230 Results
October 28, 2019
Unsupervised Pre-Training of Image Features on Non-Curated Data
International Conference on Computer Vision (ICCV)
Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using non-curated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available.
By: Mathilde Caron, Piotr Bojanowski, Julien Mairal, Armand Joulin
Computer Vision 
Facebook AI Research 
Machine Learning October 27, 2019
Video Classification with Channel-Separated Convolutional Networks
International Conference on Computer Vision (ICCV)
This paper studies the effects of different design choices in 3D group convolutional networks for video classification. We empirically demonstrate that the amount of channel interactions plays an important role in the accuracy of 3D group convolutional networks.
By: Du Tran, Heng Wang, Lorenzo Torresani, Matt FeiszliAugust 4, 2019
MSURU: Large Scale E-commerce Image Classification With Weakly Supervised Search Data
Conference on Knowledge Discovery and Data Mining (KDD)
In this paper we present a deployed image recognition system used in a large scale commerce search engine, which we call MSURU. It is designed to process product images uploaded daily to Facebook Marketplace. Social commerce is a growing area within Facebook and understanding visual representations of product content is important for search and recommendation applications on Marketplace.
By: Yina Tang, Fedor Borisyuk, Siddarth Malreddy, Yixuan Li, Yiqun Liu, Sergey KirshnerAugust 2, 2019
Low-Resource Corpus Filtering using Multilingual Sentence Embeddings
Association for Computational Linguistics (ACL)
In this paper, we describe our submission to the WMT19 low-resource parallel corpus filtering shared task. Our main approach is based on the LASER toolkit (Language-Agnostic SEntence Representations), which uses an encoder-decoder architecture trained on a parallel corpus to obtain multilingual sentence representations.
By: Vishrav Chaudhary, Yuqing Tang, Francisco (Paco) Guzman, Holger Schwenk, Philipp Koehn
Natural Language Processing & Speech 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 HuiAugust 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 July 29, 2019
Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
To address the challenge of rapidly generating low-level controllers, we argue for using model-based reinforcement learning (MBRL) trained on relatively small amounts of automatically generated (i.e., without system simulation) data. In this paper, we explore the capabilities of MBRL on a Crazyflie centimeter-scale quadrotor with rapid dynamics to predict and control at ≤ 50Hz.
By: Nathan O. Lambert, Daniel S. Drew, Joseph Yaconelli, Sergey Levine, Roberto Calandra, Kristofer S. J. PisterJuly 29, 2019
Word-order biases in deep-agent emergent communication
Association for Computational Linguistics (ACL)
Sequence-processing neural networks led to remarkable progress on many NLP tasks. As a consequence, there has been increasing interest in understanding to what extent they process language as humans do. We aim here to uncover which biases such models display with respect to “natural” word-order constraints.
By: Rahma Chaabouni, Eugene Kharitonov, Alessandro Lazaric, Emmanuel Dupoux, Marco Baroni