May 6, 2019

3339 words 16 mins read

Paper Group ANR 400

Predicting Personal Traits from Facial Images using Convolutional Neural Networks Augmented with Facial Landmark Information. Forecasting Framework for Open Access Time Series in Energy. Training a Probabilistic Graphical Model with Resistive Switching Electronic Synapses. Nonparametric Bayesian inference of the microcanonical stochastic block mode …

Predicting Personal Traits from Facial Images using Convolutional Neural Networks Augmented with Facial Landmark Information


Title	Predicting Personal Traits from Facial Images using Convolutional Neural Networks Augmented with Facial Landmark Information
Authors	Yoad Lewenberg, Yoram Bachrach, Sukrit Shankar, Antonio Criminisi
Abstract	We consider the task of predicting various traits of a person given an image of their face. We estimate both objective traits, such as gender, ethnicity and hair-color; as well as subjective traits, such as the emotion a person expresses or whether he is humorous or attractive. For sizeable experimentation, we contribute a new Face Attributes Dataset (FAD), having roughly 200,000 attribute labels for the above traits, for over 10,000 facial images. Due to the recent surge of research on Deep Convolutional Neural Networks (CNNs), we begin by using a CNN architecture for estimating facial attributes and show that they indeed provide an impressive baseline performance. To further improve performance, we propose a novel approach that incorporates facial landmark information for input images as an additional channel, helping the CNN learn better attribute-specific features so that the landmarks across various training images hold correspondence. We empirically analyse the performance of our method, showing consistent improvement over the baseline across traits.
Tasks
Published	2016-05-29
URL	http://arxiv.org/abs/1605.09062v1
PDF	http://arxiv.org/pdf/1605.09062v1.pdf
PWC	https://paperswithcode.com/paper/predicting-personal-traits-from-facial-images
Repo
Framework

Forecasting Framework for Open Access Time Series in Energy


Title	Forecasting Framework for Open Access Time Series in Energy
Authors	Gergo Barta, Gabor Nagy, Gabor Simon, Gyozo Papp
Abstract	In this paper we propose a framework for automated forecasting of energy-related time series using open access data from European Network of Transmission System Operators for Electricity (ENTSO-E). The framework provides forecasts for various European countries using publicly available historical data only. Our solution was benchmarked using the actual load data and the country provided estimates (where available). We conclude that the proposed system can produce timely forecasts with comparable prediction accuracy in a number of cases. We also investigate the probabilistic case of forecasting - that is, providing a probability distribution rather than a simple point forecast - and incorporate it into a web based API that provides quick and easy access to reliable forecasts.
Tasks	Time Series
Published	2016-06-02
URL	http://arxiv.org/abs/1606.00656v1
PDF	http://arxiv.org/pdf/1606.00656v1.pdf
PWC	https://paperswithcode.com/paper/forecasting-framework-for-open-access-time
Repo
Framework

Training a Probabilistic Graphical Model with Resistive Switching Electronic Synapses


Title	Training a Probabilistic Graphical Model with Resistive Switching Electronic Synapses
Authors	S. Burc Eryilmaz, Emre Neftci, Siddharth Joshi, SangBum Kim, Matthew BrightSky, Hsiang-Lan Lung, Chung Lam, Gert Cauwenberghs, H. -S. Philip Wong
Abstract	Current large scale implementations of deep learning and data mining require thousands of processors, massive amounts of off-chip memory, and consume gigajoules of energy. Emerging memory technologies such as nanoscale two-terminal resistive switching memory devices offer a compact, scalable and low power alternative that permits on-chip co-located processing and memory in fine-grain distributed parallel architecture. Here we report first use of resistive switching memory devices for implementing and training a Restricted Boltzmann Machine (RBM), a generative probabilistic graphical model as a key component for unsupervised learning in deep networks. We experimentally demonstrate a 45-synapse RBM realized with 90 resistive switching phase change memory (PCM) elements trained with a bio-inspired variant of the Contrastive Divergence (CD) algorithm, implementing Hebbian and anti-Hebbian weight updates. The resistive PCM devices show a two-fold to ten-fold reduction in error rate in a missing pixel pattern completion task trained over 30 epochs, compared to untrained case. Measured programming energy consumption is 6.1 nJ per epoch with the resistive switching PCM devices, a factor of ~150 times lower than conventional processor-memory systems. We analyze and discuss the dependence of learning performance on cycle-to-cycle variations as well as number of gradual levels in the PCM analog memory devices.
Tasks
Published	2016-09-27
URL	http://arxiv.org/abs/1609.08686v2
PDF	http://arxiv.org/pdf/1609.08686v2.pdf
PWC	https://paperswithcode.com/paper/training-a-probabilistic-graphical-model-with
Repo
Framework

Nonparametric Bayesian inference of the microcanonical stochastic block model


Title	Nonparametric Bayesian inference of the microcanonical stochastic block model
Authors	Tiago P. Peixoto
Abstract	A principled approach to characterize the hidden structure of networks is to formulate generative models, and then infer their parameters from data. When the desired structure is composed of modules or “communities”, a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints, i.e. the generated networks are not allowed to violate the patterns imposed by the model. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: 1. Deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, that not only remove limitations that seriously degrade the inference on large networks, but also reveal structures at multiple scales; 2. A very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges, but also with an unlimited number of modules. We show also how this approach can be used to sample modular hierarchies from the posterior distribution, as well as to perform model selection. We discuss and analyze the differences between sampling from the posterior and simply finding the single parameter estimate that maximizes it. Furthermore, we expose a direct equivalence between our microcanonical approach and alternative derivations based on the canonical SBM.
Tasks	Bayesian Inference, Model Selection
Published	2016-10-09
URL	http://arxiv.org/abs/1610.02703v4
PDF	http://arxiv.org/pdf/1610.02703v4.pdf
PWC	https://paperswithcode.com/paper/nonparametric-bayesian-inference-of-the
Repo
Framework

Automatically Reinforcing a Game AI


Title	Automatically Reinforcing a Game AI
Authors	David L. St-Pierre, Jean-Baptiste Hoock, Jialin Liu, Fabien Teytaud, Olivier Teytaud
Abstract	A recent research trend in Artificial Intelligence (AI) is the combination of several programs into one single, stronger, program; this is termed portfolio methods. We here investigate the application of such methods to Game Playing Programs (GPPs). In addition, we consider the case in which only one GPP is available - by decomposing this single GPP into several ones through the use of parameters or even simply random seeds. These portfolio methods are trained in a learning phase. We propose two different offline approaches. The simplest one, BestArm, is a straightforward optimization of seeds or parame- ters; it performs quite well against the original GPP, but performs poorly against an opponent which repeats games and learns. The second one, namely Nash-portfolio, performs similarly in a “one game” test, and is much more robust against an opponent who learns. We also propose an online learning portfolio, which tests several of the GPP repeatedly and progressively switches to the best one - using a bandit algorithm.
Tasks
Published	2016-07-27
URL	http://arxiv.org/abs/1607.08100v1
PDF	http://arxiv.org/pdf/1607.08100v1.pdf
PWC	https://paperswithcode.com/paper/automatically-reinforcing-a-game-ai
Repo
Framework

Estimating parameters of nonlinear systems using the elitist particle filter based on evolutionary strategies


Title	Estimating parameters of nonlinear systems using the elitist particle filter based on evolutionary strategies
Authors	Christian Huemmer, Christian Hofmann, Roland Maas, Walter Kellermann
Abstract	In this article, we present the elitist particle filter based on evolutionary strategies (EPFES) as an efficient approach for nonlinear system identification. The EPFES is derived from the frequently-employed state-space model, where the relevant information of the nonlinear system is captured by an unknown state vector. Similar to classical particle filtering, the EPFES consists of a set of particles and respective weights which represent different realizations of the latent state vector and their likelihood of being the solution of the optimization problem. As main innovation, the EPFES includes an evolutionary elitist-particle selection which combines long-term information with instantaneous sampling from an approximated continuous posterior distribution. In this article, we propose two advancements of the previously-published elitist-particle selection process. Further, the EPFES is shown to be a generalization of the widely-used Gaussian particle filter and thus evaluated with respect to the latter for two completely different scenarios: First, we consider the so-called univariate nonstationary growth model with time-variant latent state variable, where the evolutionary selection of elitist particles is evaluated for non-recursively calculated particle weights. Second, the problem of nonlinear acoustic echo cancellation is addressed in a simulated scenario with speech as input signal: By using long-term fitness measures, we highlight the efficacy of the well-generalizing EPFES in estimating the nonlinear system even for large search spaces. Finally, we illustrate similarities between the EPFES and evolutionary algorithms to outline future improvements by fusing the achievements of both fields of research.
Tasks
Published	2016-04-14
URL	http://arxiv.org/abs/1604.04198v4
PDF	http://arxiv.org/pdf/1604.04198v4.pdf
PWC	https://paperswithcode.com/paper/estimating-parameters-of-nonlinear-systems
Repo
Framework

Minimax Lower Bounds for Realizable Transductive Classification


Title	Minimax Lower Bounds for Realizable Transductive Classification
Authors	Ilya Tolstikhin, David Lopez-Paz
Abstract	Transductive learning considers a training set of $m$ labeled samples and a test set of $u$ unlabeled samples, with the goal of best labeling that particular test set. Conversely, inductive learning considers a training set of $m$ labeled samples drawn iid from $P(X,Y)$, with the goal of best labeling any future samples drawn iid from $P(X)$. This comparison suggests that transduction is a much easier type of inference than induction, but is this really the case? This paper provides a negative answer to this question, by proving the first known minimax lower bounds for transductive, realizable, binary classification. Our lower bounds show that $m$ should be at least $\Omega(d/\epsilon + \log(1/\delta)/\epsilon)$ when $\epsilon$-learning a concept class $\mathcal{H}$ of finite VC-dimension $d<\infty$ with confidence $1-\delta$, for all $m \leq u$. This result draws three important conclusions. First, general transduction is as hard as general induction, since both problems have $\Omega(d/m)$ minimax values. Second, the use of unlabeled data does not help general transduction, since supervised learning algorithms such as ERM and (Hanneke, 2015) match our transductive lower bounds while ignoring the unlabeled test set. Third, our transductive lower bounds imply lower bounds for semi-supervised learning, which add to the important discussion about the role of unlabeled data in machine learning.
Tasks
Published	2016-02-09
URL	http://arxiv.org/abs/1602.03027v1
PDF	http://arxiv.org/pdf/1602.03027v1.pdf
PWC	https://paperswithcode.com/paper/minimax-lower-bounds-for-realizable
Repo
Framework

Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks


Title	Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks
Authors	Samuli Laine, Tero Karras, Timo Aila, Antti Herva, Shunsuke Saito, Ronald Yu, Hao Li, Jaakko Lehtinen
Abstract	We present a real-time deep learning framework for video-based facial performance capture – the dense 3D tracking of an actor’s face given a monocular video. Our pipeline begins with accurately capturing a subject using a high-end production facial capture pipeline based on multi-view stereo tracking and artist-enhanced animations. With 5-10 minutes of captured footage, we train a convolutional neural network to produce high-quality output, including self-occluded regions, from a monocular video sequence of that subject. Since this 3D facial performance capture is fully automated, our system can drastically reduce the amount of labor involved in the development of modern narrative-driven video games or films involving realistic digital doubles of actors and potentially hours of animated dialogue per character. We compare our results with several state-of-the-art monocular real-time facial capture techniques and demonstrate compelling animation inference in challenging areas such as eyes and lips.
Tasks
Published	2016-09-21
URL	http://arxiv.org/abs/1609.06536v2
PDF	http://arxiv.org/pdf/1609.06536v2.pdf
PWC	https://paperswithcode.com/paper/production-level-facial-performance-capture
Repo
Framework

A Consistent Regularization Approach for Structured Prediction


Title	A Consistent Regularization Approach for Structured Prediction
Authors	Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco
Abstract	We propose and analyze a regularization approach for structured prediction problems. We characterize a large class of loss functions that allows to naturally embed structured outputs in a linear space. We exploit this fact to design learning algorithms using a surrogate loss approach and regularization techniques. We prove universal consistency and finite sample bounds characterizing the generalization properties of the proposed methods. Experimental results are provided to demonstrate the practical usefulness of the proposed approach.
Tasks	Structured Prediction
Published	2016-05-24
URL	http://arxiv.org/abs/1605.07588v3
PDF	http://arxiv.org/pdf/1605.07588v3.pdf
PWC	https://paperswithcode.com/paper/a-consistent-regularization-approach-for
Repo
Framework

A Self-Driving Robot Using Deep Convolutional Neural Networks on Neuromorphic Hardware


Title	A Self-Driving Robot Using Deep Convolutional Neural Networks on Neuromorphic Hardware
Authors	Tiffany Hwu, Jacob Isbell, Nicolas Oros, Jeffrey Krichmar
Abstract	Neuromorphic computing is a promising solution for reducing the size, weight and power of mobile embedded systems. In this paper, we introduce a realization of such a system by creating the first closed-loop battery-powered communication system between an IBM TrueNorth NS1e and an autonomous Android-Based Robotics platform. Using this system, we constructed a dataset of path following behavior by manually driving the Android-Based robot along steep mountain trails and recording video frames from the camera mounted on the robot along with the corresponding motor commands. We used this dataset to train a deep convolutional neural network implemented on the TrueNorth NS1e. The NS1e, which was mounted on the robot and powered by the robot’s battery, resulted in a self-driving robot that could successfully traverse a steep mountain path in real time. To our knowledge, this represents the first time the TrueNorth NS1e neuromorphic chip has been embedded on a mobile platform under closed-loop control.
Tasks
Published	2016-11-04
URL	http://arxiv.org/abs/1611.01235v1
PDF	http://arxiv.org/pdf/1611.01235v1.pdf
PWC	https://paperswithcode.com/paper/a-self-driving-robot-using-deep-convolutional
Repo
Framework

Perceptual uniform descriptor and Ranking on manifold: A bridge between image representation and ranking for image retrieval


Title	Perceptual uniform descriptor and Ranking on manifold: A bridge between image representation and ranking for image retrieval
Authors	Shenglan Liu, Jun Wu, Lin Feng, Yang Liu, Hong Qiao, Wenbo Luo Muxin Sun, Wei Wang
Abstract	Incompatibility of image descriptor and ranking is always neglected in image retrieval. In this paper, manifold learning and Gestalt psychology theory are involved to solve the incompatibility problem. A new holistic descriptor called Perceptual Uniform Descriptor (PUD) based on Gestalt psychology is proposed, which combines color and gradient direction to imitate the human visual uniformity. PUD features in the same class images distributes on one manifold in most cases because PUD improves the visual uniformity of the traditional descriptors. Thus, we use manifold ranking and PUD to realize image retrieval. Experiments were carried out on five benchmark data sets, and the proposed method can greatly improve the accuracy of image retrieval. Our experimental results in the Ukbench and Corel-1K datasets demonstrated that N-S score reached to 3.58 (HSV 3.4) and mAP to 81.77% (ODBTC 77.9%) respectively by utilizing PUD which has only 280 dimension. The results are higher than other holistic image descriptors (even some local ones) and state-of-the-arts retrieval methods.
Tasks	Image Retrieval
Published	2016-09-24
URL	http://arxiv.org/abs/1609.07615v1
PDF	http://arxiv.org/pdf/1609.07615v1.pdf
PWC	https://paperswithcode.com/paper/perceptual-uniform-descriptor-and-ranking-on
Repo
Framework

A novel multiclassSVM based framework to classify lithology from well logs: a real-world application


Title	A novel multiclassSVM based framework to classify lithology from well logs: a real-world application
Authors	Soumi Chaki, Aurobinda Routray, William K. Mohanty, Mamata Jenamani
Abstract	Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one or one-against-all strategies. In this paper, an attempt has been made to classify lithology using a multiclass SVM based framework using well logs as predictor variables. Here, the lithology is classified into four classes such as sand, shaly sand, sandy shale and shale based on the relative values of sand and shale fractions as suggested by an expert geologist. The available dataset consisting well logs (gamma ray, neutron porosity, density, and P-sonic) and class information from four closely spaced wells from an onshore hydrocarbon field is divided into training and testing sets. We have used one-against-all strategy to combine the results of multiple binary classifiers. The reported results established the superiority of multiclass SVM compared to other classifiers in terms of classification accuracy. The selection of kernel function and associated parameters has also been investigated here. It can be envisaged from the results achieved in this study that the proposed framework based on multiclass SVM can further be used to solve classification problems. In future research endeavor, seismic attributes can be introduced in the framework to classify the lithology throughout a study area from seismic inputs.
Tasks
Published	2016-12-02
URL	http://arxiv.org/abs/1612.00840v1
PDF	http://arxiv.org/pdf/1612.00840v1.pdf
PWC	https://paperswithcode.com/paper/a-novel-multiclasssvm-based-framework-to
Repo
Framework

Sentence Similarity Measures for Fine-Grained Estimation of Topical Relevance in Learner Essays


Title	Sentence Similarity Measures for Fine-Grained Estimation of Topical Relevance in Learner Essays
Authors	Marek Rei, Ronan Cummins
Abstract	We investigate the task of assessing sentence-level prompt relevance in learner essays. Various systems using word overlap, neural embeddings and neural compositional models are evaluated on two datasets of learner writing. We propose a new method for sentence-level similarity calculation, which learns to adjust the weights of pre-trained word embeddings for a specific task, achieving substantially higher accuracy compared to other relevant baselines.
Tasks	Word Embeddings
Published	2016-06-09
URL	http://arxiv.org/abs/1606.03144v1
PDF	http://arxiv.org/pdf/1606.03144v1.pdf
PWC	https://paperswithcode.com/paper/sentence-similarity-measures-for-fine-grained
Repo
Framework

Understanding Neural Networks through Representation Erasure


Title	Understanding Neural Networks through Representation Erasure
Authors	Jiwei Li, Will Monroe, Dan Jurafsky
Abstract	While neural networks have been successfully applied to many natural language processing tasks, they come at the cost of interpretability. In this paper, we propose a general methodology to analyze and interpret decisions from a neural model by observing the effects on the model of erasing various parts of the representation, such as input word-vector dimensions, intermediate hidden units, or input words. We present several approaches to analyzing the effects of such erasure, from computing the relative difference in evaluation metrics, to using reinforcement learning to erase the minimum set of input words in order to flip a neural model’s decision. In a comprehensive analysis of multiple NLP tasks, including linguistic feature classification, sentence-level sentiment analysis, and document level sentiment aspect prediction, we show that the proposed methodology not only offers clear explanations about neural model decisions, but also provides a way to conduct error analysis on neural models.
Tasks	Sentiment Analysis
Published	2016-12-24
URL	http://arxiv.org/abs/1612.08220v3
PDF	http://arxiv.org/pdf/1612.08220v3.pdf
PWC	https://paperswithcode.com/paper/understanding-neural-networks-through
Repo
Framework

Online Unsupervised Multi-view Feature Selection


Title	Online Unsupervised Multi-view Feature Selection
Authors	Weixiang Shao, Lifang He, Chun-Ta Lu, Xiaokai Wei, Philip S. Yu
Abstract	In the era of big data, it is becoming common to have data with multiple modalities or coming from multiple sources, known as “multi-view data”. Multi-view data are usually unlabeled and come from high-dimensional spaces (such as language vocabularies), unsupervised multi-view feature selection is crucial to many applications. However, it is nontrivial due to the following challenges. First, there are too many instances or the feature dimensionality is too large. Thus, the data may not fit in memory. How to select useful features with limited memory space? Second, how to select features from streaming data and handles the concept drift? Third, how to leverage the consistent and complementary information from different views to improve the feature selection in the situation when the data are too big or come in as streams? To the best of our knowledge, none of the previous works can solve all the challenges simultaneously. In this paper, we propose an Online unsupervised Multi-View Feature Selection, OMVFS, which deals with large-scale/streaming multi-view data in an online fashion. OMVFS embeds unsupervised feature selection into a clustering algorithm via NMF with sparse learning. It further incorporates the graph regularization to preserve the local structure information and help select discriminative features. Instead of storing all the historical data, OMVFS processes the multi-view data chunk by chunk and aggregates all the necessary information into several small matrices. By using the buffering technique, the proposed OMVFS can reduce the computational and storage cost while taking advantage of the structure information. Furthermore, OMVFS can capture the concept drifts in the data streams. Extensive experiments on four real-world datasets show the effectiveness and efficiency of the proposed OMVFS method. More importantly, OMVFS is about 100 times faster than the off-line methods.
Tasks	Feature Selection, Sparse Learning
Published	2016-09-27
URL	http://arxiv.org/abs/1609.08286v1
PDF	http://arxiv.org/pdf/1609.08286v1.pdf
PWC	https://paperswithcode.com/paper/online-unsupervised-multi-view-feature
Repo
Framework