pwc-archive/papers-with-abstracts huggingface dataset:
Skeleton-Based Online Action Prediction Using Scale Selection Network
Authors: Jun Liu, Amir Shahroudy, Gang Wang, Ling-Yu Duan, Alex C. Kot
Date: 2019-02-08 00:00:00
Tasks: Prediction, Skeleton Based Action Recognition

Action prediction is to recognize the class label of an ongoing activity when
only a part of it is observed. In this paper, we focus on online action
prediction in streaming 3D skeleton sequences. A dilated convolutional network
is introduced to model the motion dynamics in temporal dimension via a sliding
window over the temporal axis. Since there are significant temporal scale
variations in the observed part of the ongoing action at different time steps,
a novel window scale selection method is proposed to make our network focus on
the performed part of the ongoing action and try to suppress the possible
incoming interference from the previous actions at each step. An activation
sharing scheme is also proposed to handle the overlapping computations among
the adjacent time steps, which enables our framework to run more efficiently.
Moreover, to enhance the performance of our framework for action prediction
with the skeletal input data, a hierarchy of dilated tree convolutions are also
designed to learn the multi-level structured semantic representations over the
skeleton joints at each frame. Our proposed approach is evaluated on four
challenging datasets. The extensive experiments demonstrate the effectiveness
of our method for skeleton-based online action prediction.
Size Independent Neural Transfer for RDDL Planning
Authors: Sankalp Garg, Aniket Bajpai, Mausam
Date: 2019-02-08 00:00:00
Tasks: Decoder, Transfer Learning

Neural planners for RDDL MDPs produce deep reactive policies in an offline
fashion. These scale well with large domains, but are sample inefficient and
time-consuming to train from scratch for each new problem. To mitigate this,
recent work has studied neural transfer learning, so that a generic planner
trained on other problems of the same domain can rapidly transfer to a new
problem. However, this approach only transfers across problems of the same
size. We present the first method for neural transfer of RDDL MDPs that can
transfer across problems of different sizes. Our architecture has two key
innovations to achieve size independence: (1) a state encoder, which outputs a
fixed length state embedding by max pooling over varying number of object
embeddings, (2) a single parameter-tied action decoder that projects object
embeddings into action probabilities for the final policy. On the two
challenging RDDL domains of SysAdmin and Game Of Life, our approach powerfully
transfers across problem sizes and has superior learning curves over training
from scratch.
Reinforcement Learning from Hierarchical Critics
Authors: Zehong Cao, Chin-Teng Lin
Date: 2019-02-08 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Unity

In this study, we investigate the use of global information to speed up the learning process and increase the cumulative rewards of reinforcement learning (RL) in competition tasks. Within the actor-critic RL, we introduce multiple cooperative critics from two levels of the hierarchy and propose a reinforcement learning from hierarchical critics (RLHC) algorithm. In our approach, each agent receives value information from local and global critics regarding a competition task and accesses multiple cooperative critics in a top-down hierarchy. Thus, each agent not only receives low-level details but also considers coordination from higher levels, thereby obtaining global information to improve the training performance. Then, we test the proposed RLHC algorithm against the benchmark algorithm, proximal policy optimisation (PPO), for two experimental scenarios performed in a Unity environment consisting of tennis and soccer agents' competitions. The results showed that RLHC outperforms the benchmark on both competition tasks.
Knowledge Graph Fact Prediction via Knowledge-Enriched Tensor Factorization
Authors: Ankur Padia, Kostantinos Kalpakis, Francis Ferraro, Tim Finin
Date: 2019-02-08 00:00:00
Proceeding: Journal of Web Semantics 2019 12
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Tensor Decomposition

We present a family of novel methods for embedding knowledge graphs into
real-valued tensors. These tensor-based embeddings capture the ordered
relations that are typical in the knowledge graphs represented by semantic web
languages like RDF. Unlike many previous models, our methods can easily use
prior background knowledge provided by users or extracted automatically from
existing knowledge graphs. In addition to providing more robust methods for
knowledge graph embedding, we provide a provably-convergent, linear tensor
factorization algorithm. We demonstrate the efficacy of our models for the task
of predicting new facts across eight different knowledge graphs, achieving
between 5% and 50% relative improvement over existing state-of-the-art
knowledge graph embedding techniques. Our empirical evaluation shows that all
of the tensor decomposition models perform well when the average degree of an
entity in a graph is high, with constraint-based models doing better on graphs
with a small number of highly similar relations and regularization-based models
dominating for graphs with relations of varying degrees of similarity.
A Fast Algorithm for Cosine Transform Based Tensor Singular Value Decomposition
Authors: Wen-Hao Xu, Xi-Le Zhao, Michael Ng
Date: 2019-02-08 00:00:00

Recently, there has been a lot of research into tensor singular value
decomposition (t-SVD) by using discrete Fourier transform (DFT) matrix. The
main aims of this paper are to propose and study tensor singular value
decomposition based on the discrete cosine transform (DCT) matrix. The
advantages of using DCT are that (i) the complex arithmetic is not involved in
the cosine transform based tensor singular value decomposition, so the
computational cost required can be saved; (ii) the intrinsic reflexive boundary
condition along the tubes in the third dimension of tensors is employed, so its
performance would be better than that by using the periodic boundary condition
in DFT. We demonstrate that the tensor product between two tensors by using DCT
can be equivalent to the multiplication between a block Toeplitz-plus-Hankel
matrix and a block vector. Numerical examples of low-rank tensor completion are
further given to illustrate that the efficiency by using DCT is two times
faster than that by using DFT and also the errors of video and multispectral
image completion by using DCT are smaller than those by using DFT.
OrthographicNet: A Deep Transfer Learning Approach for 3D Object Recognition in Open-Ended Domains
Authors: Hamidreza Kasaei
Date: 2019-02-08 00:00:00
Tasks: 3D Object Recognition, Object, Object Recognition, Transfer Learning

Nowadays, service robots are appearing more and more in our daily life. For this type of robot, open-ended object category learning and recognition is necessary since no matter how extensive the training data used for batch learning, the robot might be faced with a new object when operating in a real-world environment. In this work, we present OrthographicNet, a Convolutional Neural Network (CNN)-based model, for 3D object recognition in open-ended domains. In particular, OrthographicNet generates a global rotation- and scale-invariant representation for a given 3D object, enabling robots to recognize the same or similar objects seen from different perspectives. Experimental results show that our approach yields significant improvements over the previous state-of-the-art approaches concerning object recognition performance and scalability in open-ended scenarios. Moreover, OrthographicNet demonstrates the capability of learning new categories from very few examples on-site. Regarding real-time performance, three real-world demonstrations validate the promising performance of the proposed architecture.
K-nn active learning under local smoothness condition
Authors: Boris Ndjia Njike, Xavier Siebert
Date: 2019-02-08 00:00:00
Tasks: Active Learning

There is a large body of work on convergence rates either in passive or active learning. Here we outline some of the results that have been obtained, more specifically in a nonparametric setting under assumptions about the smoothness and the margin noise. We also discuss the relative merits of these underlying assumptions by putting active learning in perspective with recent work on passive learning. We provide a novel active learning algorithm with a rate of convergence better than in passive learning, using a particular smoothness assumption customized for $k$-nearest neighbors. This smoothness assumption provides a dependence on the marginal distribution of the instance space unlike other recent algorithms. Our algorithm thus avoids the strong density assumption that supposes the existence of the density function of the marginal distribution of the instance space and is therefore more generally applicable.
Models of Visually Grounded Speech Signal Pay Attention To Nouns: a Bilingual Experiment on English and Japanese
Authors: William N. Havard, Jean-Pierre Chevrot, Laurent Besacier
Date: 2019-02-08 00:00:00
Tasks: Retrieval

We investigate the behaviour of attention in neural models of visually
grounded speech trained on two languages: English and Japanese. Experimental
results show that attention focuses on nouns and this behaviour holds true for
two very typologically different languages. We also draw parallels between
artificial neural attention and human attention and show that neural attention
focuses on word endings as it has been theorised for human attention. Finally,
we investigate how two visually grounded monolingual models can be used to
perform cross-lingual speech-to-speech retrieval. For both languages, the
enriched bilingual (speech-image) corpora with part-of-speech tags and forced
alignments are distributed to the community for reproducible research.
Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition
Authors: Zizhao Zhang, Adriana Romero, Matthew J. Muckley, Pascal Vincent, Lin Yang, Michal Drozdzal
Date: 2019-02-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: MRI Reconstruction

The goal of MRI reconstruction is to restore a high fidelity image from
partially observed measurements. This partial view naturally induces
reconstruction uncertainty that can only be reduced by acquiring additional
measurements. In this paper, we present a novel method for MRI reconstruction
that, at inference time, dynamically selects the measurements to take and
iteratively refines the prediction in order to best reduce the reconstruction
error and, thus, its uncertainty. We validate our method on a large scale knee
MRI dataset, as well as on ImageNet. Results show that (1) our system
successfully outperforms active acquisition baselines; (2) our uncertainty
estimates correlate with error maps; and (3) our ResNet-based architecture
surpasses standard pixel-to-pixel models in the task of MRI reconstruction. The
proposed method not only shows high-quality reconstructions but also paves the
road towards more applicable solutions for accelerating MRI.
Collaboration based Multi-Label Learning
Authors: Lei Feng, Bo An, Shuo He
Date: 2019-02-08 00:00:00
Tasks: Multi-Label Learning

It is well-known that exploiting label correlations is crucially important to
multi-label learning. Most of the existing approaches take label correlations
as prior knowledge, which may not correctly characterize the real relationships
among labels. Besides, label correlations are normally used to regularize the
hypothesis space, while the final predictions are not explicitly correlated. In
this paper, we suggest that for each individual label, the final prediction
involves the collaboration between its own prediction and the predictions of
other labels. Based on this assumption, we first propose a novel method to
learn the label correlations via sparse reconstruction in the label space.
Then, by seamlessly integrating the learned label correlations into model
training, we propose a novel multi-label learning approach that aims to
explicitly account for the correlated predictions of labels while training the
desired model simultaneously. Extensive experimental results show that our
approach outperforms the state-of-the-art counterparts.
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance
Authors: Ulysse Marteau-Ferey, Dmitrii Ostrovskii, Francis Bach, Alessandro Rudi
Date: 2019-02-08 00:00:00
Tasks: regression

We consider learning methods based on the regularization of a convex empirical risk by a squared Hilbertian norm, a setting that includes linear predictors and non-linear predictors through positive-definite kernels. In order to go beyond the generic analysis leading to convergence rates of the excess risk as $O(1/\sqrt{n})$ from $n$ observations, we assume that the individual losses are self-concordant, that is, their third-order derivatives are bounded by their second-order derivatives. This setting includes least-squares, as well as all generalized linear models such as logistic and softmax regression. For this class of losses, we provide a bias-variance decomposition and show that the assumptions commonly made in least-squares regression, such as the source and capacity conditions, can be adapted to obtain fast non-asymptotic rates of convergence by improving the bias terms, the variance terms or both.
Partial Label Learning with Self-Guided Retraining
Authors: Lei Feng, Bo An
Date: 2019-02-08 00:00:00
Tasks: Partial Label Learning

Partial label learning deals with the problem where each training instance is
assigned a set of candidate labels, only one of which is correct. This paper
provides the first attempt to leverage the idea of self-training for dealing
with partially labeled examples. Specifically, we propose a unified formulation
with proper constraints to train the desired model and perform pseudo-labeling
jointly. For pseudo-labeling, unlike traditional self-training that manually
differentiates the ground-truth label with enough high confidence, we introduce
the maximum infinity norm regularization on the modeling outputs to
automatically achieve this consideratum, which results in a convex-concave
optimization problem. We show that optimizing this convex-concave problem is
equivalent to solving a set of quadratic programming (QP) problems. By
proposing an upper-bound surrogate objective function, we turn to solving only
one QP problem for improving the optimization efficiency. Extensive experiments
on synthesized and real-world datasets demonstrate that the proposed approach
significantly outperforms the state-of-the-art partial label learning
approaches.
A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion from Heartbeat
Authors: Ross Harper, Joshua Southern
Date: 2019-02-08 00:00:00
Tasks: Decision Making, Time Series, Time Series Analysis

Automatic prediction of emotion promises to revolutionise human-computer interaction. Recent trends involve fusion of multiple data modalities - audio, visual, and physiological - to classify emotional state. However, in practice, collection of physiological data `in the wild' is currently limited to heartbeat time series of the kind generated by affordable wearable heart monitors. Furthermore, real-world applications of emotion prediction often require some measure of uncertainty over model output, in order to inform downstream decision-making. We present here an end-to-end deep learning model for classifying emotional valence from unimodal heartbeat time series. We further propose a Bayesian framework for modelling uncertainty over these valence predictions, and describe a probabilistic procedure for choosing to accept or reject model output according to the intended application. We benchmarked our framework against two established datasets and achieved peak classification accuracy of 90%. These results lay the foundation for applications of affective computing in real-world domains such as healthcare, where a high premium is placed on non-invasive collection of data, and predictive certainty.
Bandit Principal Component Analysis
Authors: Wojciech Kotłowski, Gergely Neu
Date: 2019-02-08 00:00:00
Tasks: Decision Making

We consider a partial-feedback variant of the well-studied online PCA problem
where a learner attempts to predict a sequence of $d$-dimensional vectors in
terms of a quadratic loss, while only having limited feedback about the
environment's choices. We focus on a natural notion of bandit feedback where
the learner only observes the loss associated with its own prediction. Based on
the classical observation that this decision-making problem can be lifted to
the space of density matrices, we propose an algorithm that is shown to achieve
a regret of $O(d^{3/2}\sqrt{T})$ after $T$ rounds in the worst case. We also
prove data-dependent bounds that improve on the basic result when the loss
matrices of the environment have bounded rank or the loss of the best action is
bounded. One version of our algorithm runs in $O(d)$ time per trial which
massively improves over every previously known online PCA method. We complement
these results by a lower bound of $\Omega(d\sqrt{T})$.
On the security relevance of weights in deep learning
Authors: Kathrin Grosse, Thomas A. Trost, Marius Mosbach, Michael Backes, Dietrich Klakow
Date: 2019-02-08 00:00:00
Tasks: Deep Learning

Recently, a weight-based attack on stochastic gradient descent inducing overfitting has been proposed. We show that the threat is broader: A task-independent permutation on the initial weights suffices to limit the achieved accuracy to for example 50% on the Fashion MNIST dataset from initially more than $90$%. These findings are confirmed on MNIST and CIFAR. We formally confirm that the attack succeeds with high likelihood and does not depend on the data. Empirically, weight statistics and loss appear unsuspicious, making it hard to detect the attack if the user is not aware. Our paper is thus a call for action to acknowledge the importance of the initial weights in deep learning.
Fourier Neural Networks: A Comparative Study
Authors: Abylay Zhumekenov, Malika Uteuliyeva, Olzhas Kabdolov, Rustem Takhanov, Zhenisbek Assylbekov, Alejandro J. Castro
Date: 2019-02-08 00:00:00

We review neural network architectures which were motivated by Fourier series
and integrals and which are referred to as Fourier neural networks. These
networks are empirically evaluated in synthetic and real-world tasks. Neither
of them outperforms the standard neural network with sigmoid activation
function in the real-world tasks. All neural networks, both Fourier and the
standard one, empirically demonstrate lower approximation error than the
truncated Fourier series when it comes to an approximation of a known function
of multiple variables.
Covariance and Correlation Kernels on a Graph in the Generalized Bag-of-Paths Formalism
Authors: Guillaume Guex, Sylvain Courtain, Marco Saerens
Date: 2019-02-08 00:00:00
Tasks: General Classification, valid

This work derives closed-form expressions computing the expectation of co-presence and of number of co-occurrences of nodes on paths sampled from a network according to general path weights (a bag of paths). The underlying idea is that two nodes are considered as similar when they often appear together on (preferably short) paths of the network. The different expressions are obtained for both regular and hitting paths and serve as a basis for computing new covariance and correlation measures between nodes, which are valid positive semi-definite kernels on a graph. Experiments on semi-supervised classification problems show that the introduced similarity measures provide competitive results compared to other state-of-the-art distance and similarity measures between nodes.
Distribution of residual autocorrelations for multiplicative seasonal ARMA models with uncorrelated but non-independent error terms
Authors: Yacouba Boubacar Maïnassara, Abdoulkarim Ilmi Amir
Date: 2019-02-08 00:00:00

In this paper we consider portmanteau tests for testing the adequacy of
multiplicative seasonal autoregressive moving-average (SARMA) models under the
assumption that the errors are uncorrelated but not necessarily independent.We
relax the standard independence assumption on the error term in order to extend
the range of application of the SARMA models.We study the asymptotic
distributions of residual and normalized residual empirical autocovariances and
autocorrelations underweak assumptions on the noise. We establish the
asymptotic behaviour of the proposed statistics. A set of Monte Carlo
experiments and an application to monthly mean total sunspot number are
presented.
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning
Authors: Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita, Masanori Koyama
Date: 2019-02-08 00:00:00
Tasks: Representation Learning

Hyperbolic space is a geometry that is known to be well-suited for representation learning of data with an underlying hierarchical structure. In this paper, we present a novel hyperbolic distribution called \textit{pseudo-hyperbolic Gaussian}, a Gaussian-like distribution on hyperbolic space whose density can be evaluated analytically and differentiated with respect to the parameters. Our distribution enables the gradient-based learning of the probabilistic models on hyperbolic space that could never have been considered before. Also, we can sample from this hyperbolic probability distribution without resorting to auxiliary means like rejection sampling. As applications of our distribution, we develop a hyperbolic-analog of variational autoencoder and a method of probabilistic word embedding on hyperbolic space. We demonstrate the efficacy of our distribution on various datasets including MNIST, Atari 2600 Breakout, and WordNet.
Fair Decisions Despite Imperfect Predictions
Authors: Niki Kilbertus, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera
Date: 2019-02-08 00:00:00
Tasks: Causal Inference, Decision Making, Fairness

Consequential decisions are increasingly informed by sophisticated data-driven predictive models. However, to consistently learn accurate predictive models, one needs access to ground truth labels. Unfortunately, in practice, labels may only exist conditional on certain decisions---if a loan is denied, there is not even an option for the individual to pay back the loan. Hence, the observed data distribution depends on how decisions are being made. In this paper, we show that in this selective labels setting, learning a predictor directly only from available labeled data is suboptimal in terms of both fairness and utility. To avoid this undesirable behavior, we propose to directly learn decision policies that maximize utility under fairness constraints and thereby take into account how decisions affect which data is observed in the future. Our results suggest the need for a paradigm shift in the context of fair machine learning from the currently prevalent idea of simply building predictive models from a single static dataset via risk minimization, to a more interactive notion of "learning to decide". In particular, such policies should not entirely neglect part of the input space, drawing connections to explore/exploit tradeoffs in reinforcement learning, data missingness, and potential outcomes in causal inference. Experiments on synthetic and real-world data illustrate the favorable properties of learning to decide in terms of utility and fairness.