pwc-archive/papers-with-abstracts huggingface dataset:
Toward Game Level Generation from Gameplay Videos
Authors: Matthew Guzdial, Mark Riedl
Date: 2016-02-23 00:00:00
Tasks: Game Design

Algorithms that generate computer game content require game design knowledge.
We present an approach to automatically learn game design knowledge for level
design from gameplay videos. We further demonstrate how the acquired design
knowledge can be used to generate sections of game levels. Our approach
involves parsing video of people playing a game to detect the appearance of
patterns of sprites and utilizing machine learning to build a probabilistic
model of sprite placement. We show how rich game design information can be
automatically parsed from gameplay videos and represented as a set of
generative probabilistic models. We use Super Mario Bros. as a proof of
concept. We evaluate our approach on a measure of playability and stylistic
similarity to the original levels as represented in the gameplay videos.
Recovering the number of clusters in data sets with noise features using feature rescaling factors
Authors: Renato Cordeiro de Amorim, Christian Hennig
Date: 2016-02-22 00:00:00
Tasks: Clustering

In this paper we introduce three methods for re-scaling data sets aiming at
improving the likelihood of clustering validity indexes to return the true
number of spherical Gaussian clusters with additional noise features. Our
method obtains feature re-scaling factors taking into account the structure of
a given data set and the intuitive idea that different features may have
different degrees of relevance at different clusters.
  We experiment with the Silhouette (using squared Euclidean, Manhattan, and
the p$^{th}$ power of the Minkowski distance), Dunn's, Calinski-Harabasz and
Hartigan indexes on data sets with spherical Gaussian clusters with and without
noise features. We conclude that our methods indeed increase the chances of
estimating the true number of clusters in a data set.
Empath: Understanding Topic Signals in Large-Scale Text
Authors: Ethan Fast, Binbin Chen, Michael Bernstein
Date: 2016-02-22 00:00:00

Human language is colored by a broad range of topics, but existing text
analysis tools only focus on a small number of them. We present Empath, a tool
that can generate and validate new lexical categories on demand from a small
set of seed terms (like "bleed" and "punch" to generate the category violence).
Empath draws connotations between words and phrases by deep learning a neural
embedding across more than 1.8 billion words of modern fiction. Given a small
set of seed words that characterize a category, Empath uses its neural
embedding to discover new related terms, then validates the category with a
crowd-powered filter. Empath also analyzes text across 200 built-in,
pre-validated categories we have generated from common topics in our web
dataset, like neglect, government, and social media. We show that Empath's
data-driven, human validated categories are highly correlated (r=0.906) with
similar categories in LIWC.
Blind score normalization method for PLDA based speaker recognition
Authors: Danila Doroshin, Nikolay Lubimov, Marina Nastasenko, Mikhail Kotov
Date: 2016-02-22 00:00:00
Tasks: Speaker Recognition

Probabilistic Linear Discriminant Analysis (PLDA) has become state-of-the-art
method for modeling $i$-vector space in speaker recognition task. However the
performance degradation is observed if enrollment data size differs from one
speaker to another. This paper presents a solution to such problem by
introducing new PLDA scoring normalization technique. Normalization parameters
are derived in a blind way, so that, unlike traditional \textit{ZT-norm}, no
extra development data is required. Moreover, proposed method has shown to be
optimal in terms of detection cost function. The experiments conducted on NIST
SRE 2014 database demonstrate an improved accuracy in a mixed enrollment number
condition.
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
Authors: Ramazan Gokberk Cinbis, Jakob Verbeek, Cordelia Schmid
Date: 2015-03-03 00:00:00
Tasks: Multiple Instance Learning, Object, Object Localization, Weakly-supervised Learning, Weakly-Supervised Object Localization

Object category localization is a challenging problem in computer vision.
Standard supervised training requires bounding box annotations of object
instances. This time-consuming annotation process is sidestepped in weakly
supervised learning. In this case, the supervised information is restricted to
binary labels that indicate the absence/presence of object instances in the
image, without their locations. We follow a multiple-instance learning approach
that iteratively trains the detector and infers the object locations in the
positive training images. Our main contribution is a multi-fold multiple
instance learning procedure, which prevents training from prematurely locking
onto erroneous object locations. This procedure is particularly important when
using high-dimensional representations, such as Fisher vectors and
convolutional neural network features. We also propose a window refinement
method, which improves the localization accuracy by incorporating an objectness
prior. We present a detailed experimental evaluation using the PASCAL VOC 2007
dataset, which verifies the effectiveness of our approach.
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning
Authors: Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov
Date: 2015-11-19 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Model Compression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning, Transfer Reinforcement Learning

The ability to act in multiple environments and transfer previous knowledge
to new situations can be considered a critical aspect of any intelligent agent.
Towards this goal, we define a novel method of multitask and transfer learning
that enables an autonomous agent to learn how to behave in multiple tasks
simultaneously, and then generalize its knowledge to new domains. This method,
termed "Actor-Mimic", exploits the use of deep reinforcement learning and model
compression techniques to train a single policy network that learns how to act
in a set of distinct tasks by using the guidance of several expert teachers. We
then show that the representations learnt by the deep policy network are
capable of generalizing to new tasks with no prior expert guidance, speeding up
learning in novel environments. Although our method can in general be applied
to a wide range of problems, we use Atari games as a testing environment to
demonstrate these methods.
Enablers and Inhibitors in Causal Justifications of Logic Programs
Authors: Pedro Cabalar, Jorge Fandinno
Date: 2016-02-22 00:00:00
Tasks: Negation

To appear in Theory and Practice of Logic Programming (TPLP). In this paper
we propose an extension of logic programming (LP) where each default literal
derived from the well-founded model is associated to a justification
represented as an algebraic expression. This expression contains both causal
explanations (in the form of proof graphs built with rule labels) and terms
under the scope of negation that stand for conditions that enable or disable
the application of causal rules. Using some examples, we discuss how these new
conditions, we respectively call "enablers" and "inhibitors", are intimately
related to default negation and have an essentially different nature from
regular cause-effect relations. The most important result is a formal
comparison to the recent algebraic approaches for justifications in LP:
"Why-not Provenance" (WnP) and "Causal Graphs" (CG). We show that the current
approach extends both WnP and CG justifications under the Well-Founded
Semantics and, as a byproduct, we also establish a formal relation between
these two approaches.
Multinomial Loss on Held-out Data for the Sparse Non-negative Matrix Language Model
Authors: Ciprian Chelba, Fernando Pereira
Date: 2015-11-05 00:00:00
Tasks: Language Modeling, Language Modelling

We describe Sparse Non-negative Matrix (SNM) language model estimation using
multinomial loss on held-out data.
  Being able to train on held-out data is important in practical situations
where the training data is usually mismatched from the held-out/test data. It
is also less constrained than the previous training algorithm using
leave-one-out on training data: it allows the use of richer meta-features in
the adjustment model, e.g. the diversity counts used by Kneser-Ney smoothing
which would be difficult to deal with correctly in leave-one-out training.
  In experiments on the one billion words language modeling benchmark, we are
able to slightly improve on our previous results which use a different loss
function, and employ leave-one-out training on a subset of the main training
set. Surprisingly, an adjustment model with meta-features that discard all
lexical information can perform as well as lexicalized meta-features. We find
that fairly small amounts of held-out data (on the order of 30-70 thousand
words) are sufficient for training the adjustment model.
  In a real-life scenario where the training data is a mix of data sources that
are imbalanced in size, and of different degrees of relevance to the held-out
and test data, taking into account the data source for a given skip-/n-gram
feature and combining them for best performance on held-out/test data improves
over skip-/n-gram SNM models trained on pooled data by about 8% in the SMT
setup, or as much as 15% in the ASR/IME setup.
  The ability to mix various data sources based on how relevant they are to a
mismatched held-out set is probably the most attractive feature of the new
estimation method for SNM LM.
Learning to classify with possible sensor failures
Authors: Tianpei Xie, Nasser M. Nasrabadi, Alfred O. Hero
Date: 2015-07-16 00:00:00
Tasks: Anomaly Detection, General Classification, Robust classification

In this paper, we propose a general framework to learn a robust large-margin
binary classifier when corrupt measurements, called anomalies, caused by sensor
failure might be present in the training set. The goal is to minimize the
generalization error of the classifier on non-corrupted measurements while
controlling the false alarm rate associated with anomalous samples. By
incorporating a non-parametric regularizer based on an empirical entropy
estimator, we propose a Geometric-Entropy-Minimization regularized Maximum
Entropy Discrimination (GEM-MED) method to learn to classify and detect
anomalies in a joint manner. We demonstrate using simulated data and a real
multimodal data set. Our GEM-MED method can yield improved performance over
previous robust classification methods in terms of both classification accuracy
and anomaly detection rate.
Temporal Network Analysis of Literary Texts
Authors: Sandra D. Prado, Silvio R. Dahmen, Ana L. C. Bazzan, Padraig Mac Carron, Ralph Kenna
Date: 2016-02-22 00:00:00

We study temporal networks of characters in literature focusing on "Alice's
Adventures in Wonderland" (1865) by Lewis Carroll and the anonymous "La Chanson
de Roland" (around 1100). The former, one of the most influential pieces of
nonsense literature ever written, describes the adventures of Alice in a
fantasy world with logic plays interspersed along the narrative. The latter, a
song of heroic deeds, depicts the Battle of Roncevaux in 778 A.D. during
Charlemagne's campaign on the Iberian Peninsula. We apply methods recently
developed by Taylor and coworkers \cite{Taylor+2015} to find time-averaged
eigenvector centralities, Freeman indices and vitalities of characters. We show
that temporal networks are more appropriate than static ones for studying
stories, as they capture features that the time-independent approaches fail to
yield.
Principal Component Projection Without Principal Component Analysis
Authors: Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford
Date: 2016-02-22 00:00:00
Tasks: regression

We show how to efficiently project a vector onto the top principal components of a matrix, without explicitly computing these components. Specifically, we introduce an iterative algorithm that provably computes the projection using few calls to any black-box routine for ridge regression. By avoiding explicit principal component analysis (PCA), our algorithm is the first with no runtime dependence on the number of top principal components. We show that it can be used to give a fast iterative method for the popular principal component regression problem, giving the first major runtime improvement over the naive method of combining PCA with regression. To achieve our results, we first observe that ridge regression can be used to obtain a "smooth projection" onto the top principal components. We then sharpen this approximation to true projection using a low-degree polynomial approximation to the matrix step function. Step function approximation is a topic of long-term interest in scientific computing. We extend prior theory by constructing polynomials with simple iterative structure and rigorously analyzing their behavior under limited precision.
Geodesics of learned representations
Authors: Olivier J. Hénaff, Eero P. Simoncelli
Date: 2015-11-19 00:00:00
Tasks: image-classification, Image Classification, Translation

We develop a new method for visualizing and refining the invariances of
learned representations. Specifically, we test for a general form of
invariance, linearization, in which the action of a transformation is confined
to a low-dimensional subspace. Given two reference images (typically, differing
by some transformation), we synthesize a sequence of images lying on a path
between them that is of minimal length in the space of the representation (a
"representational geodesic"). If the transformation relating the two reference
images is linearized by the representation, this sequence should follow the
gradual evolution of this transformation. We use this method to assess the
invariance properties of a state-of-the-art image classification network and
find that geodesics generated for image pairs differing by translation,
rotation, and dilation do not evolve according to their associated
transformations. Our method also suggests a remedy for these failures, and
following this prescription, we show that the modified representation is able
to linearize a variety of geometric image transformations.
Higher-Order Low-Rank Regression
Authors: Guillaume Rabusseau, Hachem Kadri
Date: 2016-02-22 00:00:00
Tasks: regression

This paper proposes an efficient algorithm (HOLRR) to handle regression tasks
where the outputs have a tensor structure. We formulate the regression problem
as the minimization of a least square criterion under a multilinear rank
constraint, a difficult non convex problem. HOLRR computes efficiently an
approximate solution of this problem, with solid theoretical guarantees. A
kernel extension is also presented. Experiments on synthetic and real data show
that HOLRR outperforms multivariate and multilinear regression methods and is
considerably faster than existing tensor methods.
Dynamic Filtering of Time-Varying Sparse Signals via l1 Minimization
Authors: Adam Charles, Aurele Balavoine, Christopher Rozell
Date: 2015-07-22 00:00:00

Despite the importance of sparsity signal models and the increasing
prevalence of high-dimensional streaming data, there are relatively few
algorithms for dynamic filtering of time-varying sparse signals. Of the
existing algorithms, fewer still provide strong performance guarantees. This
paper examines two algorithms for dynamic filtering of sparse signals that are
based on efficient l1 optimization methods. We first present an analysis for
one simple algorithm (BPDN-DF) that works well when the system dynamics are
known exactly. We then introduce a novel second algorithm (RWL1-DF) that is
more computationally complex than BPDN-DF but performs better in practice,
especially in the case where the system dynamics model is inaccurate.
Robustness to model inaccuracy is achieved by using a hierarchical
probabilistic data model and propagating higher-order statistics from the
previous estimate (akin to Kalman filtering) in the sparse inference process.
We demonstrate the properties of these algorithms on both simulated data as
well as natural video sequences. Taken together, the algorithms presented in
this paper represent the first strong performance analysis of dynamic filtering
algorithms for time-varying sparse signals as well as state-of-the-art
performance in this emerging application.
From quantum foundations via natural language meaning to a theory of everything
Authors: Bob Coecke
Date: 2016-02-22 00:00:00

In this paper we argue for a paradigmatic shift from `reductionism' to
`togetherness'. In particular, we show how interaction between systems in
quantum theory naturally carries over to modelling how word meanings interact
in natural language. Since meaning in natural language, depending on the
subject domain, encompasses discussions within any scientific discipline, we
obtain a template for theories such as social interaction, animal behaviour,
and many others.
Understanding Visual Concepts with Continuation Learning
Authors: William F. Whitney, Michael Chang, tejas kulkarni, Joshua B. Tenenbaum
Date: 2016-02-22 00:00:00
Tasks: Atari Games

We introduce a neural network architecture and a learning algorithm to
produce factorized symbolic representations. We propose to learn these concepts
by observing consecutive frames, letting all the components of the hidden
representation except a small discrete set (gating units) be predicted from the
previous frame, and let the factors of variation in the next frame be
represented entirely by these discrete gated units (corresponding to symbolic
representations). We demonstrate the efficacy of our approach on datasets of
faces undergoing 3D transformations and Atari 2600 games.
A Statistical Model for Stroke Outcome Prediction and Treatment Planning
Authors: Abhishek Sengupta, Vaibhav Rajan, Sakyajit Bhattacharya, G R K Sarma
Date: 2016-02-22 00:00:00
Tasks: regression

Stroke is a major cause of mortality and long--term disability in the world.
Predictive outcome models in stroke are valuable for personalized treatment,
rehabilitation planning and in controlled clinical trials. In this paper we
design a new model to predict outcome in the short-term, the putative
therapeutic window for several treatments. Our regression-based model has a
parametric form that is designed to address many challenges common in medical
datasets like highly correlated variables and class imbalance. Empirically our
model outperforms the best--known previous models in predicting short--term
outcomes and in inferring the most effective treatments that improve outcome.
Convexification of Learning from Constraints
Authors: Iaroslav Shcherbatyi, Bjoern Andres
Date: 2016-02-22 00:00:00
Tasks: Form

Regularized empirical risk minimization with constrained labels (in contrast
to fixed labels) is a remarkably general abstraction of learning. For common
loss and regularization functions, this optimization problem assumes the form
of a mixed integer program (MIP) whose objective function is non-convex. In
this form, the problem is resistant to standard optimization techniques. We
construct MIPs with the same solutions whose objective functions are convex.
Specifically, we characterize the tightest convex extension of the objective
function, given by the Legendre-Fenchel biconjugate. Computing values of this
tightest convex extension is NP-hard. However, by applying our characterization
to every function in an additive decomposition of the objective function, we
obtain a class of looser convex extensions that can be computed efficiently.
For some decompositions, common loss and regularization functions, we derive a
closed form.
Distributed Deep Learning Using Synchronous Stochastic Gradient Descent
Authors: Dipankar Das, Sasikanth Avancha, Dheevatsa Mudigere, Karthikeyan Vaidynathan, Srinivas Sridharan, Dhiraj Kalamkar, Bharat Kaul, Pradeep Dubey
Date: 2016-02-22 00:00:00
Tasks: Deep Learning

We design and implement a distributed multinode synchronous SGD algorithm,
without altering hyper parameters, or compressing data, or altering algorithmic
behavior. We perform a detailed analysis of scaling, and identify optimal
design points for different networks. We demonstrate scaling of CNNs on 100s of
nodes, and present what we believe to be record training throughputs. A 512
minibatch VGG-A CNN training run is scaled 90X on 128 nodes. Also 256 minibatch
VGG-A and OverFeat-FAST networks are scaled 53X and 42X respectively on a 64
node cluster. We also demonstrate the generality of our approach via
best-in-class 6.5X scaling for a 7-layer DNN on 16 nodes. Thereafter we attempt
to democratize deep-learning by training on an Ethernet based AWS cluster and
show ~14X scaling on 16 nodes.
Regular expressions for decoding of neural network outputs
Authors: Tobias Strauß, Gundram Leifert, Tobias Grüning, Roger Labahn
Date: 2015-09-15 00:00:00
Tasks: Decoder, General Classification, Handwritten Text Recognition, Information Retrieval, Retrieval

This article proposes a convenient tool for decoding the output of neural
networks trained by Connectionist Temporal Classification (CTC) for handwritten
text recognition. We use regular expressions to describe the complex structures
expected in the writing. The corresponding finite automata are employed to
build a decoder. We analyze theoretically which calculations are relevant and
which can be avoided. A great speed-up results from an approximation. We
conclude that the approximation most likely fails if the regular expression
does not match the ground truth which is not harmful for many applications
since the low probability will be even underestimated. The proposed decoder is
very efficient compared to other decoding methods. The variety of applications
reaches from information retrieval to full text recognition. We refer to
applications where we integrated the proposed decoder successfully.
An Effective and Efficient Approach for Clusterability Evaluation
Authors: Margareta Ackerman, Andreas Adolfsson, Naomi Brownstein
Date: 2016-02-22 00:00:00
Tasks: Clustering

Clustering is an essential data mining tool that aims to discover inherent
cluster structure in data. As such, the study of clusterability, which
evaluates whether data possesses such structure, is an integral part of cluster
analysis. Yet, despite their central role in the theory and application of
clustering, current notions of clusterability fall short in two crucial aspects
that render them impractical; most are computationally infeasible and others
fail to classify the structure of real datasets.
  In this paper, we propose a novel approach to clusterability evaluation that
is both computationally efficient and successfully captures the structure of
real data. Our method applies multimodality tests to the (one-dimensional) set
of pairwise distances based on the original, potentially high-dimensional data.
We present extensive analyses of our approach for both the Dip and Silverman
multimodality tests on real data as well as 17,000 simulations, demonstrating
the success of our approach as the first practical notion of clusterability.