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August 20, 2021 Virginie Do, Jamal Atif, Jérôme Lang, Nicolas Usunier
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Online Selection of Diverse Committees

We study three methods, theoretically and experimentally: a greedy algorithm that includes volunteers as long as proportionality is not violated; a non-adaptive method that includes a volunteer with a probability depending only on their features, assuming that the joint feature distribution in the volunteer pool is known; and a reinforcement learning based approach when this distribution is not known a priori but learnt online.
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August 13, 2021 Dimitris Kalimeris, Smriti Bhagat, Shankar Kalyanaraman, Udi Weinsberg
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Preference Amplification in Recommender Systems

We propose a theoretical framework for studying such amplification in a matrix factorization based recommender system. We model the dynamics of the system, where users interact with the recommender systems and gradually “drift” toward the recommended content, with the recommender system adapting, based on user feedback, to the updated preferences.
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August 9, 2021 Stephen Lombardi, Tomas Simon, Gabriel Schwartz, Michael Zollhoefer, Yaser Sheikh, Jason Saragih
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Mixture of Volumetric Primitives for Efficient Neural Rendering

We present Mixture of Volumetric Primitives (MVP), a representation for rendering dynamic 3D content that combines the completeness of volumetric representations with the efficiency of primitive-based rendering, e.g., point-based or mesh-based methods. Our approach achieves this by leveraging spatially shared computation with a convolutional architecture and by minimizing computation in empty regions of space with volumetric primitives that can move to cover only occupied regions.
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August 9, 2021 Jungdam Won, Deepak Gopinath, Jessica Hodgins
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Control Strategies for Physically Simulated Characters Performing Two-player Competitive Sports

In this paper, we develop a learning framework that generates control policies for physically simulated athletes who have many degrees-of-freedom. Our framework uses a two step-approach, learning basic skills and learning boutlevel strategies, with deep reinforcement learning, which is inspired by the way that people how to learn competitive sports.
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August 9, 2021 He Zhang, Yuting Ye, Takaaki Shiratori, Taku Komura
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ManipNet: Neural Manipulation Synthesis with a Hand-Object Spatial Representation

In this paper, we propose a hand-object spatial representation that can achieve generalization from limited data. Our representation combines the global object shape as voxel occupancies with local geometric details as samples of closest distances. This representation is used by a neural network to regress finger motions from input trajectories of wrists and objects.
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August 1, 2021 Yun Tang, Juan Pino, Xian Li, Changhan Wang, Dmitriy Genzel
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Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task

In this study, we are interested in training a speech to text translation model along with an auxiliary text to text translation task. We conduct a detailed analysis to understand the impact of the auxiliary task on the primary task within the multitask learning framework.
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August 1, 2021 Nasser Aldaghri, Hessam Mahdavifar, Ahmad Beirami
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Coded Machine Unlearning

Our experimental results show that the proposed coded machine unlearning provides a better performance versus unlearning cost trade-off compared to the uncoded baseline.
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July 24, 2021 Jonathan Lorraine, Jack Parker-Holder, Paul Vicol, Aldo Pacchiano, Luke Metz, Tal Kachman, Jakob Foerster
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Using Bifurcations for Diversity in Differentiable Games

RR is designed for conservative gradient systems (i.e. settings involving a single loss function), where it branches at saddles – the only relevant bifurcation points. We generalize this idea to non-conservative, multi-agent gradient systems by identifying new types of bifurcation points and proposing a method to follow eigenvectors with complex eigenvalues.
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July 23, 2021 David Eriksson, Martin Jankowiak
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High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces

In an extensive suite of experiments comparing to existing methods for high-dimensional BO we demonstrate that our algorithm, Sparse Axis-Aligned Subspace BO (SAASBO), achieves excellent performance on several synthetic and real-world problems without the need to set problem-specific hyperparameters.
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July 18, 2021 Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, Stéphane Deny
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Barlow Twins: Self-Supervised Learning via Redundancy Reduction

We propose an objective function that naturally avoids collapse by measuring the cross-correlation matrix between the outputs of two identical networks fed with distorted versions of a sample, and making it as close to the identity matrix as possible. This causes the embedding vectors of distorted versions of a sample to be similar, while minimizing the redundancy between the components of these vectors.
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