145 Results
December 3, 2018
High-Level Strategy Selection under Partial Observability in StarCraft: Brood War
Reinforcement Learning under Partial Observability Workshop at NeurIPS 2018
We consider the problem of high-level strategy selection in the adversarial setting of real-time strategy games from a reinforcement learning perspective, where taking an action corresponds to switching to the respective strategy.
By: Jonas Gehring, Da Ju, Vegard Mella, Daniel Gant, Nicolas Usunier, Gabriel Synnaeve
Facebook AI Research 
Machine Learning 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 OllivierDecember 2, 2018
One-Shot Unsupervised Cross Domain Translation
Neural Information Processing Systems (NeurIPS)
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 WolfNovember 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-GuzmanNovember 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 YoonNovember 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 LaptevNovember 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
Computer Vision 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
Systems & Networking November 2, 2018
Jump to better conclusions: SCAN both left and right
Empirical Methods in Natural Language Processing (EMNLP)
Lake and Baroni (2018) recently introduced the SCAN data set, which consists of simple commands paired with action sequences and is intended to test the strong generalization abilities of recurrent sequence-to-sequence models. Their initial experiments suggested that such models may fail because they lack the ability to extract systematic rules. Here, we take a closer look at SCAN and show that it does not always capture the kind of generalization that it was designed for.
By: Joost Bastings, Marco Baroni, Jason Weston, Kyunghyun Cho, Douwe Kiela
Natural Language Processing & Speech November 2, 2018
Do explanations make VQA models more predictable to a human?
Empirical Methods in Natural Language Processing (EMNLP)
A rich line of research attempts to make deep neural networks more transparent by generating human-interpretable ‘explanations’ of their decision process, especially for interactive tasks like Visual Question Answering (VQA). In this work, we analyze if existing explanations indeed make a VQA model – its responses as well as failures – more predictable to a human.
By: Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav, Prithvijit Chattopadhyay, Devi Parikh