214 Results
August 1, 2019
Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks
Annual Meeting of the Association for Computational Linguistics (ACL)
Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. This paper proposes a series of lightweight and memory efficient neural architectures for a potpourri of natural language processing (NLP) tasks.
By: Yi Tay, Aston Zhang, Luu Anh Tuan, Jinfeng Rao, Shuai Zhang, Shuohang Wang, Jie Fu, Siu Cheung Hui
Facebook AI Research 
Machine Learning 
Natural Language Processing & Speech August 1, 2019
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives
Annual Meeting of the Association for Computational Linguistics (ACL)
This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens. We propose a curriculum learning (CL) based Pointer-Generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty.
By: Yi Tay, Shuohang Wang, Luu Anh Tuan, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston ZhangAugust 1, 2019
Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue
Annual Meeting of the Association for Computational Linguistics (ACL)
In this paper, we (1) propose using tree-structured semantic representations, like those used in traditional rule-based NLG systems, for better discourse-level structuring and sentence-level planning; (2) introduce a challenging dataset using this representation for the weather domain; (3) introduce a constrained decoding approach for Seq2Seq models that leverages this representation to improve semantic correctness; and (4) demonstrate promising results on our dataset and the E2E dataset.
By: Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, Rajen SubbaJuly 31, 2019
Neural Volumes: Learning Dynamic Renderable Volumes from Images
SIGGRAPH
To overcome memory limitations of voxel-based representations, we learn a dynamic irregular grid structure implemented with a warp field during ray-marching. This structure greatly improves the apparent resolution and reduces grid-like artifacts and jagged motion. Finally, we demonstrate how to incorporate surface-based representations into our volumetric-learning framework for applications where the highest resolution is required, using facial performance capture as a case in point.
By: Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, Yaser Sheikh
AR/VR 
Computer Vision July 15, 2019
Searching for Communities: a Facebook Way
ACM SIGIR Conference on Research and Development in Information Retrieval
Giving people the power to build community is central to Facebook’s mission. Technically, searching for communities poses very different challenges compared to the standard IR problems.
By: Viet Ha-Thuc, Srinath Aaleti, Rongda Zhu, Nade Sritanyaratana, Corey Chen
Data Science July 12, 2019
VR Facial Animation via Multiview Image Translation
SIGGRAPH
In this work, we present a bidirectional system that can animate avatar heads of both users’ full likeness using consumer-friendly headset mounted cameras (HMC). There are two main challenges in doing this: unaccommodating camera views and the image-to-avatar domain gap. We address both challenges by leveraging constraints imposed by multiview geometry to establish precise image-to-avatar correspondence, which are then used to learn an end-to-end model for real-time tracking.
By: Shih-En Wei, Jason Saragih, Tomas Simon, Adam W. Harley, Stephen Lombardi, Michal Perdoch, Alexander Hypes, Dawei Wang, Hernan Badino, Yaser SheikhJune 30, 2019
Perturbed-History Exploration in Stochastic Multi-Armed Bandits
International Joint Conference on Artificial Intelligence (IJCAI)
We propose an online algorithm for cumulative regret minimization in a stochastic multi-armed bandit. The algorithm adds O(t) i.i.d. pseudo-rewards to its history in round t and then pulls the arm with the highest average reward in its perturbed history.
By: Branislav Kveton, Csaba Szepesvári, Mohammad Ghavamzadeh, Craig BoutilierJune 22, 2019
HackPPL: A Universal Probabilistic Programming Language
MAPL at PLDI
This paper overviews the design and implementation choices for the HackPPL toolchain and presents findings by applying it to a representative problem faced by social media companies.
By: Jessica Ai, Nimar S. Arora, Ning Dong, Beliz Gokkaya, Thomas Jiang, Anitha Kubendran, Arun Kumar, Michael Tingley, Narjes TorabiJune 20, 2019
Risk-Sensitive Generative Adversarial Imitation Learning
International Conference on Artificial Intelligence and Statistics (AISTATS)
We study risk-sensitive imitation learning where the agent’s goal is to perform at least as well as the expert in terms of a risk profile. We first formulate our risk-sensitive imitation learning setting. We consider the generative adversarial approach to imitation learning (GAIL) and derive an optimization problem for our formulation, which we call it risk-sensitive GAIL (RS-GAIL).
By: Jonathan Lacotte, Mohammad Ghavamzadeh, Yinlam Chow, Marco PavoneJune 18, 2019
Embodied Question Answering in Photorealistic Environments with Point Cloud Perception
Conference Computer Vision and Pattern Recognition (CVPR)
To help bridge the gap between internet vision-style problems and the goal of vision for embodied perception we instantiate a large-scale navigation task – Embodied Question Answering [1] in photo-realistic environments (Matterport 3D).
By: Erik Wijmans, Samyak Datta, Oleksandr Maksymets, Abhishek Das, Georgia Gkioxari, Stefan Lee, Irfan Essa, Devi Parikh, Dhruv Batra