Research Area
Year Published

626 Results

December 3, 2018

Training with Low-precision Embedding Tables

Systems for Machine Learning Workshop at NeurIPS 2018

In this work, we focus on building a system to train continuous embeddings in low precision floating point representation. Specifically, our system performs SGD-style model updates in single precision arithmetics, casts the updated parameters using stochastic rounding and stores the parameters in half-precision floating point.

By: Jian Zhang, Jiyan Yang, Hector Yuen
December 3, 2018

Explore-Exploit: A Framework for Interactive and Online Learning

Systems for Machine Learning Workshop at NeurIPS 2018

We present Explore-Exploit: a framework designed to collect and utilize user feedback in an interactive and online setting that minimizes regressions in end-user experience. This framework provides a suite of online learning operators for various tasks such as personalization ranking, candidate selection and active learning.

By: Honglei Liu, Anuj Kumar, Wenhai Yang, Benoit Dumoulin
December 2, 2018

One-Shot Unsupervised Cross Domain Translation

Conference on Neural Information Processing Systems (NIPS)

Given a single image x from domain A and a set of images from domain B, our task is to generate the analogous of x in B. We argue that this task could be a key AI capability that underlines the ability of cognitive agents to act in the world and present empirical evidence that the existing unsupervised domain translation methods fail on this task.

By: Sagie Benaim, Lior Wolf
December 2, 2018

The Description Length of Deep Learning Models

Neural Information Processing Systems (NeurIPS)

We demonstrate experimentally the ability of deep neural networks to compress the training data even when accounting for parameter encoding. The compression viewpoint originally motivated the use of variational methods in neural networks (Hinton and Van Camp, 1993; Schmidhuber, 1997).

By: Léonard Blier, Yann Ollivier
November 30, 2018

A Block Coordinate Ascent Algorithm for Mean-Variance Optimization

Neural Information Processing Systems (NeurIPS)

Risk management in dynamic decision problems is a primary concern in many fields, including financial investment, autonomous driving, and healthcare. The mean-variance function is one of the most widely used objective functions in risk management due to its simplicity and interpretability. Existing algorithms for mean-variance optimization are based on multi-time-scale stochastic approximation, whose learning rate schedules are often hard to tune, and have only asymptotic convergence proof. In this paper, we develop a model-free policy search framework for mean-variance optimization with finite-sample error bound analysis (to local optima).

By: Tengyang Xie, Bo Liu, Yangyang Xu, Mohammad Ghavamzadeh, Yinlam Chow, Daoming Lyu, Daesub Yoon
November 30, 2018

A Lyapunov-based Approach to Safe Reinforcement Learning

Neural Information Processing Systems (NeurIPS)

To incorporate safety in RL, we derive algorithms under the framework of constrained Markov decision processes (CMDPs), an extension of the standard Markov decision processes (MDPs) augmented with constraints on expected cumulative costs. Our approach hinges on a novel Lyapunov method.

By: Yinlam Chow, Ofir Nachum, Mohammad Ghavamzadeh, Edgar Duenez-Guzman
November 27, 2018

AnoGen: Deep Anomaly Generator

Outlier Detection De-constructed (ODD) Workshop

Motivated by the continued success of Variational Auto-Encoders (VAE) and Generative Adversarial Networks (GANs) to produce realistic-looking data we provide a platform to generate a realistic time-series with anomalies called AnoGen.

By: Nikolay Laptev
November 27, 2018

Deep Incremental Learning for Efficient High-Fidelity Face Tracking

ACM SIGGRAPH ASIA 2018

In this paper, we present an incremental learning framework for efficient and accurate facial performance tracking. Our approach is to alternate the modeling step, which takes tracked meshes and texture maps to train our deep learning-based statistical model, and the tracking step, which takes predictions of geometry and texture our model infers from measured images and optimize the predicted geometry by minimizing image, geometry and facial landmark errors.

By: Chenglei Wu, Takaaki Shiratori, Yaser Sheikh
November 24, 2018

Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications

ArXiv

The application of deep learning techniques resulted in remarkable improvement of machine learning models. In this paper we provide detailed characterizations of deep learning models used in many Facebook social network services.

By: Jongsoo Park, Maxim Naumov, Protonu Basu, Summer Deng, Aravind Kalaiah, Daya Khudia, James Law, Parth Malani, Andrey Malevich, Satish Nadathur, Juan Pino, Martin Schatz, Alexander Sidorov, Viswanath Sivakumar, Andrew Tulloch, Xiaodong Wang, Yiming Wu, Hector Yuen, Utku Diril, Dmytro Dzhulgakov, Kim Hazelwood, Bill Jia, Yangqing Jia, Lin Qiao, Vijay Rao, Nadav Rotem, Sungjoo Yoo, Mikhail Smelyanskiy
November 3, 2018

The Effect of Computer-Generated Descriptions on Photo-Sharing Experiences of People with Visual Impairments

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

Like sighted people, visually impaired people want to share photographs on social networking services, but find it difficult to identify and select photos from their albums. We aimed to address this problem by incorporating state-of-the-art computer-generated descriptions into Facebook’s photo-sharing feature.

By: Yuhang Zhao, Shaomei Wu, Lindsay Reynolds, Shiri Azenkot