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The Massachusetts Institute of Technology is a private research university in Cambridge, Massachusetts.

The Computational Limits of Deep Learning

Deep learning's recent history has been one of achievement: from triumph...
07/10/2020 ∙ by Neil C. Thompson, et al. ∙ 378 ∙ share

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Understanding Human Judgments of Causality

Discriminating between causality and correlation is a major problem in m...
12/19/2019 ∙ by Masahiro Kazama, et al. ∙ 100 ∙ share

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BIKED: A Dataset and Machine Learning Benchmarks for Data-Driven Bicycle Design

In this paper, we present "BIKED," a dataset comprised of 4500 individua...
03/10/2021 ∙ by Lyle Regenwetter, et al. ∙ 87 ∙ share

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The Synthesizability of Molecules Proposed by Generative Models

The discovery of functional molecules is an expensive and time-consuming...
02/17/2020 ∙ by Wenhao Gao, et al. ∙ 83 ∙ share

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Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization

While deep learning is successful in a number of applications, it is not...
08/25/2019 ∙ by Tomaso Poggio, et al. ∙ 82 ∙ share

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Learning to Localize Sound Sources in Visual Scenes: Analysis and Applications

Visual events are usually accompanied by sounds in our daily lives. Howe...
11/20/2019 ∙ by Arda Senocak, et al. ∙ 65 ∙ share

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TransCenter: Transformers with Dense Queries for Multiple-Object Tracking

Transformer networks have proven extremely powerful for a wide variety o...
03/28/2021 ∙ by Yihong Xu, et al. ∙ 52 ∙ share

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Interpretable Neuroevolutionary Models for Learning Non-Differentiable Functions and Programs

A key factor in the modern success of deep learning is the astonishing e...
07/16/2020 ∙ by Allan Costa, et al. ∙ 50 ∙ share

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Generalized Maximum Causal Entropy for Inverse Reinforcement Learning

We consider the problem of learning from demonstrated trajectories with ...
11/16/2019 ∙ by Tien Mai, et al. ∙ 44 ∙ share

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A Probe into Understanding GAN and VAE models

Both generative adversarial network models and variational autoencoders ...
12/13/2018 ∙ by Jingzhao Zhang, et al. ∙ 38 ∙ share

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Data augmentation using learned transforms for one-shot medical image segmentation

Biomedical image segmentation is an important task in many medical appli...
02/25/2019 ∙ by Amy Zhao, et al. ∙ 33 ∙ share

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A Theory of Uncertainty Variables for State Estimation and Inference

Probability theory forms an overarching framework for modeling uncertain...
09/24/2019 ∙ by Rajat Talak, et al. ∙ 32 ∙ share

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Survey of Attacks and Defenses on Edge-Deployed Neural Networks

Deep Neural Network (DNN) workloads are quickly moving from datacenters ...
11/27/2019 ∙ by Mihailo Isakov, et al. ∙ 30 ∙ share

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Deep Learning Meets Projective Clustering

A common approach for compressing NLP networks is to encode the embeddin...
10/08/2020 ∙ by Alaa Maalouf, et al. ∙ 30 ∙ share

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Avoidance Learning Using Observational Reinforcement Learning

Imitation learning seeks to learn an expert policy from sampled demonstr...
09/24/2019 ∙ by David Venuto, et al. ∙ 29 ∙ share

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DiffTaichi: Differentiable Programming for Physical Simulation

We study the problem of learning and optimizing through physical simulat...
10/01/2019 ∙ by Yuanming Hu, et al. ∙ 29 ∙ share

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Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping

We provide an open-source C++ library for real-time metric-semantic visu...
10/06/2019 ∙ by Antoni Rosinol, et al. ∙ 28 ∙ share

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Compositional Explanations of Neurons

We describe a procedure for explaining neurons in deep representations b...
06/24/2020 ∙ by Jesse Mu, et al. ∙ 28 ∙ share

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Interpretable and synergistic deep learning for visual explanation and statistical estimations of segmentation of disease features from medical images

Deep learning (DL) models for disease classification or segmentation fro...
11/11/2020 ∙ by Sambuddha Ghosal, et al. ∙ 27 ∙ share

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Continual Learning with Self-Organizing Maps

Despite remarkable successes achieved by modern neural networks in a wid...
04/19/2019 ∙ by Pouya Bashivan, et al. ∙ 26 ∙ share

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Inverse Reinforcement Learning with Missing Data

We consider the problem of recovering an expert's reward function with i...
11/16/2019 ∙ by Tien Mai, et al. ∙ 26 ∙ share

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Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization

Domain generalization is the problem of machine learning when the traini...
02/13/2020 ∙ by Vikas K. Garg, et al. ∙ 26 ∙ share

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Uncertainty Quantification Using Neural Networks for Molecular Property Prediction

Uncertainty quantification (UQ) is an important component of molecular p...
05/20/2020 ∙ by Lior Hirschfeld, et al. ∙ 25 ∙ share

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Targeting for long-term outcomes

Decision-makers often want to target interventions (e.g., marketing camp...
10/29/2020 ∙ by Jeremy Yang, et al. ∙ 25 ∙ share

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3D Traffic Simulation for Autonomous Vehicles in Unity and Python

Over the recent years, there has been an explosion of studies on autonom...
10/30/2018 ∙ by Zhijing Jin, et al. ∙ 24 ∙ share

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On Fast Leverage Score Sampling and Optimal Learning

Leverage score sampling provides an appealing way to perform approximate...
10/31/2018 ∙ by Alessandro Rudi, et al. ∙ 24 ∙ share

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Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling

We consider the problem of inferring the values of an arbitrary set of v...
02/06/2019 ∙ by Hao Wang, et al. ∙ 24 ∙ share

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Are Transformers universal approximators of sequence-to-sequence functions?

Despite the widespread adoption of Transformer models for NLP tasks, the...
12/20/2019 ∙ by Chulhee Yun, et al. ∙ 24 ∙ share

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How do Data Science Workers Collaborate? Roles, Workflows, and Tools

Today, the prominence of data science within organizations has given ris...
01/18/2020 ∙ by Amy X. Zhang, et al. ∙ 24 ∙ share

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Federated Visual Classification with Real-World Data Distribution

Federated Learning enables visual models to be trained on-device, bringi...
03/18/2020 ∙ by Tzu Ming Harry Hsu, et al. ∙ 24 ∙ share

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Adversarial Genetic Programming for Cyber Security: A Rising Application Domain Where GP Matters

Cyber security adversaries and engagements are ubiquitous and ceaseless....
04/07/2020 ∙ by Una-May O'Reilly, et al. ∙ 24 ∙ share

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HAT: Hardware-Aware Transformers for Efficient Natural Language Processing

Transformers are ubiquitous in Natural Language Processing (NLP) tasks, ...
05/28/2020 ∙ by Hanrui Wang, et al. ∙ 24 ∙ share

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Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning

Learning robotic manipulation tasks using reinforcement learning with sp...
12/23/2019 ∙ by Richard Li, et al. ∙ 23 ∙ share

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Anycost GANs for Interactive Image Synthesis and Editing

Generative adversarial networks (GANs) have enabled photorealistic image...
03/04/2021 ∙ by Ji Lin, et al. ∙ 23 ∙ share

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Unsupervised Image Transformation Learning via Generative Adversarial Networks

In this work, we study the image transformation problem by learning the ...
03/13/2021 ∙ by Kaiwen Zha, et al. ∙ 23 ∙ share

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The Limitations of Adversarial Training and the Blind-Spot Attack

The adversarial training procedure proposed by Madry et al. (2018) is on...
01/15/2019 ∙ by Huan Zhang, et al. ∙ 22 ∙ share

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Speech2Face: Learning the Face Behind a Voice

How much can we infer about a person's looks from the way they speak? In...
05/23/2019 ∙ by Tae-Hyun Oh, et al. ∙ 22 ∙ share

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Learning with AMIGo: Adversarially Motivated Intrinsic Goals

A key challenge for reinforcement learning (RL) consists of learning in ...
06/22/2020 ∙ by Andres Campero, et al. ∙ 22 ∙ share

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Verifiably Safe Exploration for End-to-End Reinforcement Learning

Deploying deep reinforcement learning in safety-critical settings requir...
07/02/2020 ∙ by Nathan Hunt, et al. ∙ 22 ∙ share

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Observational Overfitting in Reinforcement Learning

A major component of overfitting in model-free reinforcement learning (R...
12/06/2019 ∙ by Xingyou Song, et al. ∙ 21 ∙ share

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Semantic Photo Manipulation with a Generative Image Prior

Despite the recent success of GANs in synthesizing images conditioned on...
05/15/2020 ∙ by David Bau, et al. ∙ 21 ∙ share

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Theory of Minds: Understanding Behavior in Groups Through Inverse Planning

Human social behavior is structured by relationships. We form teams, gro...
01/18/2019 ∙ by Michael Shum, et al. ∙ 20 ∙ share

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EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

Graph representation learning resurges as a trending research subject ow...
02/26/2019 ∙ by Aldo Pareja, et al. ∙ 20 ∙ share

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A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates

We propose a robust approach for the registration of two sets of 3D poin...
03/20/2019 ∙ by Heng Yang, et al. ∙ 20 ∙ share

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Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization

We recover a video of the motion taking place in a hidden scene by obser...
12/05/2019 ∙ by Miika Aittala, et al. ∙ 20 ∙ share

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Overinterpretation reveals image classification model pathologies

Image classifiers are typically scored on their test set accuracy, but h...
03/19/2020 ∙ by Brandon Carter, et al. ∙ 20 ∙ share

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Subgraph Neural Networks

Deep learning methods for graphs achieve remarkable performance on many ...
06/18/2020 ∙ by Emily Alsentzer, et al. ∙ 20 ∙ share

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Probabilistic Programming Bots in Intuitive Physics Game Play

Recent findings suggest that humans deploy cognitive mechanism of physic...
04/05/2021 ∙ by Fahad Alhasoun, et al. ∙ 20 ∙ share

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Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection

Semidefinite Programming (SDP) and Sums-of-Squares (SOS) relaxations hav...
09/18/2019 ∙ by Heng Yang, et al. ∙ 19 ∙ share

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Minimax Semiparametric Learning With Approximate Sparsity

Many objects of interest can be expressed as a linear, mean square conti...
12/27/2019 ∙ by Jelena Bradic, et al. ∙ 19 ∙ share

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