pwc-archive/papers-with-abstracts huggingface dataset:
Two-Stream Region Convolutional 3D Network for Temporal Activity Detection
Authors: Huijuan Xu, Abir Das, Kate Saenko
Date: 2019-06-05 00:00:00
Tasks: Action Detection, Action Recognition, Activity Detection, General Classification, Optical Flow Estimation, Vocal Bursts Valence Prediction

We address the problem of temporal activity detection in continuous, untrimmed video streams. This is a difficult task that requires extracting meaningful spatio-temporal features to capture activities, accurately localizing the start and end times of each activity. We introduce a new model, Region Convolutional 3D Network (R-C3D), which encodes the video streams using a three-dimensional fully convolutional network, then generates candidate temporal regions containing activities and finally classifies selected regions into specific activities. Computation is saved due to the sharing of convolutional features between the proposal and the classification pipelines. We further improve the detection performance by efficiently integrating an optical flow based motion stream with the original RGB stream. The two-stream network is jointly optimized by fusing the flow and RGB feature maps at different levels. Additionally, the training stage incorporates an online hard example mining strategy to address the extreme foreground-background imbalance typically observed in any detection pipeline. Instead of heuristically sampling the candidate segments for the final activity classification stage, we rank them according to their performance and only select the worst performers to update the model. This improves the model without heavy hyper-parameter tuning. Extensive experiments on three benchmark datasets are carried out to show superior performance over existing temporal activity detection methods. Our model achieves state-of-the-art results on the THUMOS'14 and Charades datasets. We further demonstrate that our model is a general temporal activity detection framework that does not rely on assumptions about particular dataset properties by evaluating our approach on the ActivityNet dataset.
Manifold-regression to predict from MEG/EEG brain signals without source modeling
Authors: David Sabbagh, Pierre Ablin, Gael Varoquaux, Alexandre Gramfort, Denis A. Engemann
Date: 2019-06-04 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: EEG, Electroencephalogram (EEG), regression, Time Series Analysis

Magnetoencephalography and electroencephalography (M/EEG) can reveal neuronal dynamics non-invasively in real-time and are therefore appreciated methods in medicine and neuroscience. Recent advances in modeling brain-behavior relationships have highlighted the effectiveness of Riemannian geometry for summarizing the spatially correlated time-series from M/EEG in terms of their covariance. However, after artefact-suppression, M/EEG data is often rank deficient which limits the application of Riemannian concepts. In this article, we focus on the task of regression with rank-reduced covariance matrices. We study two Riemannian approaches that vectorize the M/EEG covariance between-sensors through projection into a tangent space. The Wasserstein distance readily applies to rank-reduced data but lacks affine-invariance. This can be overcome by finding a common subspace in which the covariance matrices are full rank, enabling the affine-invariant geometric distance. We investigated the implications of these two approaches in synthetic generative models, which allowed us to control estimation bias of a linear model for prediction. We show that Wasserstein and geometric distances allow perfect out-of-sample prediction on the generative models. We then evaluated the methods on real data with regard to their effectiveness in predicting age from M/EEG covariance matrices. The findings suggest that the data-driven Riemannian methods outperform different sensor-space estimators and that they get close to the performance of biophysics-driven source-localization model that requires MRI acquisitions and tedious data processing. Our study suggests that the proposed Riemannian methods can serve as fundamental building-blocks for automated large-scale analysis of M/EEG.
Bayesian Active Learning With Abstention Feedbacks
Authors: Cuong V. Nguyen, Lam Si Tung Ho, Huan Xu, Vu Dinh, Binh Nguyen
Date: 2019-06-04 00:00:00
Tasks: Active Learning, General Classification

We study pool-based active learning with abstention feedbacks where a labeler can abstain from labeling a queried example with some unknown abstention rate. This is an important problem with many useful applications. We take a Bayesian approach to the problem and develop two new greedy algorithms that learn both the classification problem and the unknown abstention rate at the same time. These are achieved by simply incorporating the estimated average abstention rate into the greedy criteria. We prove that both algorithms have near-optimality guarantees: they respectively achieve a ${(1-\frac{1}{e})}$ constant factor approximation of the optimal expected or worst-case value of a useful utility function. Our experiments show the algorithms perform well in various practical scenarios.
Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning
Authors: Nicholas Waytowich, Sean L. Barton, Vernon Lawhern, Ethan Stump, Garrett Warnell
Date: 2019-04-24 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft, Starcraft II

While deep reinforcement learning techniques have led to agents that are
successfully able to learn to perform a number of tasks that had been
previously unlearnable, these techniques are still susceptible to the
longstanding problem of {\em reward sparsity}. This is especially true for
tasks such as training an agent to play StarCraft II, a real-time strategy game
where reward is only given at the end of a game which is usually very long.
While this problem can be addressed through reward shaping, such approaches
typically require a human expert with specialized knowledge. Inspired by the
vision of enabling reward shaping through the more-accessible paradigm of
natural-language narration, we investigate to what extent we can contextualize
these narrations by grounding them to the goal-specific states. We present a
mutual-embedding model using a multi-input deep-neural network that projects a
sequence of natural language commands into the same high-dimensional
representation space as corresponding goal states. We show that using this
model we can learn an embedding space with separable and distinct clusters that
accurately maps natural-language commands to corresponding game states . We
also discuss how this model can allow for the use of narrations as a robust
form of reward shaping to improve RL performance and efficiency.
Linear and Quadratic Discriminant Analysis: Tutorial
Authors: Benyamin Ghojogh, Mark Crowley
Date: 2019-06-01 00:00:00
Tasks: Dimensionality Reduction, General Classification, Metric Learning, regression

This tutorial explains Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) as two fundamental classification methods in statistical and probabilistic learning. We start with the optimization of decision boundary on which the posteriors are equal. Then, LDA and QDA are derived for binary and multiple classes. The estimation of parameters in LDA and QDA are also covered. Then, we explain how LDA and QDA are related to metric learning, kernel principal component analysis, Mahalanobis distance, logistic regression, Bayes optimal classifier, Gaussian naive Bayes, and likelihood ratio test. We also prove that LDA and Fisher discriminant analysis are equivalent. We finally clarify some of the theoretical concepts with simulations we provide.
A Natural Language-Inspired Multi-label Video Streaming Traffic Classification Method Based on Deep Neural Networks
Authors: Yan Shi, Dezhi Feng, Subir Biswas
Date: 2019-06-04 00:00:00
Tasks: Classification, General Classification, Traffic Classification, Zero-Shot Learning

This paper presents a deep-learning based traffic classification method for identifying multiple streaming video sources at the same time within an encrypted tunnel. The work defines a novel feature inspired by Natural Language Processing (NLP) that allows existing NLP techniques to help the traffic classification. The feature extraction method is described, and a large dataset containing video streaming and web traffic is created to verify its effectiveness. Results are obtained by applying several NLP methods to show that the proposed method performs well on both binary and multilabel traffic classification problems. We also show the ability to achieve zero-shot learning with the proposed method.
An Information-Theoretical Approach to the Information Capacity and Cost-Effectiveness Evaluation of Color Palettes
Authors: R. Tanju Sirmen, B. Burak Ustundag
Date: 2019-06-03 00:00:00

Colors are used as effective tools of representing and transferring information. Number of colors in a palette is the direct arbiter of the information conveying capacity. Yet it should be well elaborated, since increasing the entropy by adding colors comes with its cost on decoding. Despite the possible effects upon diverse applications, a methodology for cost-effectiveness evaluation of palettes seems deficient. In this work, this need is being addressed from an information-theoretical perspective, via the articulated metrics and formulae. Besides, the proposed metrics are computed for some developed and known palettes, and observed results are evaluated.
Topological Data Analysis of Task-Based fMRI Data from Experiments on Schizophrenia
Authors: Bernadette J. Stolz, Tegan Emerson, Satu Nahkuri, Mason A. Porter, Heather A. Harrington
Date: 2018-09-22 00:00:00
Tasks: Clustering, Community Detection, Time Series Analysis, Topological Data Analysis

We use methods from computational algebraic topology to study functional brain networks, in which nodes represent brain regions and weighted edges encode the similarity of fMRI time series from each region. With these tools, which allow one to characterize topological invariants such as loops in high-dimensional data, we are able to gain understanding into low-dimensional structures in networks in a way that complements traditional approaches that are based on pairwise interactions. In the present paper, we use persistent homology to analyze networks that we construct from task-based fMRI data from schizophrenia patients, healthy controls, and healthy siblings of schizophrenia patients. We thereby explore the persistence of topological structures such as loops at different scales in these networks. We use persistence landscapes and persistence images to create output summaries from our persistent-homology calculations, and we study the persistence landscapes and images using $k$-means clustering and community detection. Based on our analysis of persistence landscapes, we find that the members of the sibling cohort have topological features (specifically, their 1-dimensional loops) that are distinct from the other two cohorts. From the persistence images, we are able to distinguish all three subject groups and to determine the brain regions in the loops (with four or more edges) that allow us to make these distinctions.
Neural IR Meets Graph Embedding: A Ranking Model for Product Search
Authors: Yuan Zhang, Dong Wang, Yan Zhang
Date: 2019-01-24 00:00:00
Tasks: Graph Embedding, Information Retrieval, Learning-To-Rank, Retrieval

Recently, neural models for information retrieval are becoming increasingly
popular. They provide effective approaches for product search due to their
competitive advantages in semantic matching. However, it is challenging to use
graph-based features, though proved very useful in IR literature, in these
neural approaches. In this paper, we leverage the recent advances in graph
embedding techniques to enable neural retrieval models to exploit
graph-structured data for automatic feature extraction. The proposed approach
can not only help to overcome the long-tail problem of click-through data, but
also incorporate external heterogeneous information to improve search results.
Extensive experiments on a real-world e-commerce dataset demonstrate
significant improvement achieved by our proposed approach over multiple strong
baselines both as an individual retrieval model and as a feature used in
learning-to-rank frameworks.
Thumbs up? Sentiment Classification using Machine Learning Techniques
Authors: Bo Pang, Lillian Lee, Shivakumar Vaithyanathan
Date: 2002-05-28 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Sentiment Analysis, Sentiment Classification

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging.
Unsupervised Compositionality Prediction of Nominal Compounds
Authors: Silvio Cordeiro, Aline Villavicencio, Marco Idiart, Carlos Ramisch
Date: 2019-03-01 00:00:00
Proceeding: CL 2019 3
Tasks: Prediction

Nominal compounds such as red wine and nut case display a continuum of compositionality, with varying contributions from the components of the compound to its semantics. This article proposes a framework for compound compositionality prediction using distributional semantic models, evaluating to what extent they capture idiomaticity compared to human judgments. For evaluation, we introduce data sets containing human judgments in three languages: English, French, and Portuguese. The results obtained reveal a high agreement between the models and human predictions, suggesting that they are able to incorporate information about idiomaticity. We also present an in-depth evaluation of various factors that can affect prediction, such as model and corpus parameters and compositionality operations. General crosslingual analyses reveal the impact of morphological variation and corpus size in the ability of the model to predict compositionality, and of a uniform combination of the components for best results.
Parsing Chinese Sentences with Grammatical Relations
Authors: Weiwei Sun, Yufei Chen, Xiaojun Wan, Meichun Liu
Date: 2019-03-01 00:00:00
Proceeding: CL 2019 3

We report our work on building linguistic resources and data-driven parsers in the grammatical relation (GR) analysis for Mandarin Chinese. Chinese, as an analytic language, encodes grammatical information in a highly configurational rather than morphological way. Accordingly, it is possible and reasonable to represent almost all grammatical relations as bilexical dependencies. In this work, we propose to represent grammatical information using general directed dependency graphs. Both only-local and rich long-distance dependencies are explicitly represented. To create high-quality annotations, we take advantage of an existing TreeBank, namely, Chinese TreeBank (CTB), which is grounded on the Government and Binding theory. We define a set of linguistic rules to explore CTB{'}s implicit phrase structural information and build deep dependency graphs. The reliability of this linguistically motivated GR extraction procedure is highlighted by manual evaluation. Based on the converted corpus, data-driven, including graph- and transition-based, models are explored for Chinese GR parsing. For graph-based parsing, a new perspective, graph merging, is proposed for building flexible dependency graphs: constructing complex graphs via constructing simple subgraphs. Two key problems are discussed in this perspective: (1) how to decompose a complex graph into simple subgraphs, and (2) how to combine subgraphs into a coherent complex graph. For transition-based parsing, we introduce a neural parser based on a list-based transition system. We also discuss several other key problems, including dynamic oracle and beam search for neural transition-based parsing. Evaluation gauges how successful GR parsing for Chinese can be by applying data-driven models. The empirical analysis suggests several directions for future study.
Automatic Inference of Sound Correspondence Patterns across Multiple Languages
Authors: Johann-Mattis List
Date: 2019-03-01 00:00:00
Proceeding: CL 2019 3

Sound correspondence patterns play a crucial role for linguistic reconstruction. Linguists use them to prove language relationship, to reconstruct proto-forms, and for classical phylogenetic reconstruction based on shared innovations. Cognate words that fail to conform with expected patterns can further point to various kinds of exceptions in sound change, such as analogy or assimilation of frequent words. Here I present an automatic method for the inference of sound correspondence patterns across multiple languages based on a network approach. The core idea is to represent all columns in aligned cognate sets as nodes in a network with edges representing the degree of compatibility between the nodes. The task of inferring all compatible correspondence sets can then be handled as the well-known minimum clique cover problem in graph theory, which essentially seeks to split the graph into the smallest number of cliques in which each node is represented by exactly one clique. The resulting partitions represent all correspondence patterns that can be inferred for a given data set. By excluding those patterns that occur in only a few cognate sets, the core of regularly recurring sound correspondences can be inferred. Based on this idea, the article presents a method for automatic correspondence pattern recognition, which is implemented as part of a Python library which supplements the article. To illustrate the usefulness of the method, I present how the inferred patterns can be used to predict words that have not been observed before.
KnightCap: A chess program that learns by combining TD(lambda) with game-tree search
Authors: Jonathan Baxter, Andrew Tridgell, Lex Weaver
Date: 1999-01-10 00:00:00

In this paper we present TDLeaf(lambda), a variation on the TD(lambda) algorithm that enables it to be used in conjunction with game-tree search. We present some experiments in which our chess program ``KnightCap'' used TDLeaf(lambda) to learn its evaluation function while playing on the Free Internet Chess Server (FICS, fics.onenet.net). The main success we report is that KnightCap improved from a 1650 rating to a 2150 rating in just 308 games and 3 days of play. As a reference, a rating of 1650 corresponds to about level B human play (on a scale from E (1000) to A (1800)), while 2150 is human master level. We discuss some of the reasons for this success, principle among them being the use of on-line, rather than self-play.
Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition
Authors: Erik F. Tjong Kim Sang, Fien De Meulder
Date: 2003-06-12 00:00:00
Tasks: Named Entity Recognition, Named Entity Recognition (NER)

We describe the CoNLL-2003 shared task: language-independent named entity recognition. We give background information on the data sets (English and German) and the evaluation method, present a general overview of the systems that have taken part in the task and discuss their performance.
A tutorial introduction to the minimum description length principle
Authors: Peter Grunwald
Date: 2004-06-04 00:00:00

This tutorial provides an overview of and introduction to Rissanen's Minimum Description Length (MDL) Principle. The first chapter provides a conceptual, entirely non-technical introduction to the subject. It serves as a basis for the technical introduction given in the second chapter, in which all the ideas of the first chapter are made mathematically precise. The main ideas are discussed in great conceptual and technical detail. This tutorial is an extended version of the first two chapters of the collection "Advances in Minimum Description Length: Theory and Application" (edited by P.Grunwald, I.J. Myung and M. Pitt, to be published by the MIT Press, Spring 2005).
The information bottleneck method
Authors: Naftali Tishby, Fernando C. Pereira, William Bialek
Date: 2000-04-24 00:00:00

We define the relevant information in a signal $x\in X$ as being the information that this signal provides about another signal $y\in \Y$. Examples include the information that face images provide about the names of the people portrayed, or the information that speech sounds provide about the words spoken. Understanding the signal $x$ requires more than just predicting $y$, it also requires specifying which features of $\X$ play a role in the prediction. We formalize this problem as that of finding a short code for $\X$ that preserves the maximum information about $\Y$. That is, we squeeze the information that $\X$ provides about $\Y$ through a `bottleneck' formed by a limited set of codewords $\tX$. This constrained optimization problem can be seen as a generalization of rate distortion theory in which the distortion measure $d(x,\x)$ emerges from the joint statistics of $\X$ and $\Y$. This approach yields an exact set of self consistent equations for the coding rules $X \to \tX$ and $\tX \to \Y$. Solutions to these equations can be found by a convergent re-estimation method that generalizes the Blahut-Arimoto algorithm. Our variational principle provides a surprisingly rich framework for discussing a variety of problems in signal processing and learning, as will be described in detail elsewhere.
Neural Self-Training through Spaced Repetition
Authors: Hadi Amiri
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6

Self-training is a semi-supervised learning approach for utilizing unlabeled data to create better learners. The efficacy of self-training algorithms depends on their data sampling techniques. The majority of current sampling techniques are based on predetermined policies which may not effectively explore the data space or improve model generalizability. In this work, we tackle the above challenges by introducing a new data sampling technique based on spaced repetition that dynamically samples informative and diverse unlabeled instances with respect to individual learner and instance characteristics. The proposed model is specifically effective in the context of neural models which can suffer from overfitting and high-variance gradients when trained with small amount of labeled data. Our model outperforms current semi-supervised learning approaches developed for neural networks on publicly-available datasets.
Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders
Authors: Cory Shain, Micha Elsner
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Language Acquisition

In this paper, we deploy binary stochastic neural autoencoder networks as models of infant language learning in two typologically unrelated languages (Xitsonga and English). We show that the drive to model auditory percepts leads to latent clusters that partially align with theory-driven phonemic categories. We further evaluate the degree to which theory-driven phonological features are encoded in the latent bit patterns, finding that some (e.g. [+-approximant]), are well represented by the network in both languages, while others (e.g. [+-spread glottis]) are less so. Together, these findings suggest that many reliable cues to phonemic structure are immediately available to infants from bottom-up perceptual characteristics alone, but that these cues must eventually be supplemented by top-down lexical and phonotactic information to achieve adult-like phone discrimination. Our results also suggest differences in degree of perceptual availability between features, yielding testable predictions as to which features might depend more or less heavily on top-down cues during child language acquisition.