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August 20, 2021
Virginie Do, Jamal Atif, Jérôme Lang, Nicolas Usunier
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.
Areas
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
Paper
June 18, 2021
Oran Gafni, Oron Ashual, Lior Wolf
Single-Shot Freestyle Dance Reenactment
In this work, we propose a novel method that can reanimate a single image by arbitrary video sequences, unseen during training. The method combines three networks: (i) a segmentationmapping network, (ii) a realistic frame-rendering network, and (iii) a face refinement network.
Areas
ARTIFICIAL INTELLIGENCE
COMPUTER VISION
MACHINE LEARNING
Paper
June 14, 2021
Saeed Rashidi, Matthew Denton, Srinivas Sridharan, Sudarshan Srinivasan, Amoghavarsha Suresh, Jade Nie, Tushar Krishna
Enabling Compute-Communication Overlap in Distributed Deep Learning Training Platforms
This work makes two key contributions. First, via real system measurements and detailed modeling, we provide an understanding of compute and memory bandwidth demands for DL compute and comms. Second, we propose a novel DL collective communication accelerator called Accelerator Collectives Engine (ACE) that sits alongside the compute and networking engines at the accelerator endpoint.
Areas
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
NETWORKING & CONNECTIVITY
SYSTEMS & INFRASTRUCTURE
Paper
June 11, 2021
Alexandros Haliassos, Konstantinos Vougioukas, Stavros Petridis, Maja Pantic
Lips Don’t Lie: A Generalisable and Robust Approach to Face Forgery Detection
In this paper, we propose LipForensics, a detection approach capable of both generalizing to novel manipulations and withstanding various distortions. LipForensics targets high-level semantic irregularities in mouth movements, which are common in many generated videos. It consists in first pretraining a spatio-temporal network to perform visual speech recognition (lipreading), thus learning rich internal representations related to natural mouth motion.
Areas
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
Paper
June 7, 2021
Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson
On the model-based stochastic value gradient for continuous reinforcement learning
In response, researchers have proposed model-based agents with increasingly complex components, from ensembles of probabilistic dynamics models, to heuristics for mitigating model error. In a reversal of this trend, we show that simple model-based agents can be derived from existing ideas that not only match, but outperform state-of-the-art model-free agents in terms of both sample-efficiency and final reward.
Areas
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
Paper
June 6, 2021
Yaxuan Zhou, Hao Jiang, Vamsi Krishna Ithapu
On the Predictability of HRTFs from Ear Shapes Using Deep Networks
Using 3D ear shapes as inputs, we explore the bounds of HRTF predictability using deep neural networks. To that end, we propose and evaluate two models, and identify the lowest achievable spectral distance error when predicting the true HRTF magnitude spectra.
Areas
AR/VR
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
Paper
June 6, 2021
Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li
Deep Learning on Graphs for Natural Language Processing
This tutorial of Deep Learning on Graphs for Natural Language Processing (DLG4NLP) is timely for the computational linguistics community, and covers relevant and interesting topics, including automatic graph construction for NLP, graph representation learning for NLP, various advanced GNN based models (e.g., graph2seq, graph2tree, and graph2graph) for NLP, and the applications of GNNs in various NLP tasks (e.g., machine translation, natural language generation, information extraction and semantic parsing).
Areas
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
NATURAL LANGUAGE PROCESSING & SPEECH
Paper
June 6, 2021
Panagiotis Tzirakis, Anurag Kumar, Jacob Donley
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.
Areas
AR/VR
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
NATURAL LANGUAGE PROCESSING & SPEECH
Paper
June 1, 2021
Bindita Chaudhuri, Nikolaos Sarafianos, Linda Shapiro, Tony Tung
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.
Areas
AR/VR
ARTIFICIAL INTELLIGENCE
COMPUTER VISION
Paper
May 31, 2021
Rogerio Bonatti, Arthur Bucker, Sebastian Scherer, Mustafa Mukadam, Jessica Hodgins
Batteries, camera, action! Learning a semantic control space for expressive robot cinematography
In this work, we develop a data-driven framework that enables editing of these complex camera positioning parameters in a semantic space (e.g. calm, enjoyable, establishing).
Areas
ARTIFICIAL INTELLIGENCE
Paper
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