Search Results for author: Shiyu Chang

Found 93 papers, 53 papers with code

Grasping the Arrow of Time from the Singularity: Decoding Micromotion in Low-dimensional Latent Spaces from StyleGAN

1 code implementation27 Apr 2022 Qiucheng Wu, Yifan Jiang, Junru Wu, Kai Wang, Gong Zhang, Humphrey Shi, Zhangyang Wang, Shiyu Chang

To study the motion features in the latent space of StyleGAN, in this paper, we hypothesize and demonstrate that a series of meaningful, natural, and versatile small, local movements (referred to as "micromotion", such as expression, head movement, and aging effect) can be represented in low-rank spaces extracted from the latent space of a conventionally pre-trained StyleGAN-v2 model for face generation, with the guidance of proper "anchors" in the form of either short text or video clips.

Disentanglement Face Generation

Improving Self-Supervised Speech Representations by Disentangling Speakers

no code implementations20 Apr 2022 Kaizhi Qian, Yang Zhang, Heting Gao, Junrui Ni, Cheng-I Lai, David Cox, Mark Hasegawa-Johnson, Shiyu Chang

Self-supervised learning in speech involves training a speech representation network on a large-scale unannotated speech corpus, and then applying the learned representations to downstream tasks.

Disentanglement Self-Supervised Learning

Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection

no code implementations15 Apr 2022 Minqian Liu, Shiyu Chang, Lifu Huang

Lifelong event detection aims to incrementally update a model with new event types and data while retaining the capability on previously learned old types.

Event Detection

Unsupervised Text-to-Speech Synthesis by Unsupervised Automatic Speech Recognition

no code implementations29 Mar 2022 Junrui Ni, Liming Wang, Heting Gao, Kaizhi Qian, Yang Zhang, Shiyu Chang, Mark Hasegawa-Johnson

An unsupervised text-to-speech synthesis (TTS) system learns to generate the speech waveform corresponding to any written sentence in a language by observing: 1) a collection of untranscribed speech waveforms in that language; 2) a collection of texts written in that language without access to any transcribed speech.

Automatic Speech Recognition Speech Synthesis +1

How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective

1 code implementation ICLR 2022 Yimeng Zhang, Yuguang Yao, Jinghan Jia, JinFeng Yi, Mingyi Hong, Shiyu Chang, Sijia Liu

To tackle this problem, we next propose to prepend an autoencoder (AE) to a given (black-box) model so that DS can be trained using variance-reduced ZO optimization.

Adversarial Robustness Image Classification +1

Adversarial Support Alignment

1 code implementation ICLR 2022 Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola

Furthermore, we show that our approach can be viewed as a limit of existing notions of alignment by increasing transportation assignment tolerance.

Domain Adaptation

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.

Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization

1 code implementation23 Dec 2021 Yihua Zhang, Guanhua Zhang, Prashant Khanduri, Mingyi Hong, Shiyu Chang, Sijia Liu

We first show that the commonly-used Fast-AT is equivalent to using a stochastic gradient algorithm to solve a linearized BLO problem involving a sign operation.

Adversarial Defense

Understanding Interlocking Dynamics of Cooperative Rationalization

1 code implementation NeurIPS 2021 Mo Yu, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola

The selection mechanism is commonly integrated into the model itself by specifying a two-component cascaded system consisting of a rationale generator, which makes a binary selection of the input features (which is the rationale), and a predictor, which predicts the output based only on the selected features.

Hard Attention

Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding

no code implementations Findings (ACL) 2022 Sijia Wang, Mo Yu, Shiyu Chang, Lichao Sun, Lifu Huang

Event extraction is typically modeled as a multi-class classification problem where event types and argument roles are treated as atomic symbols.

Event Extraction Multi-class Classification

Global Rhythm Style Transfer Without Text Transcriptions

no code implementations16 Jun 2021 Kaizhi Qian, Yang Zhang, Shiyu Chang, JinJun Xiong, Chuang Gan, David Cox, Mark Hasegawa-Johnson

In this paper, we propose AutoPST, which can disentangle global prosody style from speech without relying on any text transcriptions.

Representation Learning Style Transfer

Learning Stable Classifiers by Transferring Unstable Features

no code implementations15 Jun 2021 Yujia Bao, Shiyu Chang, Regina Barzilay

Specifically, we derive a representation that encodes the unstable features by contrasting different data environments in the source task.

Transfer Learning

Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers

1 code implementation26 May 2021 Yujia Bao, Shiyu Chang, Regina Barzilay

In this work, we prove that by interpolating the distributions of the correct predictions and the wrong predictions, we can uncover an oracle distribution where the unstable correlation vanishes.

Image Classification Text Classification

TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up

9 code implementations NeurIPS 2021 Yifan Jiang, Shiyu Chang, Zhangyang Wang

Our vanilla GAN architecture, dubbed TransGAN, consists of a memory-friendly transformer-based generator that progressively increases feature resolution, and correspondingly a multi-scale discriminator to capture simultaneously semantic contexts and low-level textures.

Data Augmentation Image Generation

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

Self-Progressing Robust Training

1 code implementation22 Dec 2020 Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das

Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy machine learning systems.

Adversarial Robustness

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.

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

Lifelong Object Detection

no code implementations2 Sep 2020 Wang Zhou, Shiyu Chang, Norma Sosa, Hendrik Hamann, David Cox

Recent advances in object detection have benefited significantly from rapid developments in deep neural networks.

Knowledge Distillation Object Detection +1

Proper Network Interpretability Helps Adversarial Robustness in Classification

1 code implementation ICML 2020 Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Cynthia Liu, Pin-Yu Chen, Shiyu Chang, Luca Daniel

Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability (namely, making network interpretation maps visually similar), or interpretability is itself susceptible to adversarial attacks.

Adversarial Robustness Classification +2

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.

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

Invariant Rationalization

1 code implementation ICML 2020 Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola

Selective rationalization improves neural network interpretability by identifying a small subset of input features -- the rationale -- that best explains or supports the prediction.

Context-Aware Conversation Thread Detection in Multi-Party Chat

no code implementations IJCNLP 2019 Ming Tan, Dakuo Wang, Yupeng Gao, Haoyu Wang, Saloni Potdar, Xiaoxiao Guo, Shiyu Chang, Mo Yu

In multi-party chat, it is common for multiple conversations to occur concurrently, leading to intermingled conversation threads in chat logs.

Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control

2 code implementations IJCNLP 2019 Mo Yu, Shiyu Chang, Yang Zhang, Tommi S. Jaakkola

Moreover, we explicitly control the rationale complement via an adversary so as not to leave any useful information out of the selection.

A Game Theoretic Approach to Class-wise Selective Rationalization

1 code implementation NeurIPS 2019 Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola

Selection of input features such as relevant pieces of text has become a common technique of highlighting how complex neural predictors operate.

Sentiment Analysis

An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack

no code implementations ICLR 2019 Yang Zhang, Shiyu Chang, Mo Yu, Kaizhi Qian

The second paradigm, called the zero-confidence attack, finds the smallest perturbation needed to cause mis-classification, also known as the margin of an input feature.

Adversarial Attack

Continuous Convolutional Neural Network forNonuniform Time Series

no code implementations25 Sep 2019 Hui Shi, Yang Zhang, Hao Wu, Shiyu Chang, Kaizhi Qian, Mark Hasegawa-Johnson, Jishen Zhao

Convolutional neural network (CNN) for time series data implicitly assumes that the data are uniformly sampled, whereas many event-based and multi-modal data are nonuniform or have heterogeneous sampling rates.

Time Series

Visual Interpretability Alone Helps Adversarial Robustness

no code implementations25 Sep 2019 Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Pin-Yu Chen, Shiyu Chang, Luca Daniel

Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability, and interpretability is itself susceptible to adversarial attacks.

Adversarial Robustness

SPROUT: Self-Progressing Robust Training

no code implementations25 Sep 2019 Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das

Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy and reliable machine learning systems.

Adversarial Robustness

Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering

no code implementations WS 2019 Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Hong Wang, Shiyu Chang, Murray Campbell, William Yang Wang

To resolve this issue, we introduce a new sub-problem of open-domain multi-hop QA, which aims to recognize the bridge (\emph{i. e.}, the anchor that links to the answer passage) from the context of a set of start passages with a reading comprehension model.

Information Retrieval Multi-hop Question Answering +2

Meta Reasoning over Knowledge Graphs

no code implementations13 Aug 2019 Hong Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang

The ability to reason over learned knowledge is an innate ability for humans and humans can easily master new reasoning rules with only a few demonstrations.

Few-Shot Learning Knowledge Base Completion +1

TWEETQA: A Social Media Focused Question Answering Dataset

no code implementations ACL 2019 Wenhan Xiong, Jiawei Wu, Hong Wang, Vivek Kulkarni, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang

With social media becoming increasingly pop-ular on which lots of news and real-time eventsare reported, developing automated questionanswering systems is critical to the effective-ness of many applications that rely on real-time knowledge.

Question Answering

Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers

1 code implementation NeurIPS 2019 Guang-He Lee, Yang Yuan, Shiyu Chang, Tommi S. Jaakkola

Specifically, an $\ell_2$ bounded adversary cannot alter the ensemble prediction generated by an additive isotropic Gaussian noise, where the radius for the adversary depends on both the variance of the distribution as well as the ensemble margin at the point of interest.

Adversarial Robustness

Self-Supervised Learning for Contextualized Extractive Summarization

1 code implementation ACL 2019 Hong Wang, Xin Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang

Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss, which does not explicitly capture the global context at the document level.

Extractive Summarization Self-Supervised Learning

Coupled Variational Recurrent Collaborative Filtering

1 code implementation11 Jun 2019 Qingquan Song, Shiyu Chang, Xia Hu

To bridge the gap, in this paper, we propose a Coupled Variational Recurrent Collaborative Filtering (CVRCF) framework based on the idea of Deep Bayesian Learning to handle the streaming recommendation problem.

Collaborative Filtering Recommendation Systems +1

Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader

2 code implementations ACL 2019 Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang

We propose a new end-to-end question answering model, which learns to aggregate answer evidence from an incomplete knowledge base (KB) and a set of retrieved text snippets.

Question Answering

Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets

2 code implementations ACL 2019 Guanhua Zhang, Bing Bai, Jian Liang, Kun Bai, Shiyu Chang, Mo Yu, Conghui Zhu, Tiejun Zhao

Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process.

Selection bias

AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss

10 code implementations14 May 2019 Kaizhi Qian, Yang Zhang, Shiyu Chang, Xuesong Yang, Mark Hasegawa-Johnson

On the other hand, CVAE training is simple but does not come with the distribution-matching property of a GAN.

Style Transfer Voice Conversion

Hybrid Reinforcement Learning with Expert State Sequences

1 code implementation11 Mar 2019 Xiaoxiao Guo, Shiyu Chang, Mo Yu, Gerald Tesauro, Murray Campbell

The empirical results show that (1) the agents are able to leverage state expert sequences to learn faster than pure reinforcement learning baselines, (2) our tensor-based action inference model is advantageous compared to standard deep neural networks in inferring expert actions, and (3) the hybrid policy optimization objective is robust against noise in expert state sequences.

Atari Games Imitation Learning +1

Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing

1 code implementation NAACL 2019 Wenhan Xiong, Jiawei Wu, Deren Lei, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang

Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance.

Entity Typing

Sentence Embedding Alignment for Lifelong Relation Extraction

2 code implementations NAACL 2019 Hong Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang

We formulate such a challenging problem as lifelong relation extraction and investigate memory-efficient incremental learning methods without catastrophically forgetting knowledge learned from previous tasks.

Incremental Learning Relation Extraction +2

A Simple Non-i.i.d. Sampling Approach for Efficient Training and Better Generalization

no code implementations23 Nov 2018 Bowen Cheng, Yunchao Wei, Jiahui Yu, Shiyu Chang, JinJun Xiong, Wen-mei Hwu, Thomas S. Huang, Humphrey Shi

While training on samples drawn from independent and identical distribution has been a de facto paradigm for optimizing image classification networks, humans learn new concepts in an easy-to-hard manner and on the selected examples progressively.

General Classification Image Classification +5

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Learning Corresponded Rationales for Text Matching

no code implementations27 Sep 2018 Mo Yu, Shiyu Chang, Tommi S Jaakkola

The ability to predict matches between two sources of text has a number of applications including natural language inference (NLI) and question answering (QA).

Natural Language Inference Question Answering +1

Improving Reinforcement Learning Based Image Captioning with Natural Language Prior

1 code implementation EMNLP 2018 Tszhang Guo, Shiyu Chang, Mo Yu, Kun Bai

Recently, Reinforcement Learning (RL) approaches have demonstrated advanced performance in image captioning by directly optimizing the metric used for testing.

Image Captioning reinforcement-learning

Deriving Machine Attention from Human Rationales

3 code implementations EMNLP 2018 Yujia Bao, Shiyu Chang, Mo Yu, Regina Barzilay

Attention-based models are successful when trained on large amounts of data.

Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

1 code implementation11 Aug 2018 Wei Tan, Shiyu Chang, Liana Fong, Cheng Li, Zijun Wang, Liangliang Cao

Current MF implementations are either optimized for a single machine or with a need of a large computer cluster but still are insufficient.

Collaborative Filtering Data Compression

Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents

3 code implementations16 Jun 2018 Wenhan Xiong, Xiaoxiao Guo, Mo Yu, Shiyu Chang, Bo-Wen Zhou, William Yang Wang

We investigate the task of learning to follow natural language instructions by jointly reasoning with visual observations and language inputs.

Efficient Exploration reinforcement-learning

A Co-Matching Model for Multi-choice Reading Comprehension

1 code implementation ACL 2018 Shuohang Wang, Mo Yu, Shiyu Chang, Jing Jiang

Multi-choice reading comprehension is a challenging task, which involves the matching between a passage and a question-answer pair.

Reading Comprehension

Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization

1 code implementation NeurIPS 2018 Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen, Pai-Shun Ting, Shiyu Chang, Lisa Amini

As application demands for zeroth-order (gradient-free) optimization accelerate, the need for variance reduced and faster converging approaches is also intensifying.

Material Classification Stochastic Optimization

Deep Learning Based Speech Beamforming

no code implementations15 Feb 2018 Kaizhi Qian, Yang Zhang, Shiyu Chang, Xuesong Yang, Dinei Florencio, Mark Hasegawa-Johnson

On the other hand, deep learning based enhancement approaches are able to learn complicated speech distributions and perform efficient inference, but they are unable to deal with variable number of input channels.

Speech Enhancement

Faster Reinforcement Learning with Expert State Sequences

no code implementations ICLR 2018 Xiaoxiao Guo, Shiyu Chang, Mo Yu, Miao Liu, Gerald Tesauro

In this paper, we consider a realistic and more difficult sce- nario where a reinforcement learning agent only has access to the state sequences of an expert, while the expert actions are not available.

Imitation Learning reinforcement-learning

Dilated Recurrent Neural Networks

2 code implementations NeurIPS 2017 Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael Witbrock, Mark Hasegawa-Johnson, Thomas S. Huang

To provide a theory-based quantification of the architecture's advantages, we introduce a memory capacity measure, the mean recurrent length, which is more suitable for RNNs with long skip connections than existing measures.

Sequential Image Classification

Robust Video Super-Resolution With Learned Temporal Dynamics

no code implementations ICCV 2017 Ding Liu, Zhaowen Wang, Yuchen Fan, Xian-Ming Liu, Zhangyang Wang, Shiyu Chang, Thomas Huang

Second, we reduce the complexity of motion between neighboring frames using a spatial alignment network that is much more robust and efficient than competing alignment methods and can be jointly trained with the temporal adaptive network in an end-to-end manner.

Frame Video Super-Resolution

Robust Task Clustering for Deep Many-Task Learning

no code implementations26 Aug 2017 Mo Yu, Xiaoxiao Guo, Jin-Feng Yi, Shiyu Chang, Saloni Potdar, Gerald Tesauro, Haoyu Wang, Bo-Wen Zhou

We propose a new method to measure task similarities with cross-task transfer performance matrix for the deep learning scenario.

Few-Shot Learning General Classification +4

Fast Wavenet Generation Algorithm

6 code implementations29 Nov 2016 Tom Le Paine, Pooya Khorrami, Shiyu Chang, Yang Zhang, Prajit Ramachandran, Mark A. Hasegawa-Johnson, Thomas S. Huang

This paper presents an efficient implementation of the Wavenet generation process called Fast Wavenet.

Stacked Approximated Regression Machine: A Simple Deep Learning Approach

no code implementations14 Aug 2016 Zhangyang Wang, Shiyu Chang, Qing Ling, Shuai Huang, Xia Hu, Honghui Shi, Thomas S. Huang

With the agreement of my coauthors, I Zhangyang Wang would like to withdraw the manuscript "Stacked Approximated Regression Machine: A Simple Deep Learning Approach".

Streaming Recommender Systems

no code implementations21 Jul 2016 Shiyu Chang, Yang Zhang, Jiliang Tang, Dawei Yin, Yi Chang, Mark A. Hasegawa-Johnson, Thomas S. Huang

The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios.

Recommendation Systems

D3: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images

no code implementations CVPR 2016 Zhangyang Wang, Ding Liu, Shiyu Chang, Qing Ling, Yingzhen Yang, Thomas S. Huang

In this paper, we design a Deep Dual-Domain (D3) based fast restoration model to remove artifacts of JPEG compressed images.

Learning A Deep $\ell_\infty$ Encoder for Hashing

no code implementations6 Apr 2016 Zhangyang Wang, Yingzhen Yang, Shiyu Chang, Qing Ling, Thomas S. Huang

We investigate the $\ell_\infty$-constrained representation which demonstrates robustness to quantization errors, utilizing the tool of deep learning.


$\mathbf{D^3}$: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images

no code implementations16 Jan 2016 Zhangyang Wang, Ding Liu, Shiyu Chang, Qing Ling, Yingzhen Yang, Thomas S. Huang

In this paper, we design a Deep Dual-Domain ($\mathbf{D^3}$) based fast restoration model to remove artifacts of JPEG compressed images.

Learning A Task-Specific Deep Architecture For Clustering

no code implementations1 Sep 2015 Zhangyang Wang, Shiyu Chang, Jiayu Zhou, Meng Wang, Thomas S. Huang

In this paper, we propose to emulate the sparse coding-based clustering pipeline in the context of deep learning, leading to a carefully crafted deep model benefiting from both.

Learning Super-Resolution Jointly from External and Internal Examples

no code implementations3 Mar 2015 Zhangyang Wang, Yingzhen Yang, Zhaowen Wang, Shiyu Chang, Jianchao Yang, Thomas S. Huang

Single image super-resolution (SR) aims to estimate a high-resolution (HR) image from a lowresolution (LR) input.

Image Super-Resolution

Scalable Similarity Learning using Large Margin Neighborhood Embedding

no code implementations24 Apr 2014 Zhaowen Wang, Jianchao Yang, Zhe Lin, Jonathan Brandt, Shiyu Chang, Thomas Huang

In this paper, we present an image similarity learning method that can scale well in both the number of images and the dimensionality of image descriptors.

Metric Learning

Learning Locally-Adaptive Decision Functions for Person Verification

no code implementations CVPR 2013 Zhen Li, Shiyu Chang, Feng Liang, Thomas S. Huang, Liangliang Cao, John R. Smith

This paper proposes to learn a decision function for verification that can be viewed as a joint model of a distance metric and a locally adaptive thresholding rule.

Face Verification Metric Learning +1

Blind Image Deblurring by Spectral Properties of Convolution Operators

no code implementations10 Sep 2012 Guangcan Liu, Shiyu Chang, Yi Ma

We show that the minimizer of this regularizer guarantees to give good approximation to the blur kernel if the original image is sharp enough.

Blind Image Deblurring Image Deblurring

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