 Yue Cao, Tianlong Chen, Zhangyang Wang, Yang shen

Optimizing an objective function with uncertainty awareness is well-known to improve the accuracy and confidence of optimization solutions.

Image Classification Variational Inference

Robust Overfitting may be mitigated by properly learned smoothening

no code implementations ICLR 2021 Tianlong Chen, Zhenyu Zhang, Sijia Liu, Shiyu Chang, Zhangyang Wang

A recent study (Rice et al., 2020) revealed overfitting to be a dominant phenomenon in adversarially robust training of deep networks, and that appropriate early-stopping of adversarial training (AT) could match the performance gains of most recent algorithmic improvements.

Knowledge Distillation

Learning A Minimax Optimizer: A Pilot Study

no code implementations ICLR 2021 Jiayi Shen, Xiaohan Chen, Howard Heaton, Tianlong Chen, Jialin Liu, Wotao Yin, Zhangyang Wang

We first present Twin L2O, the first dedicated minimax L2O framework consisting of two LSTMs for updating min and max variables, respectively.

ALFA: Adversarial Feature Augmentation for Enhanced Image Recognition

no code implementations1 Jan 2021 Tianlong Chen, Yu Cheng, Zhe Gan, Yu Hu, Zhangyang Wang, Jingjing Liu

Adversarial training is an effective method to combat adversarial attacks in order to create robust neural networks.

The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models

1 code implementation CVPR 2021 Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, Zhangyang Wang

We extend the scope of LTH and question whether matching subnetworks still exist in pre-trained computer vision models, that enjoy the same downstream transfer performance.

Robust Pre-Training by Adversarial Contrastive Learning

1 code implementation NeurIPS 2020 Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang

Recent work has shown that, when integrated with adversarial training, self-supervised pre-training can lead to state-of-the-art robustness In this work, we improve robustness-aware self-supervised pre-training by learning representations that are consistent under both data augmentations and adversarial perturbations.

Adversarial Robustness Contrastive Learning

Graph Contrastive Learning with Augmentations

2 code implementations NeurIPS 2020 Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang shen

In this paper, we propose a graph contrastive learning (GraphCL) framework for learning unsupervised representations of graph data.

Contrastive Learning Representation Learning +2

Training Stronger Baselines for Learning to Optimize

1 code implementation NeurIPS 2020 Tianlong Chen, Weiyi Zhang, Jingyang Zhou, Shiyu Chang, Sijia Liu, Lisa Amini, Zhangyang Wang

Learning to optimize (L2O) has gained increasing attention since classical optimizers require laborious problem-specific design and hyperparameter tuning.

Imitation Learning

PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning

no code implementations6 Oct 2020 Yuli Zheng, Zhenyu Wu, Ye Yuan, Tianlong Chen, Zhangyang Wang

While machine learning is increasingly used in this field, the resulting large-scale collection of user private information has reinvigorated the privacy debate, considering dozens of data breach incidents every year caused by unauthorized hackers, and (potentially even more) information misuse/abuse by authorized parties.

Can 3D Adversarial Logos Cloak Humans?

1 code implementation25 Jun 2020 Yi Wang, Jingyang Zhou, Tianlong Chen, Sijia Liu, Shiyu Chang, Chandrajit Bajaj, Zhangyang Wang

Contrary to the traditional adversarial patch, this new form of attack is mapped into the 3D object world and back-propagates to the 2D image domain through differentiable rendering.

Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training

1 code implementation ICML 2020 Xuxi Chen, Wuyang Chen, Tianlong Chen, Ye Yuan, Chen Gong, Kewei Chen, Zhangyang Wang

Many real-world applications have to tackle the Positive-Unlabeled (PU) learning problem, i. e., learning binary classifiers from a large amount of unlabeled data and a few labeled positive examples.

When Does Self-Supervision Help Graph Convolutional Networks?

1 code implementation ICML 2020 Yuning You, Tianlong Chen, Zhangyang Wang, Yang shen

We first elaborate three mechanisms to incorporate self-supervision into GCNs, analyze the limitations of pretraining & finetuning and self-training, and proceed to focus on multi-task learning.

Multi-Task Learning Representation Learning +1

AutoSpeech: Neural Architecture Search for Speaker Recognition

3 code implementations7 May 2020 Shaojin Ding, Tianlong Chen, Xinyu Gong, Weiwei Zha, Zhangyang Wang

Speaker recognition systems based on Convolutional Neural Networks (CNNs) are often built with off-the-shelf backbones such as VGG-Net or ResNet.

Image Classification Neural Architecture Search +3

Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing

no code implementations LREC 2020 Xiaojing Yu, Tianlong Chen, Zhengjie Yu, Huiyu Li, Yang Yang, Xiaoqian Jiang, Anxiao Jiang

Compared to existing datasets, the queries in the dataset here are derived from the eligibility criteria of clinical trials and include \textit{Order-sensitive, Counting-based, and Boolean-type} cases which are not seen before.

Semantic Parsing Text-To-Sql

L^2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks

2 code implementations CVPR 2020 Yuning You, Tianlong Chen, Zhangyang Wang, Yang shen

Graph convolution networks (GCN) are increasingly popular in many applications, yet remain notoriously hard to train over large graph datasets.

Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning

1 code implementation CVPR 2020 Tianlong Chen, Sijia Liu, Shiyu Chang, Yu Cheng, Lisa Amini, Zhangyang Wang

We conduct extensive experiments to demonstrate that the proposed framework achieves large performance margins (eg, 3. 83% on robust accuracy and 1. 3% on standard accuracy, on the CIFAR-10 dataset), compared with the conventional end-to-end adversarial training baseline.

Adversarial Robustness

I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively

1 code implementation ICLR 2020 Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma

On the other hand, the trained classifiers have traditionally been evaluated on small and fixed sets of test images, which are deemed to be extremely sparsely distributed in the space of all natural images.

Image Classification

Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference

2 code implementations ICLR 2020 Ting-Kuei Hu, Tianlong Chen, Haotao Wang, Zhangyang Wang

Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) (Tsipras et al., 2019).

VGAI: End-to-End Learning of Vision-Based Decentralized Controllers for Robot Swarms

no code implementations6 Feb 2020 Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Zhangyang Wang, Alejandro Ribeiro, Brian M. Sadler

More specifically, we consider that each robot has access to a visual perception of the immediate surroundings, and communication capabilities to transmit and receive messages from other neighboring robots.

Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification

1 code implementation26 Nov 2019 Ye Yuan, Wuyang Chen, Tianlong Chen, Yang Yang, Zhou Ren, Zhangyang Wang, Gang Hua

Many real-world applications, such as city-scale traffic monitoring and control, requires large-scale re-identification.

Learning to Optimize in Swarms

1 code implementation NeurIPS 2019 Yue Cao, Tianlong Chen, Zhangyang Wang, Yang shen

Learning to optimize has emerged as a powerful framework for various optimization and machine learning tasks.

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