May 7, 2019

2750 words 13 mins read

Paper Group AWR 36

A Mathematical Formalization of Hierarchical Temporal Memory’s Spatial Pooler. Specific Differential Entropy Rate Estimation for Continuous-Valued Time Series. Benchmarking Deep Reinforcement Learning for Continuous Control. RNN Approaches to Text Normalization: A Challenge. Towards Viewpoint Invariant 3D Human Pose Estimation. A budget-constrained …

A Mathematical Formalization of Hierarchical Temporal Memory’s Spatial Pooler


Title	A Mathematical Formalization of Hierarchical Temporal Memory’s Spatial Pooler
Authors	James Mnatzaganian, Ernest Fokoué, Dhireesha Kudithipudi
Abstract	Hierarchical temporal memory (HTM) is an emerging machine learning algorithm, with the potential to provide a means to perform predictions on spatiotemporal data. The algorithm, inspired by the neocortex, currently does not have a comprehensive mathematical framework. This work brings together all aspects of the spatial pooler (SP), a critical learning component in HTM, under a single unifying framework. The primary learning mechanism is explored, where a maximum likelihood estimator for determining the degree of permanence update is proposed. The boosting mechanisms are studied and found to be only relevant during the initial few iterations of the network. Observations are made relating HTM to well-known algorithms such as competitive learning and attribute bagging. Methods are provided for using the SP for classification as well as dimensionality reduction. Empirical evidence verifies that given the proper parameterizations, the SP may be used for feature learning.
Tasks	Dimensionality Reduction
Published	2016-01-22
URL	http://arxiv.org/abs/1601.06116v3
PDF	http://arxiv.org/pdf/1601.06116v3.pdf
PWC	https://paperswithcode.com/paper/a-mathematical-formalization-of-hierarchical
Repo	https://github.com/mrkrynmdsco/htm-python
Framework	none

Specific Differential Entropy Rate Estimation for Continuous-Valued Time Series


Title	Specific Differential Entropy Rate Estimation for Continuous-Valued Time Series
Authors	David Darmon
Abstract	We introduce a method for quantifying the inherent unpredictability of a continuous-valued time series via an extension of the differential Shannon entropy rate. Our extension, the specific entropy rate, quantifies the amount of predictive uncertainty associated with a specific state, rather than averaged over all states. We relate the specific entropy rate to popular `complexity’ measures such as Approximate and Sample Entropies. We provide a data-driven approach for estimating the specific entropy rate of an observed time series. Finally, we consider three case studies of estimating specific entropy rate from synthetic and physiological data relevant to the analysis of heart rate variability. \|
Tasks	Heart Rate Variability, Time Series
Published	2016-06-08
URL	http://arxiv.org/abs/1606.02615v1
PDF	http://arxiv.org/pdf/1606.02615v1.pdf
PWC	https://paperswithcode.com/paper/specific-differential-entropy-rate-estimation
Repo	https://github.com/ddarmon/spenra
Framework	none

Benchmarking Deep Reinforcement Learning for Continuous Control


Title	Benchmarking Deep Reinforcement Learning for Continuous Control
Authors	Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel
Abstract	Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning. Some notable examples include training agents to play Atari games based on raw pixel data and to acquire advanced manipulation skills using raw sensory inputs. However, it has been difficult to quantify progress in the domain of continuous control due to the lack of a commonly adopted benchmark. In this work, we present a benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, tasks with partial observations, and tasks with hierarchical structure. We report novel findings based on the systematic evaluation of a range of implemented reinforcement learning algorithms. Both the benchmark and reference implementations are released at https://github.com/rllab/rllab in order to facilitate experimental reproducibility and to encourage adoption by other researchers.
Tasks	Atari Games, Continuous Control
Published	2016-04-22
URL	http://arxiv.org/abs/1604.06778v3
PDF	http://arxiv.org/pdf/1604.06778v3.pdf
PWC	https://paperswithcode.com/paper/benchmarking-deep-reinforcement-learning-for
Repo	https://github.com/richardrl/cartpole-request-for-research
Framework	pytorch

RNN Approaches to Text Normalization: A Challenge


Title	RNN Approaches to Text Normalization: A Challenge
Authors	Richard Sproat, Navdeep Jaitly
Abstract	This paper presents a challenge to the community: given a large corpus of written text aligned to its normalized spoken form, train an RNN to learn the correct normalization function. We present a data set of general text where the normalizations were generated using an existing text normalization component of a text-to-speech system. This data set will be released open-source in the near future. We also present our own experiments with this data set with a variety of different RNN architectures. While some of the architectures do in fact produce very good results when measured in terms of overall accuracy, the errors that are produced are problematic, since they would convey completely the wrong message if such a system were deployed in a speech application. On the other hand, we show that a simple FST-based filter can mitigate those errors, and achieve a level of accuracy not achievable by the RNN alone. Though our conclusions are largely negative on this point, we are actually not arguing that the text normalization problem is intractable using an pure RNN approach, merely that it is not going to be something that can be solved merely by having huge amounts of annotated text data and feeding that to a general RNN model. And when we open-source our data, we will be providing a novel data set for sequence-to-sequence modeling in the hopes that the the community can find better solutions. The data used in this work have been released and are available at: https://github.com/rwsproat/text-normalization-data
Tasks
Published	2016-10-31
URL	http://arxiv.org/abs/1611.00068v2
PDF	http://arxiv.org/pdf/1611.00068v2.pdf
PWC	https://paperswithcode.com/paper/rnn-approaches-to-text-normalization-a
Repo	https://github.com/rwsproat/text-normalization-data
Framework	none

Towards Viewpoint Invariant 3D Human Pose Estimation


Title	Towards Viewpoint Invariant 3D Human Pose Estimation
Authors	Albert Haque, Boya Peng, Zelun Luo, Alexandre Alahi, Serena Yeung, Li Fei-Fei
Abstract	We propose a viewpoint invariant model for 3D human pose estimation from a single depth image. To achieve this, our discriminative model embeds local regions into a learned viewpoint invariant feature space. Formulated as a multi-task learning problem, our model is able to selectively predict partial poses in the presence of noise and occlusion. Our approach leverages a convolutional and recurrent network architecture with a top-down error feedback mechanism to self-correct previous pose estimates in an end-to-end manner. We evaluate our model on a previously published depth dataset and a newly collected human pose dataset containing 100K annotated depth images from extreme viewpoints. Experiments show that our model achieves competitive performance on frontal views while achieving state-of-the-art performance on alternate viewpoints.
Tasks	3D Human Pose Estimation, Multi-Task Learning, Pose Estimation
Published	2016-03-23
URL	http://arxiv.org/abs/1603.07076v3
PDF	http://arxiv.org/pdf/1603.07076v3.pdf
PWC	https://paperswithcode.com/paper/towards-viewpoint-invariant-3d-human-pose
Repo	https://github.com/mks0601/V2V-PoseNet_RELEASE
Framework	pytorch

A budget-constrained inverse classification framework for smooth classifiers


Title	A budget-constrained inverse classification framework for smooth classifiers
Authors	Michael T. Lash, Qihang Lin, W. Nick Street, Jennifer G. Robinson
Abstract	Inverse classification is the process of manipulating an instance such that it is more likely to conform to a specific class. Past methods that address such a problem have shortcomings. Greedy methods make changes that are overly radical, often relying on data that is strictly discrete. Other methods rely on certain data points, the presence of which cannot be guaranteed. In this paper we propose a general framework and method that overcomes these and other limitations. The formulation of our method can use any differentiable classification function. We demonstrate the method by using logistic regression and Gaussian kernel SVMs. We constrain the inverse classification to occur on features that can actually be changed, each of which incurs an individual cost. We further subject such changes to fall within a certain level of cumulative change (budget). Our framework can also accommodate the estimation of (indirectly changeable) features whose values change as a consequence of actions taken. Furthermore, we propose two methods for specifying feature-value ranges that result in different algorithmic behavior. We apply our method, and a proposed sensitivity analysis-based benchmark method, to two freely available datasets: Student Performance from the UCI Machine Learning Repository and a real world cardiovascular disease dataset. The results obtained demonstrate the validity and benefits of our framework and method.
Tasks
Published	2016-05-29
URL	http://arxiv.org/abs/1605.09068v3
PDF	http://arxiv.org/pdf/1605.09068v3.pdf
PWC	https://paperswithcode.com/paper/a-budget-constrained-inverse-classification
Repo	https://github.com/michael-lash/BCIC
Framework	none

A Systematic Approach to Blocking Convolutional Neural Networks


Title	A Systematic Approach to Blocking Convolutional Neural Networks
Authors	Xuan Yang, Jing Pu, Blaine Burton Rister, Nikhil Bhagdikar, Stephen Richardson, Shahar Kvatinsky, Jonathan Ragan-Kelley, Ardavan Pedram, Mark Horowitz
Abstract	Convolutional Neural Networks (CNNs) are the state of the art solution for many computer vision problems, and many researchers have explored optimized implementations. Most implementations heuristically block the computation to deal with the large data sizes and high data reuse of CNNs. This paper explores how to block CNN computations for memory locality by creating an analytical model for CNN-like loop nests. Using this model we automatically derive optimized blockings for common networks that improve the energy efficiency of custom hardware implementations by up to an order of magnitude. Compared to traditional CNN CPU implementations based on highly-tuned, hand-optimized BLAS libraries,our x86 programs implementing the optimal blocking reduce the number of memory accesses by up to 90%.
Tasks
Published	2016-06-14
URL	http://arxiv.org/abs/1606.04209v1
PDF	http://arxiv.org/pdf/1606.04209v1.pdf
PWC	https://paperswithcode.com/paper/a-systematic-approach-to-blocking
Repo	https://github.com/stanford-mast/nn_dataflow
Framework	none

TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games


Title	TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games
Authors	Gabriel Synnaeve, Nantas Nardelli, Alex Auvolat, Soumith Chintala, Timothée Lacroix, Zeming Lin, Florian Richoux, Nicolas Usunier
Abstract	We present TorchCraft, a library that enables deep learning research on Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it easier to control these games from a machine learning framework, here Torch. This white paper argues for using RTS games as a benchmark for AI research, and describes the design and components of TorchCraft.
Tasks	Real-Time Strategy Games, Starcraft
Published	2016-11-01
URL	http://arxiv.org/abs/1611.00625v2
PDF	http://arxiv.org/pdf/1611.00625v2.pdf
PWC	https://paperswithcode.com/paper/torchcraft-a-library-for-machine-learning
Repo	https://github.com/TorchCraft/TorchCraft
Framework	torch

Towards a Job Title Classification System


Title	Towards a Job Title Classification System
Authors	Faizan Javed, Matt McNair, Ferosh Jacob, Meng Zhao
Abstract	Document classification for text, images and other applicable entities has long been a focus of research in academia and also finds application in many industrial settings. Amidst a plethora of approaches to solve such problems, machine-learning techniques have found success in a variety of scenarios. In this paper we discuss the design of a machine learning-based semi-supervised job title classification system for the online job recruitment domain currently in production at CareerBuilder.com and propose enhancements to it. The system leverages a varied collection of classification as well clustering algorithms. These algorithms are encompassed in an architecture that facilitates leveraging existing off-the-shelf machine learning tools and techniques while keeping into consideration the challenges of constructing a scalable classification system for a large taxonomy of categories. As a continuously evolving system that is still under development we first discuss the existing semi-supervised classification system which is composed of both clustering and classification components in a proximity-based classifier setup and results of which are already used across numerous products at CareerBuilder. We then elucidate our long-term goals for job title classification and propose enhancements to the existing system in the form of a two-stage coarse and fine level classifier augmentation to construct a cascade of hierarchical vertical classifiers. Preliminary results are presented using experimental evaluation on real world industrial data.
Tasks	Document Classification
Published	2016-06-02
URL	http://arxiv.org/abs/1606.00917v1
PDF	http://arxiv.org/pdf/1606.00917v1.pdf
PWC	https://paperswithcode.com/paper/towards-a-job-title-classification-system
Repo	https://github.com/kmamykin/askamanager_salary_survey
Framework	none

Texture Synthesis with Spatial Generative Adversarial Networks


Title	Texture Synthesis with Spatial Generative Adversarial Networks
Authors	Nikolay Jetchev, Urs Bergmann, Roland Vollgraf
Abstract	Generative adversarial networks (GANs) are a recent approach to train generative models of data, which have been shown to work particularly well on image data. In the current paper we introduce a new model for texture synthesis based on GAN learning. By extending the input noise distribution space from a single vector to a whole spatial tensor, we create an architecture with properties well suited to the task of texture synthesis, which we call spatial GAN (SGAN). To our knowledge, this is the first successful completely data-driven texture synthesis method based on GANs. Our method has the following features which make it a state of the art algorithm for texture synthesis: high image quality of the generated textures, very high scalability w.r.t. the output texture size, fast real-time forward generation, the ability to fuse multiple diverse source images in complex textures. To illustrate these capabilities we present multiple experiments with different classes of texture images and use cases. We also discuss some limitations of our method with respect to the types of texture images it can synthesize, and compare it to other neural techniques for texture generation.
Tasks	Texture Synthesis
Published	2016-11-24
URL	http://arxiv.org/abs/1611.08207v4
PDF	http://arxiv.org/pdf/1611.08207v4.pdf
PWC	https://paperswithcode.com/paper/texture-synthesis-with-spatial-generative
Repo	https://github.com/zalandoresearch/spatial_gan
Framework	none

Deep Multi-fidelity Gaussian Processes


Title	Deep Multi-fidelity Gaussian Processes
Authors	Maziar Raissi, George Karniadakis
Abstract	We develop a novel multi-fidelity framework that goes far beyond the classical AR(1) Co-kriging scheme of Kennedy and O’Hagan (2000). Our method can handle general discontinuous cross-correlations among systems with different levels of fidelity. A combination of multi-fidelity Gaussian Processes (AR(1) Co-kriging) and deep neural networks enables us to construct a method that is immune to discontinuities. We demonstrate the effectiveness of the new technology using standard benchmark problems designed to resemble the outputs of complicated high- and low-fidelity codes.
Tasks	Gaussian Processes
Published	2016-04-26
URL	http://arxiv.org/abs/1604.07484v1
PDF	http://arxiv.org/pdf/1604.07484v1.pdf
PWC	https://paperswithcode.com/paper/deep-multi-fidelity-gaussian-processes
Repo	https://github.com/maziarraissi/TutorialGP
Framework	none

Incorporating Relation Paths in Neural Relation Extraction


Title	Incorporating Relation Paths in Neural Relation Extraction
Authors	Wenyuan Zeng, Yankai Lin, Zhiyuan Liu, Maosong Sun
Abstract	Distantly supervised relation extraction has been widely used to find novel relational facts from plain text. To predict the relation between a pair of two target entities, existing methods solely rely on those direct sentences containing both entities. In fact, there are also many sentences containing only one of the target entities, which provide rich and useful information for relation extraction. To address this issue, we build inference chains between two target entities via intermediate entities, and propose a path-based neural relation extraction model to encode the relational semantics from both direct sentences and inference chains. Experimental results on real-world datasets show that, our model can make full use of those sentences containing only one target entity, and achieves significant and consistent improvements on relation extraction as compared with baselines. The source code of this paper can be obtained from https: //github.com/thunlp/PathNRE.
Tasks	Relation Extraction
Published	2016-09-23
URL	http://arxiv.org/abs/1609.07479v3
PDF	http://arxiv.org/pdf/1609.07479v3.pdf
PWC	https://paperswithcode.com/paper/incorporating-relation-paths-in-neural
Repo	https://github.com/thunlp/PathNRE
Framework	none

DeepMath - Deep Sequence Models for Premise Selection


Title	DeepMath - Deep Sequence Models for Premise Selection
Authors	Alex A. Alemi, Francois Chollet, Niklas Een, Geoffrey Irving, Christian Szegedy, Josef Urban
Abstract	We study the effectiveness of neural sequence models for premise selection in automated theorem proving, one of the main bottlenecks in the formalization of mathematics. We propose a two stage approach for this task that yields good results for the premise selection task on the Mizar corpus while avoiding the hand-engineered features of existing state-of-the-art models. To our knowledge, this is the first time deep learning has been applied to theorem proving on a large scale.
Tasks	Automated Theorem Proving
Published	2016-06-14
URL	http://arxiv.org/abs/1606.04442v2
PDF	http://arxiv.org/pdf/1606.04442v2.pdf
PWC	https://paperswithcode.com/paper/deepmath-deep-sequence-models-for-premise
Repo	https://github.com/BartoszPiotrowski/ATPboost
Framework	none

Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads


Title	Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads
Authors	Ji He, Mari Ostendorf, Xiaodong He, Jianshu Chen, Jianfeng Gao, Lihong Li, Li Deng
Abstract	We introduce an online popularity prediction and tracking task as a benchmark task for reinforcement learning with a combinatorial, natural language action space. A specified number of discussion threads predicted to be popular are recommended, chosen from a fixed window of recent comments to track. Novel deep reinforcement learning architectures are studied for effective modeling of the value function associated with actions comprised of interdependent sub-actions. The proposed model, which represents dependence between sub-actions through a bi-directional LSTM, gives the best performance across different experimental configurations and domains, and it also generalizes well with varying numbers of recommendation requests.
Tasks
Published	2016-06-12
URL	http://arxiv.org/abs/1606.03667v4
PDF	http://arxiv.org/pdf/1606.03667v4.pdf
PWC	https://paperswithcode.com/paper/deep-reinforcement-learning-with-a
Repo	https://github.com/jvking/reddit-RL-simulator
Framework	none

AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification


Title	AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification
Authors	Depeng Liang, Yongdong Zhang
Abstract	Recently deeplearning models have been shown to be capable of making remarkable performance in sentences and documents classification tasks. In this work, we propose a novel framework called AC-BLSTM for modeling sentences and documents, which combines the asymmetric convolution neural network (ACNN) with the Bidirectional Long Short-Term Memory network (BLSTM). Experiment results demonstrate that our model achieves state-of-the-art results on five tasks, including sentiment analysis, question type classification, and subjectivity classification. In order to further improve the performance of AC-BLSTM, we propose a semi-supervised learning framework called G-AC-BLSTM for text classification by combining the generative model with AC-BLSTM.
Tasks	Sentence Embeddings, Sentiment Analysis, Text Classification
Published	2016-11-07
URL	http://arxiv.org/abs/1611.01884v3
PDF	http://arxiv.org/pdf/1611.01884v3.pdf
PWC	https://paperswithcode.com/paper/ac-blstm-asymmetric-convolutional
Repo	https://github.com/Ldpe2G/AC-BLSTM
Framework	mxnet