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Dec 05, 2019
Artificial Intelligence

Clone Swarms: Learning to Predict and Control Multi-Robot Systems by …

In this paper, we propose SwarmNet -- a neural network architecture that can learn to predict and imitate the behavior of an observed swarm of agents…
Dec 05, 2019
Machine Learning

Combining Q-Learning and Search with Amortized Value Estimates

We introduce "Search with Amortized Value Estimates" (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search…
Dec 05, 2019
Computer Vision

PolyTransform: Deep Polygon Transformer for Instance Segmentation

In this paper, we propose PolyTransform, a novel instance segmentation algorithm that produces precise, geometry-preserving masks by combining the st…
Dec 05, 2019
Machine Learning
Information Theory

Adversarial Risk via Optimal Transport and Optimal Couplings

The accuracy of modern machine learning algorithms deteriorates severely on adversarially manipulated test data. Optimal adversarial risk quantifies …
Dec 05, 2019
Computer Vision
Machine Learning

Self-Supervised Learning of Video-Induced Visual Invariances

We propose a general framework for self-supervised learning of transferable visual representations based on video-induced visual invariances (VIVI). …
Dec 05, 2019
Machine Learning
Computer Vision

AugMix: A Simple Data Processing Method to Improve Robustness and U…

Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assu…
Dec 05, 2019
Machine Learning
Cryptography and Security

Label-Consistent Backdoor Attacks

Deep neural networks have been demonstrated to be vulnerable to backdoor attacks. Specifically, by injecting a small number of maliciously constructe…
Dec 05, 2019
Machine Learning
Statistics Theory

On the Sample Complexity of Learning Sum-Product Networks

Sum-Product Networks (SPNs) can be regarded as a form of deep graphical models that compactly represent deeply factored and mixed distributions. An S…
Dec 05, 2019
Machine Learning

Normalizing Flows for Probabilistic Modeling and Inference

Normalizing flows provide a general mechanism for defining expressive probability distributions, only requiring the specification of a (usually simpl…
Dec 05, 2019
Computation and Language

We have presented the first study on social bias in KG embeddings, and proposed a new metric for measuring such bias. We demonstrated that differences in the distributions of entities in real-world knowledge graphs (there are many more male bankers …

Measuring Social Bias in Knowledge Graph Embeddings

It has recently been shown that word embeddings encode social biases, with a harmful impact on downstream tasks. However, to this point there has bee…
Dec 05, 2019
Machine Learning

Deep Ensembles: A Loss Landscape Perspective

Deep ensembles have been empirically shown to be a promising approach for improving accuracy, uncertainty and out-of-distribution robustness of deep …
Dec 05, 2019
Robotics

Reorienting Objects in 3D Space Using Pivoting

We consider the problem of reorienting a rigid object with arbitrary known shape on a table using a two-finger pinch gripper. Reorienting problem is …
Dec 05, 2019
Computer Vision

Revisiting Few-Shot Learning for Facial Expression Recognition

Most of the existing deep neural nets on automatic facial expression recognition focus on a set of predefined emotion classes, where the amount of tr…
Dec 05, 2019
Machine Learning

MetaFun: Meta-Learning with Iterative Functional Updates

Few-shot supervised learning leverages experience from previous learning tasks to solve new tasks where only a few labelled examples are available. O…
Dec 05, 2019
Disordered Systems and Neural Networks
Statistical Mechanics
Machine Learning
Statistics Theory

Rademacher complexity and spin glasses: A link between the replica an…

Statistical learning theory provides bounds of the generalization gap, using in particular the Vapnik-Chervonenkis dimension and the Rademacher compl…
Dec 05, 2019
Machine Learning
Statistics Theory

Causal structure based root cause analysis of outliers

We describe a formal approach to identify 'root causes' of outliers observed in $n$ variables $X_1,\dots,X_n$ in a scenario where the causal relation…
Dec 05, 2019
Machine Learning
Multiagent Systems
Optimization and Control

Collective Learning

In this paper, we introduce the concept of collective learning (CL) which exploits the notion of collective intelligence in the field of distributed …
Dec 05, 2019
Machine Learning

Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learni…

In this paper we present an end-to-end framework for addressing the problem of dynamic pricing on E-commerce platform using methods based on deep rei…
Dec 05, 2019
Machine Learning

Screening Data Points in Empirical Risk Minimization via Ellipsoidal …

We design simple screening tests to automatically discard data samples in empirical risk minimization without losing optimization guarantees. We deri…
Dec 05, 2019
Artificial Intelligence

Reinforcement Learning with Non-Markovian Rewards

The standard RL world model is that of a Markov Decision Process (MDP). A basic premise of MDPs is that the rewards depend on the last state and acti…
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