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Oct 15, 2020
Computer Vision

LTN: Long-Term Network for Long-Term Motion Prediction

Making accurate motion prediction of surrounding agents such as pedestrians and vehicles is a critical task when robots are trying to perform autonom…
Oct 15, 2020
Computer Vision

Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation

We propose a general framework for searching surrogate losses for mainstream semantic segmentation metrics. This is in contrast to existing loss func…
Oct 15, 2020
Robotics

Multi-Resolution 3D Mapping with Explicit Free Space Representation f…

With the aim of bridging the gap between high quality reconstruction and mobile robot motion planning, we propose an efficient system that leverages …
Oct 15, 2020
Machine Learning
Computer Vision

Representation Learning via Invariant Causal Mechanisms

Self-supervised learning has emerged as a strategy to reduce the reliance on costly supervised signal by pretraining representations only using unlab…
Oct 15, 2020
Machine Learning
Atmospheric and Oceanic Physics

Rainfall-Runoff Prediction at Multiple Timescales with a Single Long …

Long Short-Term Memory Networks (LSTMs) have been applied to daily discharge prediction with remarkable success. Many practical scenarios, however, r…
Oct 15, 2020
Artificial Intelligence
Multiagent Systems

Multi-Agent Trust Region Policy Optimization

We extend trust region policy optimization (TRPO) to multi-agent reinforcement learning (MARL) problems. We show that the policy update of TRPO can b…
Oct 15, 2020
Machine Learning

Marginal Contribution Feature Importance -- an Axiomatic Approach for…

When training a predictive model over medical data, the goal is sometimes to gain insights about a certain disease. In such cases, it is common to us…
Oct 15, 2020
Machine Learning
Information Theory

Probabilistic Sequential Shrinking: A Best Arm Identification Algorit…

We consider a best arm identification (BAI) problem for stochastic bandits with adversarial corruptions in the fixed-budget setting of $T$ steps. We …
Oct 15, 2020
Machine Learning
Artificial Intelligence

An Alternative to Backpropagation in Deep Reinforcement Learning

State-of-the-art deep learning algorithms mostly rely on gradient backpropagation to train a deep artificial neural network, which is generally regar…
Oct 15, 2020
Robotics

Pick-Place With Uncertain Object Instance Segmentation and Shape Co…

In this paper we consider joint perception and control of a pick-place system. It is important to consider perception and control jointly as some act…
Oct 15, 2020
Computation and Language
Machine Learning

Improving Natural Language Processing Tasks with Human Gaze-Guided …

A lack of corpora has so far limited advances in integrating human gaze data as a supervisory signal in neural attention mechanisms for natural langu…
Oct 15, 2020
Computation and Language

Compressive Summarization with Plausibility and Salience Modeling

Compressive summarization systems typically rely on a crafted set of syntactic rules to determine what spans of possible summary sentences can be del…
Oct 15, 2020
Computation and Language

Understanding Neural Abstractive Summarization Models via Uncertainty

An advantage of seq2seq abstractive summarization models is that they generate text in a free-form manner, but this flexibility makes it difficult to…
Oct 15, 2020
Computer Vision

An Empirical Analysis of Visual Features for Multiple Object Tracking…

This paper addresses the problem of selecting appearance features for multiple object tracking (MOT) in urban scenes. Over the years, a large number …
Oct 15, 2020
Machine Learning
Artificial Intelligence

Avoiding Side Effects By Considering Future Tasks

Designing reward functions is difficult: the designer has to specify what to do (what it means to complete the task) as well as what not to do (side …
Oct 15, 2020
Machine Learning

Neograd: gradient descent with an adaptive learning rate

Since its inception by Cauchy in 1847, the gradient descent algorithm has been without guidance as to how to efficiently set the learning rate. This …
Oct 15, 2020
Machine Learning

Constructing Multilayer Perceptrons as Piecewise Low-Order Polynomial…

The construction of a multilayer perceptron (MLP) as a piecewise low-order polynomial approximator using a signal processing approach is presented in…
Oct 15, 2020
Machine Learning
Social and Information Networks

Deep Generative Modeling in Network Science with Applications to Publ…

Network data is increasingly being used in quantitative, data-driven public policy research. These are typically very rich datasets that contain comp…
Oct 15, 2020
Machine Learning

Double Robust Representation Learning for Counterfactual Prediction

Causal inference, or counterfactual prediction, is central to decision making in healthcare, policy and social sciences. To de-bias causal estimators…
Oct 15, 2020
Computation and Language
Machine Learning

Continual Learning for Neural Semantic Parsing

A semantic parsing model is crucial to natural language processing applications such as goal-oriented dialogue systems. Such models can have hundreds…
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