Research Area
Year Published

999 Results

November 25, 2019

Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller

Neural Information Processing Systems (NeurIPS)

We study a generalized setup for learning from demonstration to build an agent that can manipulate novel objects in unseen scenarios by looking at only a single video of human demonstration from a third-person perspective. To accomplish this goal, our agent should not only learn to understand the intent of the demonstrated third-person video in its context but also perform the intended task in its environment configuration.

By: Pratyusha Sharma, Deepak Pathak, Abhinav Gupta

November 25, 2019

Findings of the First Shared Task on Machine Translation Robustness

Conference on Machine Learning (WMT)

We share the findings of the first shared task on improving robustness of Machine Translation (MT). The task provides a testbed representing challenges facing MT models deployed in the real world, and facilitates new approaches to improve models’ robustness to noisy input and domain mismatch. We focus on two language pairs (English-French and English-Japanese), and the submitted systems are evaluated on a blind test set consisting of noisy comments on Reddit and professionally sourced translations.

By: Xian Li, Paul Michel, Antonios Anastasopoulos, Yonatan Belinkov, Nadir Durrani, Orhan Firat, Philipp Koehn, Graham Neubig, Juan Pino, Hassan Sajjad

November 18, 2019

DeepFovea: Neural Reconstruction for Foveated Rendering and Video Compression using Learned Statistics of Natural Videos

ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia

Foveated rendering and compression can save computations by reducing the image quality in the peripheral vision. However, this can cause noticeable artifacts in the periphery, or, if done conservatively, would provide only modest savings. In this work, we explore a novel foveated reconstruction method that employs the recent advances in generative adversarial neural networks.

By: Anton S. Kaplanyan, Anton Sochenov, Thomas Leimkühler, Mikhail Okunev, Todd Goodall, Gizem Rufo

November 17, 2019

Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels

Neural Information Processing Systems (NeurIPS)

Many machine learning methods depend on human supervision to achieve optimal performance. However, in tasks such as DensePose, where the goal is to establish dense visual correspondences between images, the quality of manual annotations is intrinsically limited. We address this issue by augmenting neural network predictors with the ability to output a distribution over labels, thus explicitly and introspectively capturing the aleatoric uncertainty in the annotations.

By: Natalia Neverova, David Novotny, Andrea Vedaldi

November 10, 2019

An Integrated 6DoF Video Camera and System Design

SIGGRAPH Asia

Designing a fully integrated 360◦ video camera supporting 6DoF head motion parallax requires overcoming many technical hurdles, including camera placement, optical design, sensor resolution, system calibration, real-time video capture, depth reconstruction, and real-time novel view synthesis. While there is a large body of work describing various system components, such as multi-view depth estimation, our paper is the first to describe a complete, reproducible system that considers the challenges arising when designing, building, and deploying a full end-to-end 6DoF video camera and playback environment.

By: Albert Parra Pozo, Michael Toksvig, Terry Filiba Schrager, Joyce Hsu, Uday Mathur, Alexander Sorkine-Hornung, Richard Szeliski, Brian Cabral

November 10, 2019

Adversarial Bandits with Knapsacks

Symposium on Foundations of Computer Science (FOCS)

We consider Bandits with Knapsacks (henceforth, BwK), a general model for multi-armed bandits under supply/budget constraints. In particular, a bandit algorithm needs to solve a well-known knapsack problem: find an optimal packing of items into a limited-size knapsack. The BwK problem is a common generalization of numerous motivating examples, which range from dynamic pricing to repeated auctions to dynamic ad allocation to network routing and scheduling.

By: Nicole Immorlica, Karthik Abinav Sankararaman, Robert Schapire, Aleksandrs Slivkins

November 10, 2019

EASSE: Easier Automatic Sentence Simplification Evaluation

Conference on Empirical Methods in Natural Language Processing (EMNLP)

We introduce EASSE, a Python package aiming to facilitate and standardize automatic evaluation and comparison of Sentence Simplification (SS) systems. EASSE provides a single access point to a broad range of evaluation resources: standard automatic metrics for assessing SS outputs (e.g. SARI), word-level accuracy scores for certain simplification transformations, reference-independent quality estimation features (e.g. compression ratio), and standard test data for SS evaluation (e.g. TurkCorpus).

By: Fernando Alva-Manchego, Louis Martin, Carolina Scarton, Lucia Specia

November 9, 2019

Harassment in Social Virtual Reality: Challenges for Platform Governance

Conference on Computer-Supported Cooperative Work and Social Computing (CSCW)

In immersive virtual reality (VR) environments, experiences of harassment can be exacerbated by features such as synchronous voice chat, heightened feelings of presence and embodiment, and avatar movements that can feel like violations of personal space (such as simulated touching or grabbing). Simultaneously, efforts to govern these developing spaces are made more complex by the distributed landscape of virtual reality applications and the dynamic nature of local community norms. To better understand this nascent social and psychological environment, we interviewed VR users (n=25) about their experiences with harassment, abuse, and discomfort in social VR.

By: Lindsay Blackwell, Nicole Ellison, Natasha Elliott-Deflo, Raz Schwartz

November 7, 2019

Feature Selection for Facebook Feed Ranking System via a Group-Sparsity-Regularized Training Algorithm

Conference on Information and Knowledge Management (CIKM)

In modern production platforms, large scale online learning models are applied to data of very high dimension. To save computational resource, it is important to have an efficient algorithm to select the most significant features from an enormous feature pool. In this paper, we propose a novel neural-network-suitable feature selection algorithm, which selects important features from the input layer during training.

By: Xiuyan Ni, Yang Yu, Peng Wu, Youlin Li, Shaoliang Nie, Qichao Que, Chao Chen

November 7, 2019

Cloze-driven Pretraining of Self-attention Networks

Conference on Empirical Methods in Natural Language Processing (EMNLP)

We present a new approach for pretraining a bi-directional transformer model that provides significant performance gains across a variety of language understanding problems.

By: Alexei Baevski, Sergey Edunov, Yinhan Liu, Luke Zettlemoyer, Michael Auli