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June 6, 2021 Panagiotis Tzirakis, Anurag Kumar, Jacob Donley
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Multi-Channel Speech Enhancement Using Graph Neural Networks

In this paper, we introduce a different research direction by viewing each audio channel as a node lying in a non-Euclidean space and, specifically, a graph.
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June 1, 2021 Bindita Chaudhuri, Nikolaos Sarafianos, Linda Shapiro, Tony Tung
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Semi-supervised Synthesis of High-Resolution Editable Textures for 3D Humans

We introduce a novel approach to generate diverse high fidelity texture maps for 3D human meshes in a semi- supervised setup.
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April 13, 2021 Deeksha Sinha, Karthik Abinav Sankararaman, Abbas Kazerouni, Vashist Avadhanula
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Multi-armed Bandits with Cost Subsidy

In this paper, we consider a novel variant of the multi-armed bandit (MAB) problem, MAB with cost subsidy, which models many real-life applications where the learning agent has to pay to select an arm and is concerned about optimizing cumulative costs and rewards.
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March 31, 2021 Christian Kroer, Nicolas E. Stier-Moses
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Market Equilibrium Models in Large-Scale Internet Markets

We focus on Internet advertising auctions, fair division problems, content recommendation systems, and robust abstractions of large-scale markets.
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March 19, 2021 Jonathan Frankle, David J. Schwab, Ari S. Morcos
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Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs

In this paper, we aim to understand the role and expressive power of affine parameters used to transform features in this way. To isolate the contribution of these parameters from that of the learned features they transform, we investigate the performance achieved when training only these parameters in BatchNorm and freezing all weights at their random initializations.
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February 17, 2021 Jie M. Zhang, Mark Harman, Lei Ma, Yang Liu
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Machine Learning Testing: Survey, Landscapes and Horizons

This paper provides a comprehensive survey of techniques for testing machine learning systems; Machine Learning Testing (ML testing) research.
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January 5, 2021 Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma, Abhishek Das, Devi Parikh, Dhruv Batra, Ramakrishna Vedantam
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IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL

We propose a novel framework to identify subgoals useful for exploration in sequential decision making tasks under partial observability.
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January 1, 2021 Mahmoud Assran, Michael Rabbat
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Asynchronous Gradient-Push

We consider a multi-agent framework for distributed optimization where each agent has access to a local smooth strongly convex function, and the collective goal is to achieve consensus on the parameters that minimize the sum of the agents’ local functions. We propose an algorithm wherein each agent operates asynchronously and independently of the other agents.
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December 14, 2020 Julien M. Hendrickx, Mike Rabbat
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Stability of Decentralized Gradient Descent in Open Multi-Agent Systems

The aim of decentralized gradient descent (DGD) is to minimize a sum of n functions held by interconnected agents. We study the stability of DGD in open contexts where agents can join or leave the system, resulting each time in the addition or the removal of their function from the global objective.
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December 8, 2020 Zhenpeng Zhou, Ahmad Beirami, Paul A. Crook, Pararth Shah, Rajen Subba, Alborz Geramifard
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Resource Constrained Dialog Policy Learning via Differentiable Inductive Logic Programming

Motivated by the needs of resource constrained dialog policy learning, we introduce dialog policy via differentiable inductive logic (DILOG). We explore the tasks of one-shot learning and zero-shot domain transfer with DILOG on SimDial and MultiWoZ.
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