
Understanding Human Judgments of Causality
Discriminating between causality and correlation is a major problem in m...
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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...
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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...
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Generalized Maximum Causal Entropy for Inverse Reinforcement Learning
We consider the problem of learning from demonstrated trajectories with ...
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A Probe into Understanding GAN and VAE models
Both generative adversarial network models and variational autoencoders ...
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Data augmentation using learned transforms for oneshot medical image segmentation
Biomedical image segmentation is an important task in many medical appli...
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A Theory of Uncertainty Variables for State Estimation and Inference
Probability theory forms an overarching framework for modeling uncertain...
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Survey of Attacks and Defenses on EdgeDeployed Neural Networks
Deep Neural Network (DNN) workloads are quickly moving from datacenters ...
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Avoidance Learning Using Observational Reinforcement Learning
Imitation learning seeks to learn an expert policy from sampled demonstr...
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DiffTaichi: Differentiable Programming for Physical Simulation
We study the problem of learning and optimizing through physical simulat...
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Kimera: an OpenSource Library for RealTime MetricSemantic Localization and Mapping
We provide an opensource C++ library for realtime metricsemantic visu...
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Continual Learning with SelfOrganizing Maps
Despite remarkable successes achieved by modern neural networks in a wid...
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Inverse Reinforcement Learning with Missing Data
We consider the problem of recovering an expert's reward function with i...
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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...
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On Fast Leverage Score Sampling and Optimal Learning
Leverage score sampling provides an appealing way to perform approximate...
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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...
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Are Transformers universal approximators of sequencetosequence functions?
Despite the widespread adoption of Transformer models for NLP tasks, the...
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Towards Practical MultiObject Manipulation using Relational Reinforcement Learning
Learning robotic manipulation tasks using reinforcement learning with sp...
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The Limitations of Adversarial Training and the BlindSpot Attack
The adversarial training procedure proposed by Madry et al. (2018) is on...
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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...
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Observational Overfitting in Reinforcement Learning
A major component of overfitting in modelfree reinforcement learning (R...
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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...
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EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Graph representation learning resurges as a trending research subject ow...
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A Polynomialtime Solution for Robust Registration with Extreme Outlier Rates
We propose a robust approach for the registration of two sets of 3D poin...
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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...
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Graduated NonConvexity for Robust Spatial Perception: From NonMinimal Solvers to Global Outlier Rejection
Semidefinite Programming (SDP) and SumsofSquares (SOS) relaxations hav...
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Minimax Semiparametric Learning With Approximate Sparsity
Many objects of interest can be expressed as a linear, mean square conti...
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Efficiently testing local optimality and escaping saddles for ReLU networks
We provide a theoretical algorithm for checking local optimality and esc...
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Predicting Factuality of Reporting and Bias of News Media Sources
We present a study on predicting the factuality of reporting and bias of...
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CTToMR Conditional Generative Adversarial Networks for Ischemic Stroke Lesion Segmentation
Infarcted brain tissue resulting from acute stroke readily shows up as h...
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Paretooptimal data compression for binary classification tasks
The goal of lossy data compression is to reduce the storage cost of a da...
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Deep Context Map: Agent Trajectory Prediction using Locationspecific Latent Maps
In this paper, we propose a novel approach for agent motion prediction i...
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Every Local Minimum is a Global Minimum of an Induced Model
For nonconvex optimization in machine learning, this paper proves that ...
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The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development
As machine learning is applied more and more widely, data scientists oft...
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Confidence Calibration for Convolutional Neural Networks Using Structured Dropout
In classification applications, we often want probabilistic predictions ...
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Painting Many Pasts: Synthesizing Time Lapse Videos of Paintings
We introduce a new video synthesis task: synthesizing time lapse videos ...
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kPAMSC: Generalizable Manipulation Planning using KeyPoint Affordance and Shape Completion
Manipulation planning is the task of computing robot trajectories that m...
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ℓ_∞ Vector Contraction for Rademacher Complexity
We show that the Rademacher complexity of any R^Kvalued function class ...
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Reasoning About Physical Interactions with ObjectOriented Prediction and Planning
Objectbased factorizations provide a useful level of abstraction for in...
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Learning protein sequence embeddings using information from structure
Inferring the structural properties of a protein from its amino acid seq...
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Kernel Instrumental Variable Regression
Instrumental variable regression is a strategy for learning causal relat...
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Characterizing Sources of Uncertainty to Proxy Calibration and Disambiguate Annotator and Data Bias
Supporting model interpretability for complex phenomena where annotators...
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Deep Evidential Regression
Deterministic neural networks (NNs) are increasingly being deployed in s...
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Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
Classical deformable registration techniques achieve impressive results ...
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Learning to guide task and motion planning using scorespace representation
In this paper, we propose a learning algorithm that speeds up the search...
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Is Robustness the Cost of Accuracy?  A Comprehensive Study on the Robustness of 18 Deep Image Classification Models
The prediction accuracy has been the longlasting and sole standard for ...
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How Powerful are Graph Neural Networks?
Graph Neural Networks (GNNs) for representation learning of graphs broad...
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Finite sample expressive power of smallwidth ReLU networks
We study universal finite sample expressivity of neural networks, define...
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SGD without Replacement: Sharper Rates for General Smooth Convex Functions
We study stochastic gradient descent without replacement () for smooth ...
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Unsupervised Discovery of Parts, Structure, and Dynamics
Humans easily recognize object parts and their hierarchical structure by...
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MIT
The Massachusetts Institute of Technology is a private research university in Cambridge, Massachusetts.