Selected MIT-IBM Watson AI Lab Publications

The publications listed below are co-authored by MIT and IBM researchers.

 

Association for the Advancement of Artificial Intelligence (AAAI) 2020

Towards Certificated Model Robustness Against Weight Perturbations
Jeet Mohapatra (MIT), Lily Weng (MIT), Pin-Yu Chen, Sijia Liu, Luca Daniel (MIT)
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs 
Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler (MIT), Tao Schardl (MIT); Charles E. Leisersen (MIT)

Artificial Intelligence, Ethics, and Society (AIES) 2020

Learning Occupational Task-Shares Dynamics for the Future of Work
Subhro Das, Sebastian Steffen (MIT), Wyatt Clarke, Prabhat Reddy, Erik Brynjolfsson (MIT), Martin Fleming

Artificial Intelligence and Statistics (AISTATS) 2020

Characterization of Overlap in Observational Studies
Fredrik D. Johansson (MIT), Dennis Wei, Michael Oberst (MIT), Tian Gao, Gabriel Brat, David Sontag (MIT), Kush Varshney

Computer Vision and Pattern Recognition (CVPR) 2020

Relation Guided Knowledge Transfer for Incremental Learning of Object Detectors
Kandan Ramakrishnan, Rameswar Panda, Quanfu Fan, John Henning, Aude Oliva (MIT), Rogerio Feris 
Towards Verifying Robustness of Neural Networks against Semantic Perturbations
Jeet Mohapatra (MIT), Lily Weng (MIT), Pin-Yu Chen, Sijia Liu, Luca Daniel (MIT)
Music Gesture for Visual Sound Separation
Chuang Gan, Deng Huang, Hang Zhao (MIT), Joshua Tenenbaum (MIT), Antonio Torralba (MIT)

International Conference on Learning Representations (ICLR) 2020

Few-shot Text Classification With Distributional Signatures
Yujia Bao (MIT), Menghua Wu (MIT), Shiyu Chang, Regina Barzilay (MIT)
Deep Audio Priors Emerge From Harmonic Convolutional Networks
Zhoutong Zhang (MIT), Yunyun Wang (MIT), Chuang Gan, Jiajun Wu (MIT), Joshua Tenenbaum (MIT), Antonio Torralba (MIT), William T. Freeman (MIT)
Once For All: Train One Network And Specialize It For Efficient Deployment
Han Cai (MIT), Chuang Gan, Tianzhe Wang (MIT), Zhekai Zhang (MIT), Song Han (MIT)
CLEVRER: Collision Events For Video Representation And Reasoning
Kexin Yi, Chuang Gan, Yunzhu Li (MIT), Pushmeet Kohli, Jiajun Wu (MIT), Antonio Torralba (MIT), Joshua Tenenbaum (MIT)

International Conference on Machine Learning (ICML) 2020

Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks
Sijia Liu, Songtao Lu, Xiangyi Chen, Yao Feng, Kaidi Xu, Abdullah Al-Dujaili (MIT), Mingyi Hong, Una-May O’Reilly (MIT)
Proper Network Interpretability Helps Adversarial Robustness in Classification
Akhilan Boopathy (MIT), Sijia Liu, Gaoyuan Zhang, Cynthia Liu (MIT), Pin-Yu Chen, Shiyu Chang, Luca Daniel (MIT)
Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion
Nghia Hoang, Thanh Lam, Bryan Kian Hsiang Low, Patrick Jaillet (MIT)
Invariant Rationalization
Shiyu Chang, Yang Zhang, Mo Yu, Tommi Jaakkola (MIT)
Model Fusion with Kullback–Leibler Divergence
Sebastian Claici (MIT), Mikhail Yurochkin, Soumya Ghosh, Justin Solomon (MIT)

 

Non-Conference Papers and Refereed Journal Publications 2020

Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding.
Mathew Monfort (MIT), Kandan Ramakrishnan, Alex Andonian (MIT), Barry McNamara (MIT), Alex Lascelles (MIT), Bowen Pan (MIT), Quanfu Fan, Dan Gutfreund, Rogerio Feris, Aude Oliva (MIT)
IEEE PAMI
Moments in Time Dataset: One Million Videos for Event Understanding
Mathew Monfort (MIT), Alex Andonian (MIT), Bolei Zhou, Kandan Ramakrishnan (MIT), Sarah Adel Bargal, Tom Yan (MIT), Lisa Brown, Quanfu Fan, Dan Gutfreund, Carl Vondrick, Aude Oliva (MIT)
IEEE PAMI 
Experiences and Insights for Collaborative Industry-Academic Research in Artificial Intelligence
Lisa Amini, Ching-Hua Chen, David Cox, Aude Oliva (MIT), Antonio Torralba (MIT)
AI Magazine 
Artificial Intelligence Method To Design And Fold Alpha-helical Structural Proteins From The Primary Amino Acid Sequence
Zhao Qin (MIT), Lingfei Wu, Hui Sun (MIT), Siyu Huo, Tengfei Ma, Eugene Lim (MIT), Pin-Yu Chen, Benedetto Marelli (MIT), Markus J. Buehler (MIT)
Extreme Mechanics Letters 
1515: Should Diuretic Initiation Be Delayed In Icu Patients With Recent Vasopressor Use? A Causal Analysis
Somnath Bose, Li-wei Lehman (MIT), Kechun Huang (MIT), Daniel Talmor, Zach Shahn
Critical Care Medicine 48, no. 1: 733 
Fluid-limiting treatment strategies among sepsis patients in the ICU: a retrospective causal analysis.
Zach Shahn, Nathan Shapiro, Patrick Tyler, Daniel Talmor, Li-wei Lehman (MIT)
Critical Care Medicine 24, 62
G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes.
Rui Li (MIT), Zach Shahn, Jun Li (MIT), Mingyu Lu (MIT), Prithwish Chakraborty, Daby Sow, Mohamed F. Ghalwash, Li-Wei Lehman (MIT)
CoRR abs/2003.10551
Estimating Differential Entropy under Gaussian Convolutions
Jonathan Weed, Kristjan Greenewald (MIT-IBM Lab), Ziv Goldfeld, Yury Polyanskiy (MIT)
IEEE Transactions on Information Theory
Alloying conducting channels for reliable neuromorphic computing
Hanwool Yeon (MIT), Peng Lin (MIT), Chanyeol Choi (MIT), Scott H. Tan (MIT), Yongmo Park (MIT), Doyoon Lee (MIT), Jaeyong Lee (MIT), Feng Xu, Bin Gao, Huaquiang Wu, He Qian, Yifan Nie, Seyoung Kim, Jeehwan Kim (MIT)
Nature Nanotechnology 

Association for the Advancement of Artificial Intelligence (AAAI) 2019

Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems
Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, Jonathan How (MIT)
Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation
Lijie Fan (MIT), Wenbing Huang, Chuang Gan, Junzhou Huang, Boqing Gong
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
Akhilan Boopathy (MIT), Tsui-Wei Weng (MIT), Pin-Yu Chen, Sijia Liu, Luca Daniel (MIT)
Learning to Teach in Cooperative Multiagent Reinforcement Learning
Shayegan Omidshafiei (MIT), Dong-Ki Kim (MIT), Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How (MIT)
MIT News Article

Artificial Intelligence and Statistics (AISTATS) 2019

ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
Raj Agrawal (MIT), Karthikeyan Shanmugam (MIT-IBM Lab), Chandler Squires (MIT), Caroline Uhler (MIT), Karren Yang (MIT)
Chordal Causal Structure Learning
Kristjan Greenewald (MIT-IBM Lab), Murat Kocaoglu (IBM), Sara Magliacane (IBM), Chandler Squires (MIT), Guy Bresler (MIT)
Size of Interventional Markov Equivalence Classes in Random DAG Models
Dmitiry Katz (MIT-IBM Lab), Karthikeyan Shanmugam (MIT-IBM Lab), Chandler Squires (MIT), Caroline Uhler (MIT)

 

Cognitive Computational Neuroscience (CCN) 2019

A Computational Model for Combinatorial Generalization in Physical Auditory Perception
Yunyun Wang (MIT), Chuang Gan (MIT-IBM Lab), Max H. Siegel (MIT), Zhoutong Zhang (MIT), Jiajun Wu (MIT), Joshua B. Tenenbaum (MIT)

 

Computer Vision and Pattern Recognition (CVPR) 2019

Identifying Interpretable Action Concepts in Deep Networks
Kandan Ramakrishnan (MIT), Mathew Monfort (MIT), Barry A McNamara (MIT), Alex Lascelles (MIT), Aude Oliva (MIT), Dan Gutfreund (MIT-IBM Lab), Rogerio Feris (MIT-IBM Lab)
Grounding Spoken Words in Unlabeled Video
Angie Boggust (MIT), Kartik Audhkhasi (IBM), Dhiraj Joshi (IBM), David Harwath (MIT), Samuel Thomas (IBM), Rogerio Feris (IBM), Dan Gutfreund (IBM), Yang Zhang (IBM), Antonio Torralba (MIT), Michael Picheny (IBM), James Glass (MIT)

 

Empirical Methods in Natural Language Processing (EMNLP) 2019

Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control
Mo Yu (IBM), Shiyu Chang (IBM), Yang Zhang (IBM), Tommi Jaakkola (MIT)

 

Gerontological Society of America Annual Scientific Meeting (GSA) 2019

Exclusion Spotter: applying advances in AI to identify ageism in online job posting
Nicola Palmarini (MIT-IBM Lab, IBM), Lee Martie (IBM), Mattie Wasiak (MIT), Gaoyuan Zhang (IBM)

International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2019

Self-supervised Audio-visual Co-segmentation
Andrew Rouditchenko (MIT), Hang Zhao (MIT), Chuang Gan (MIT-IBM Lab), Josh McDermott (MIT), Antonio Torralba (MIT)

International Conference on Computer Vision (ICCV) 2019

TSM: Temporal Shift Module for Efficient Video Understanding
Ji Lin (MIT), Chuang Gan (MIT-IBM Lab), Song Han (MIT)
The Sound of Motions
Hang Zhao (MIT), Chuang Gan (MIT-IBM Lab), Wei-Chiu Ma (MIT), Antonio Torralba (MIT)
MIT News Article
Seeing What a GAN Cannot Generate
David Bau (MIT), Jun-Yan Zhu (MIT), Jonas Wulff (MIT), William Peebles (MIT), Hendrik Strobelt (IBM), Bolei Zhou, Antonio Torralba (MIT)
Reasoning about Human-Object Interactions through Dual Attention Networks
Tete Xiao (IBM), Quanfu Fan (MIT-IBM Lab), Danny Gutfreund (MIT-IBM Lab), Bolei Zhou, Matthew Monfort (MIT), Aude Oliva (MIT)
Self-supervised Moving Vehicle Tracking with Stereo Sound
Chuang Gan (MIT-IBM Lab, IBM), Hang Zhao (MIT), Peihao Chen (IBM), David Cox (MIT-IBM Lab, IBM), Antonio Torralba (MIT)

International Conference on Data Mining (ICDM) 2019

Generative Oversampling with a Contrastive Variational Autoencoder
Wangzhi Dai (MIT), Kenney Ng (IBM), Kristen Severson (IBM), Wei Huang, Fred Anderson, Collin Stultz (MIT)

International Conference on Learning Representations (ICLR) 2019

Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference
Matthew Riemer (MIT-IBM Lab), Ignacio Cases, Robert Ajemian (MIT), Miao Liu (MIT-IBM Lab), Irina Rish (MIT-IBM Lab), Yuhai Tu (MIT-IBM Lab), and Gerald Tesauro (MIT-IBM Lab)
ZDNet Article
Defensive Quantization: When Efficiency Meets Robustness
Ji Lin (MIT), Chuang Gan (MIT-IBM Lab), Song Han (MIT)
MIT News Article 
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
David Bau (MIT), Jun-Yan Zhu (MIT), Hendrik Strobelt (MIT-IBM Lab), Bolei Zhou, Joshua B. Tenenbaum (MIT), William T. Freeman (MIT), Antonio Torralba (MIT)
MIT Tech Review Article 
Learning Entropic Wasserstein Embeddings
Charlie Frogner (MIT), Farzaneh Mirzazadeh (MIT-IBM Lab), Justin Solomon (MIT)
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
Jiayuan Mao (MIT), Chuang Gan (MIT-IBM Lab), Pushmeet Kohli, Joshua B. Tenenbaum (MIT), Jiajun Wu (MIT)
MIT News Article
MIT Tech Review Article

 

International Conference on Machine Learning (ICML) 2019

PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
Tsui-Wei Weng (MIT), Pin-Yu Chen (IBM), Lam M. Nguye (IBM), Mark S. Squillante (IBM), Ivan Oseledets, Luca Daniel (MIT)
Scalable Fair Clustering
Arturs Backurs, Piotr Indyk (MIT), Krzysztof Onak (MIT-IBM Lab), Baruch Schieber, Ali Vakilian (MIT), Tal Wagner(MIT)
Estimating Information Flow in Neural Networks
Ziv Goldfeld (MIT), Ewout van den Berg (MIT-IBM Lab), Kristjan Greenewald (MIT-IBM Lab), Igor Melnyk (MIT-IBM Lab), Nam Nguyen (MIT-IBM Lab), Brian Kingsbury (MIT-IBM Lab), Yury Polyanskiy (MIT)
Few-Shot Transfer Learning from Multiple Pre-Trained Networks
Joshua Lee (MIT), Prasanna Sattigeri (IBM), Gregory Wornell (MIT)

 

International Joint Conference on Artificial Intelligence (IJCAI) 2019

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Aldo Pareja (MIT-IBM Lab), Giacomo Domeniconi (MIT-IBM Lab), Jie Chen (MIT-IBM Lab), Tengfei Ma (MIT-IBM Lab), Toyotaro Suzumura (MIT-IBM Lab), Hiroki Kanezashi (MIT-IBM Lab), Tim Kaler (MIT),Tao B. Schardl (MIT), Charles E. Leiserson (MIT)
Bayesian Inference of Linear Temporal Logic Specifications for Contrastive Explanations
Joseph Kim (MIT), Christian Muise (MIT-IBM Lab), Ankit Shah (MIT), Shubham Agarwal (MIT-IBM Lab), Julie Shah (MIT)
Evaluating the Interpretability of the Knowledge Compilation Map: Communicating Logical Statements Effectively
Serena Booth (MIT), Christian Muise (MIT-IBM Lab), Julie Shah (MIT)

 

IEEE International Symposium on Information Theory (ISIT) 2019

Convergence of Smoothed Empirical Measures with Applications to Entropy Estimation
Yury Polyanski (MIT), Ziv Goldfeld (MIT), Jonathan Weed (MIT), Kristjan Greenewald (MIT-IBM Lab)
Optimality of the Plug-in Estimator for Differential Entropy Estimation under Gaussian Convolutions
Ziv Goldfeld (MIT), Kristjan Greenewald (IBM), Jonathan Weed (MIT), Yury Polyanskiy (MIT)

 

Conference on Knowledge Discovery and Data Mining (KDD) 2019

Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics
Jie Chen (MIT-IBM Lab), Charles E. Leisersen (MIT), Daniel Karl I. Weidele (IBM), Giacomo Domeniconi (IBM), Mark Weber (MIT-IBM Lab), Claudio Bellei, Tom Robinson
Retaining Privileged Information for Multi-Task Learning
Fengyi Tang, Cao Xiao (IBM), Fei Wang, Jiayu Zhou, Li-Wei Lehman (MIT, MIT-IBM Lab)

 

North American Chapter of the Association for Computational Linguistics (NAACL) 2019

Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
Ethan Wilcox, Peng Qian (MIT), Richard Futrell, Miguel Ballesteros (MIT-IBM Lab), Roger Levy (MIT)
MIT News Article
Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State
Richard Futrell, Ethan Wilcox, Takashi Morita (MIT), Peng Qian (MIT), Miguel Ballesteros (MIT-IBM Lab), Roger Levy (MIT)
MIT News Article

 

Neural Information Processing Systems (NeurIPS) 2019

A Stratified Approach to Robustness for Randomly Smoothed Classifiers
Guang-He Lee (MIT), Yang Yuan (MIT), Shiyu Chang (IBM), Tommi Jaakkola (MIT)
A Game Theoretic Approach to Class-wise Selective Rationalization
Shiyu Chang (IBM), Yang Zhang (MIT-IBM Lab), Mo Yu (IBM), Tommi Jaakkola (MIT)
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks
Joshua Lee (MIT), Prasanna Sattigeri (IBM), Gregory Wornell (MIT)
Hierarchical Optimal Transport for Document Representation
Mikhail Yurochkin (MIT-IBM Lab), Sebastian Claici (MIT), Edward Chien (MIT), Farzaneh Mirzazadeh (MIT-IBM Lab), Justin M Solomon (MIT)
Alleviating Label Switching with Optimal Transport
Pierre Monteiller, Sebastian Claici (MIT), Edward Chien (MIT), Farzaneh Mirzazadeh (MIT-IBM Lab), Justin M Solomon (MIT), Mikhail Yurochkin (MIT-IBM Lab)
Sample Efficient Active Learning of Causal Trees
Kristjan Greenewald (IBM), Dmitriy Katz (IBM), Karthikeyan Shanmugam (IBM), Sara Magliacane (MIT-IBM Lab), Murat Kocaoglu (MIT-IBM Lab), Enric Boix Adsera (MIT), Guy Bresler (MIT)
Visual Concept-Metaconcept Learning
Chi Han, Jiayuan Mao (MIT), Chuang Gan (MIT-IBM Lab), Josh Tenenbaum (MIT), Jiajun Wu (MIT)
Objectnet: A Large-scale Bias-controlled Dataset for Pushing the Limits of Object Recognition Models
Andrei Barbu (MIT), David Mayo (MIT), Julian Alverio (MIT), William Luo (MIT), Christopher Wang (MIT), Dan Gutfreund (IBM), Josh Tenenbaum (MIT), Boris Katz (MIT)

 

ACM Special Interest Group on Computer Graphics (SIGGRAPH) 2019

GANPaint Studio: Semantic Photo Manipulation with a Generative Image Prior
David Bau (MIT), Hendrik Strobelt (MIT-IBM Lab), William Peebles (MIT), Jonas Wulff (MIT), Bolei Zhou, Jun-Yan Zhu (MIT), Antonio Torralba (MIT)

 

International Conference on Solid State Ionics (SSI) 2019

Controlling Conductive Filaments in Resistive Switching Oxides by Controlled Chemical Disorder
Kevin J. May (MIT), Yu Ren Zhou (MIT), Takashi Ando(c), Vijay Narayanan (IBM), Harry L. Tuller (IBM), Bilge Yildiz (MIT)

Non-Conference Papers and Refereed Journal Publications 2019

Moments in Time Dataset: One Million Videos for Event Understanding
Mathew Monfort (MIT), Alex Andonian (MIT), Bolei Zhou, Kandan Ramakrishnan (MIT), Sarah Adel Bargal, Tom Yan (MIT), Lisa Brown (IBM), Quanfu Fan (IBM), Dan Gutfruend (IBM), Carl Vondrick, Aude Oliva (MIT)
IEEE PAMI
Experiences and Insights for Collaborative Industry-Academic Research in Artificial Intelligence
Lisa Amini (IBM), Ching-Hua Chen (IBM), David Cox (IBM), Aude Oliva (MIT-IBM Lab), Antonio Torralba (MIT-IBM Lab)
AI Magazine
“Inactive” Ingredients in Oral Medications
Daniel Reker (MIT-IBM Lab), Steven M. Blum (MIT), Christoph Steiger (MIT-IBM Lab), Kevin E. Anger, Jamie M. Sommer, John Fanikos, Giovanni Traverso (MIT-IBM Lab)
Science Translational Medicine
Supervised Learning With Quantum-enhanced Feature Spaces 
Vojtěch Havlíček (IBM), Antonio D. Córcoles (IBM), Kristan Temme (IBM), Aram W. Harrow (MIT), Abhinav Kandala (IBM), Jerry M. Chow (IBM), Jay M. Gambetta (IBM)
Nature 567, 209–212
Semantic photo manipulation with a generative image prior
David Bau (MIT), Hendrik Strobelt (IBM), William Peebles (MIT), Jonas Wulff (MIT), Bolei Zhou, Jun-Yan Zhu (MIT), Antonio Torralba (MIT)
ACM Transactions on Graphics (TOG) Vol. 38 (4) pp. 1-11
Hamiltonian engineering with constrained optimization for quantum sensing and control
Michael  O’Keeffe (MIT, MIT-IBM Lab), Lior Horesh (IBM, MIT-IBM Lab), John Barry (MIT), Danielle Braje (MIT, MIT-IBM Lab), Isaac Chuang (MIT, MIT-IBM Lab)
New Journal of Physics, 21 (2019) 023015

 

Association for Computational Linguistics (ACL) 2018

Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning
Hongge Chen (MIT), Huan Zhang (IBM), Pin-Yu Chen (IBM), Jinfeng Yi, Cho-Jui Hsieh

 

Computer Vision and Pattern Recognition (CVPR) 2018

Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification
Xiang Long, Chuang Gan (MIT-IBM Lab), Gerard de Melo, Jiajun Wu (MIT), Xiao Liu, Shilei Wen
Sparse, Smart Contours to Represent and Edit Images
Tali Dekel, Chuang Gan (MIT-IBM Lab), Dilip Krishnan, Ce Liu, William T. Freeman (MIT)

 

European Conference on Computer Vision (ECCV) 2018

The Sound of Pixels
Hang Zhao (MIT), Chuang Gan (MIT-IBM Lab), Andrew Rouditchenko (MIT), Carl Vondrick, Josh McDermott (MIT), Antonio Torralba (MIT)
MIT News Article

 

Empirical Methods in Natural Language Processing (EMNLP) 2018

Deriving Machine Attention from Human Rationales
Yujia Bao (MIT), Shiyu Chang (MIT-IBM Lab), Mo Yu (MIT-IBM Lab), Regina Barzilay (MIT)

 

International Conference on Healthcare Informatics (ICHI) 2018

Predicting and Understanding Unexpected Respiratory Decompensation in Critical Care Using Sparse and Heterogeneous Clinical Data
Oliver Ren (MIT), Alistair E. W. Johnson (MIT), Eric P. Lehman, Matthieu Komorowski, Jerome Aboab (MIT), Fengyi Tang (MIT), Zach Shahn (IBM), Daby Sow (MIT), Roger G. Mark (MIT), Li-wei H. Lehman (MIT)

 

International Conference on Learning Representations (ICLR) 2018

A Case Study on Neural Image Captioning
Tsui-Wei Weng (MIT), Huan Zhang, Pin-Yu Chen (MIT-IBM Lab), Jinfeng Yi, Dong Su (MIT-IBM Lab), Yupeng Gao (MIT-IBM Lab), Cho-Jui Hsieh, Luca Daniel (MIT)

International Conference on Machine Learning (ICML) 2018

Logical Rule Induction and Theory Learning Using Neural Theorem Proving
Andres Campero (MIT), Josh Tenenbaum (MIT), Aldo Pareja (MIT-IBM Lab), Tim Klinger (MIT-IBM Lab), Sebastian Riedel

 

International Conference on the Science of Electrical Engineering (ICSEE) 2018

Differential entropy estimation under Gaussian Noise
Ziv Goldfeld (MIT),Kristjan Greenewald (IBM),Yury Polyanskiy (MIT), Yihong Wu

 

Neural Information Processing Systems (NeurIPS) 2018

Scalable Graph Learning for Anti-Money Laundering: A First Look
Mark Weber, Jie Chen, Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E. Leiserson, Tao B. Schardl
NeurIPS Finance Workshop
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
Kexin Yi, Jiajun Wu (MIT), Chuang Gan (MIT-IBM Lab), Antonio Torralba (MIT), Pushmeet Kohli, Joshua B. Tenenbaum (MIT)
Efficient Neural Network Robustness Certification with General Activation Functions
Huan Zhang, Tsui-Wei Weng (MIT), Pin-Yu Chen (MIT-IBM Lab), Cho-Jui Hsieh, Luca Daniel (MIT)
Co-regularized Alignment for Unsupervised Domain Adaptation
Abhishek Kumar (MIT-IBM Lab), Prasanna Sattigeri (MIT-IBM Lab), Kahini Wadhawan (MIT-IBM Lab), Leonid Karlinsky (MIT-IBM Lab), Rogerio Feris (MIT-IBM Lab), William T. Freeman (MIT), Gregory Wornell (MIT)
Continual Learning with Self-Organizing Maps
Pouya Bashivan (MIT), Martin Schrimpf (MIT), Robert Ajemian (MIT), Irina Rish (MIT-IBM Lab), Matthew Riemer (MIT-IBM Lab), Yuhai Tu (MIT-IBM Lab)