pwc-archive/papers-with-abstracts huggingface dataset:
Inverse Reinforcement Learning via Deep Gaussian Process
Authors: Ming Jin, Andreas Damianou, Pieter Abbeel, Costas Spanos
Date: 2015-12-26 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a new approach to inverse reinforcement learning (IRL) based on
the deep Gaussian process (deep GP) model, which is capable of learning
complicated reward structures with few demonstrations. Our model stacks
multiple latent GP layers to learn abstract representations of the state
feature space, which is linked to the demonstrations through the Maximum
Entropy learning framework. Incorporating the IRL engine into the nonlinear
latent structure renders existing deep GP inference approaches intractable. To
tackle this, we develop a non-standard variational approximation framework
which extends previous inference schemes. This allows for approximate Bayesian
treatment of the feature space and guards against overfitting. Carrying out
representation and inverse reinforcement learning simultaneously within our
model outperforms state-of-the-art approaches, as we demonstrate with
experiments on standard benchmarks ("object world","highway driving") and a new
benchmark ("binary world").
Undecidability of the Lambek calculus with subexponential and bracket modalities
Authors: Max Kanovich, Stepan Kuznetsov, Andre Scedrov
Date: 2016-08-13 00:00:00

The Lambek calculus is a well-known logical formalism for modelling natural
language syntax. The original calculus covered a substantial number of
intricate natural language phenomena, but only those restricted to the
context-free setting. In order to address more subtle linguistic issues, the
Lambek calculus has been extended in various ways. In particular, Morrill and
Valentin (2015) introduce an extension with so-called exponential and bracket
modalities. Their extension is based on a non-standard contraction rule for the
exponential that interacts with the bracket structure in an intricate way. The
standard contraction rule is not admissible in this calculus. In this paper we
prove undecidability of the derivability problem in their calculus. We also
investigate restricted decidable fragments considered by Morrill and Valentin
and we show that these fragments belong to the NP class.
Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis
Authors: Xiao Fu, Kejun Huang, Mingyi Hong, Nicholas D. Sidiropoulos, Anthony Man-Cho So
Date: 2016-05-31 00:00:00

Generalized canonical correlation analysis (GCCA) aims at finding latent
low-dimensional common structure from multiple views (feature vectors in
different domains) of the same entities. Unlike principal component analysis
(PCA) that handles a single view, (G)CCA is able to integrate information from
different feature spaces. Here we focus on MAX-VAR GCCA, a popular formulation
which has recently gained renewed interest in multilingual processing and
speech modeling. The classic MAX-VAR GCCA problem can be solved optimally via
eigen-decomposition of a matrix that compounds the (whitened) correlation
matrices of the views; but this solution has serious scalability issues, and is
not directly amenable to incorporating pertinent structural constraints such as
non-negativity and sparsity on the canonical components. We posit regularized
MAX-VAR GCCA as a non-convex optimization problem and propose an alternating
optimization (AO)-based algorithm to handle it. Our algorithm alternates
between {\em inexact} solutions of a regularized least squares subproblem and a
manifold-constrained non-convex subproblem, thereby achieving substantial
memory and computational savings. An important benefit of our design is that it
can easily handle structure-promoting regularization. We show that the
algorithm globally converges to a critical point at a sublinear rate, and
approaches a global optimal solution at a linear rate when no regularization is
considered. Judiciously designed simulations and large-scale word embedding
tasks are employed to showcase the effectiveness of the proposed algorithm.
Spectral and matrix factorization methods for consistent community detection in multi-layer networks
Authors: Subhadeep Paul, Yuguo Chen
Date: 2017-04-24 00:00:00
Tasks: Clustering, Community Detection

We consider the problem of estimating a consensus community structure by
combining information from multiple layers of a multi-layer network using
methods based on the spectral clustering or a low-rank matrix factorization. As
a general theme, these "intermediate fusion" methods involve obtaining a low
column rank matrix by optimizing an objective function and then using the
columns of the matrix for clustering. However, the theoretical properties of
these methods remain largely unexplored. In the absence of statistical
guarantees on the objective functions, it is difficult to determine if the
algorithms optimizing the objectives will return good community structures. We
investigate the consistency properties of the global optimizer of some of these
objective functions under the multi-layer stochastic blockmodel. For this
purpose, we derive several new asymptotic results showing consistency of the
intermediate fusion techniques along with the spectral clustering of mean
adjacency matrix under a high dimensional setup, where the number of nodes, the
number of layers and the number of communities of the multi-layer graph grow.
Our numerical study shows that the intermediate fusion techniques outperform
late fusion methods, namely spectral clustering on aggregate spectral kernel
and module allegiance matrix in sparse networks, while they outperform the
spectral clustering of mean adjacency matrix in multi-layer networks that
contain layers with both homophilic and heterophilic communities.
Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification
Authors: Hussam Hamdan
Date: 2017-05-04 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Word Embeddings

This paper presents Senti17 system which uses ten convolutional neural
networks (ConvNet) to assign a sentiment label to a tweet. The network consists
of a convolutional layer followed by a fully-connected layer and a Softmax on
top. Ten instances of this network are initialized with the same word
embeddings as inputs but with different initializations for the network
weights. We combine the results of all instances by selecting the sentiment
label given by the majority of the ten voters. This system is ranked fourth in
SemEval-2017 Task4 over 38 systems with 67.4%
Deep Watershed Transform for Instance Segmentation
Authors: Min Bai, Raquel Urtasun
Date: 2016-11-24 00:00:00
Proceeding: CVPR 2017 7
Tasks: Instance Segmentation, Object, Segmentation, Semantic Segmentation, Template Matching

Most contemporary approaches to instance segmentation use complex pipelines
involving conditional random fields, recurrent neural networks, object
proposals, or template matching schemes. In our paper, we present a simple yet
powerful end-to-end convolutional neural network to tackle this task. Our
approach combines intuitions from the classical watershed transform and modern
deep learning to produce an energy map of the image where object instances are
unambiguously represented as basins in the energy map. We then perform a cut at
a single energy level to directly yield connected components corresponding to
object instances. Our model more than doubles the performance of the
state-of-the-art on the challenging Cityscapes Instance Level Segmentation
task.
On Identifying Disaster-Related Tweets: Matching-based or Learning-based?
Authors: Hien To, Sumeet Agrawal, Seon Ho Kim, Cyrus Shahabi
Date: 2017-05-04 00:00:00
Tasks: Sentiment Analysis

Social media such as tweets are emerging as platforms contributing to
situational awareness during disasters. Information shared on Twitter by both
affected population (e.g., requesting assistance, warning) and those outside
the impact zone (e.g., providing assistance) would help first responders,
decision makers, and the public to understand the situation first-hand.
Effective use of such information requires timely selection and analysis of
tweets that are relevant to a particular disaster. Even though abundant tweets
are promising as a data source, it is challenging to automatically identify
relevant messages since tweet are short and unstructured, resulting to
unsatisfactory classification performance of conventional learning-based
approaches. Thus, we propose a simple yet effective algorithm to identify
relevant messages based on matching keywords and hashtags, and provide a
comparison between matching-based and learning-based approaches. To evaluate
the two approaches, we put them into a framework specifically proposed for
analyzing disaster-related tweets. Analysis results on eleven datasets with
various disaster types show that our technique provides relevant tweets of
higher quality and more interpretable results of sentiment analysis tasks when
compared to learning approach.
Parametric Gaussian Process Regression for Big Data
Authors: Maziar Raissi
Date: 2017-04-11 00:00:00
Tasks: Gaussian Processes, regression, Variational Inference

This work introduces the concept of parametric Gaussian processes (PGPs),
which is built upon the seemingly self-contradictory idea of making Gaussian
processes parametric. Parametric Gaussian processes, by construction, are
designed to operate in "big data" regimes where one is interested in
quantifying the uncertainty associated with noisy data. The proposed
methodology circumvents the well-established need for stochastic variational
inference, a scalable algorithm for approximating posterior distributions. The
effectiveness of the proposed approach is demonstrated using an illustrative
example with simulated data and a benchmark dataset in the airline industry
with approximately 6 million records.
Sharp Models on Dull Hardware: Fast and Accurate Neural Machine Translation Decoding on the CPU
Authors: Jacob Devlin
Date: 2017-05-04 00:00:00
Proceeding: EMNLP 2017 9
Tasks: CPU, Decoder, Machine Translation, NMT, Translation

Attentional sequence-to-sequence models have become the new standard for
machine translation, but one challenge of such models is a significant increase
in training and decoding cost compared to phrase-based systems. Here, we focus
on efficient decoding, with a goal of achieving accuracy close the
state-of-the-art in neural machine translation (NMT), while achieving CPU
decoding speed/throughput close to that of a phrasal decoder.
  We approach this problem from two angles: First, we describe several
techniques for speeding up an NMT beam search decoder, which obtain a 4.4x
speedup over a very efficient baseline decoder without changing the decoder
output. Second, we propose a simple but powerful network architecture which
uses an RNN (GRU/LSTM) layer at bottom, followed by a series of stacked
fully-connected layers applied at every timestep. This architecture achieves
similar accuracy to a deep recurrent model, at a small fraction of the training
and decoding cost. By combining these techniques, our best system achieves a
very competitive accuracy of 38.3 BLEU on WMT English-French NewsTest2014,
while decoding at 100 words/sec on single-threaded CPU. We believe this is the
best published accuracy/speed trade-off of an NMT system.
A Hybrid Architecture for Multi-Party Conversational Systems
Authors: Maira Gatti de Bayser, Paulo Cavalin, Renan Souza, Alan Braz, Heloisa Candello, Claudio Pinhanez, Jean-Pierre Briot
Date: 2017-05-03 00:00:00
Tasks: BIG-bench Machine Learning

Multi-party Conversational Systems are systems with natural language
interaction between one or more people or systems. From the moment that an
utterance is sent to a group, to the moment that it is replied in the group by
a member, several activities must be done by the system: utterance
understanding, information search, reasoning, among others. In this paper we
present the challenges of designing and building multi-party conversational
systems, the state of the art, our proposed hybrid architecture using both
rules and machine learning and some insights after implementing and evaluating
one on the finance domain.
Machine Learning on Sequential Data Using a Recurrent Weighted Average
Authors: Jared Ostmeyer, Lindsay Cowell
Date: 2017-03-03 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

Recurrent Neural Networks (RNN) are a type of statistical model designed to
handle sequential data. The model reads a sequence one symbol at a time. Each
symbol is processed based on information collected from the previous symbols.
With existing RNN architectures, each symbol is processed using only
information from the previous processing step. To overcome this limitation, we
propose a new kind of RNN model that computes a recurrent weighted average
(RWA) over every past processing step. Because the RWA can be computed as a
running average, the computational overhead scales like that of any other RNN
architecture. The approach essentially reformulates the attention mechanism
into a stand-alone model. The performance of the RWA model is assessed on the
variable copy problem, the adding problem, classification of artificial
grammar, classification of sequences by length, and classification of the MNIST
images (where the pixels are read sequentially one at a time). On almost every
task, the RWA model is found to outperform a standard LSTM model.
Learning Deep NBNN Representations for Robust Place Categorization
Authors: Massimiliano Mancini, Samuel Rota Bulò, Elisa Ricci, Barbara Caputo
Date: 2017-02-25 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Visual Place Recognition

This paper presents an approach for semantic place categorization using data
obtained from RGB cameras. Previous studies on visual place recognition and
classification have shown that, by considering features derived from
pre-trained Convolutional Neural Networks (CNNs) in combination with part-based
classification models, high recognition accuracy can be achieved, even in
presence of occlusions and severe viewpoint changes. Inspired by these works,
we propose to exploit local deep representations, representing images as set of
regions applying a Na\"{i}ve Bayes Nearest Neighbor (NBNN) model for image
classification. As opposed to previous methods where CNNs are merely used as
feature extractors, our approach seamlessly integrates the NBNN model into a
fully-convolutional neural network. Experimental results show that the proposed
algorithm outperforms previous methods based on pre-trained CNN models and
that, when employed in challenging robot place recognition tasks, it is robust
to occlusions, environmental and sensor changes.
Spherical Wards clustering and generalized Voronoi diagrams
Authors: Marek Śmieja, Jacek Tabor
Date: 2017-05-04 00:00:00
Tasks: Clustering

Gaussian mixture model is very useful in many practical problems.
Nevertheless, it cannot be directly generalized to non Euclidean spaces. To
overcome this problem we present a spherical Gaussian-based clustering approach
for partitioning data sets with respect to arbitrary dissimilarity measure. The
proposed method is a combination of spherical Cross-Entropy Clustering with a
generalized Wards approach. The algorithm finds the optimal number of clusters
by automatically removing groups which carry no information. Moreover, it is
scale invariant and allows for forming of spherically-shaped clusters of
arbitrary sizes. In order to graphically represent and interpret the results
the notion of Voronoi diagram was generalized to non Euclidean spaces and
applied for introduced clustering method.
Semi-supervised model-based clustering with controlled clusters leakage
Authors: Marek Śmieja, Łukasz Struski, Jacek Tabor
Date: 2017-05-04 00:00:00
Tasks: Clustering

In this paper, we focus on finding clusters in partially categorized data
sets. We propose a semi-supervised version of Gaussian mixture model, called
C3L, which retrieves natural subgroups of given categories. In contrast to
other semi-supervised models, C3L is parametrized by user-defined leakage
level, which controls maximal inconsistency between initial categorization and
resulting clustering. Our method can be implemented as a module in practical
expert systems to detect clusters, which combine expert knowledge with true
distribution of data. Moreover, it can be used for improving the results of
less flexible clustering techniques, such as projection pursuit clustering. The
paper presents extensive theoretical analysis of the model and fast algorithm
for its efficient optimization. Experimental results show that C3L finds high
quality clustering model, which can be applied in discovering meaningful groups
in partially classified data.
MotifMark: Finding Regulatory Motifs in DNA Sequences
Authors: Hamid Reza Hassanzadeh, Pushkar Kolhe, Charles L. Isbell, May D. Wang
Date: 2017-05-04 00:00:00
Tasks: Specificity

The interaction between proteins and DNA is a key driving force in a
significant number of biological processes such as transcriptional regulation,
repair, recombination, splicing, and DNA modification. The identification of
DNA-binding sites and the specificity of target proteins in binding to these
regions are two important steps in understanding the mechanisms of these
biological activities. A number of high-throughput technologies have recently
emerged that try to quantify the affinity between proteins and DNA motifs.
Despite their success, these technologies have their own limitations and fall
short in precise characterization of motifs, and as a result, require further
downstream analysis to extract useful and interpretable information from a
haystack of noisy and inaccurate data. Here we propose MotifMark, a new
algorithm based on graph theory and machine learning, that can find binding
sites on candidate probes and rank their specificity in regard to the
underlying transcription factor. We developed a pipeline to analyze
experimental data derived from compact universal protein binding microarrays
and benchmarked it against two of the most accurate motif search methods. Our
results indicate that MotifMark can be a viable alternative technique for
prediction of motif from protein binding microarrays and possibly other related
high-throughput techniques.
Using real-time cluster configurations of streaming asynchronous features as online state descriptors in financial markets
Authors: Dieter Hendricks
Date: 2016-03-22 00:00:00
Tasks: Attribute, Clustering

We present a scheme for online, unsupervised state discovery and detection
from streaming, multi-featured, asynchronous data in high-frequency financial
markets. Online feature correlations are computed using an unbiased, lossless
Fourier estimator. A high-speed maximum likelihood clustering algorithm is then
used to find the feature cluster configuration which best explains the
structure in the correlation matrix. We conjecture that this feature
configuration is a candidate descriptor for the temporal state of the system.
Using a simple cluster configuration similarity metric, we are able to
enumerate the state space based on prevailing feature configurations. The
proposed state representation removes the need for human-driven data
pre-processing for state attribute specification, allowing a learning agent to
find structure in streaming data, discern changes in the system, enumerate its
perceived state space and learn suitable action-selection policies.
A Finite State and Rule-based Akshara to Prosodeme (A2P) Converter in Hindi
Authors: Somnath Roy
Date: 2017-05-04 00:00:00

This article describes a software module called Akshara to Prosodeme (A2P)
converter in Hindi. It converts an input grapheme into prosedeme (sequence of
phonemes with the specification of syllable boundaries and prosodic labels).
The software is based on two proposed finite state machines\textemdash one for
the syllabification and another for the syllable labeling. In addition to that,
it also uses a set of nonlinear phonological rules proposed for foot formation
in Hindi, which encompass solutions to schwa-deletion in simple, compound,
derived and inflected words. The nonlinear phonological rules are based on
metrical phonology with the provision of recursive foot structure. A software
module is implemented in Python. The testing of the software for
syllabification, syllable labeling, schwa deletion and prosodic labeling yield
an accuracy of more than 99% on a lexicon of size 28664 words.
A Reasoning System for a First-Order Logic of Limited Belief
Authors: Christoph Schwering
Date: 2017-05-04 00:00:00

Logics of limited belief aim at enabling computationally feasible reasoning
in highly expressive representation languages. These languages are often
dialects of first-order logic with a weaker form of logical entailment that
keeps reasoning decidable or even tractable. While a number of such logics have
been proposed in the past, they tend to remain for theoretical analysis only
and their practical relevance is very limited. In this paper, we aim to go
beyond the theory. Building on earlier work by Liu, Lakemeyer, and Levesque, we
develop a logic of limited belief that is highly expressive while remaining
decidable in the first-order and tractable in the propositional case and
exhibits some characteristics that make it attractive for an implementation. We
introduce a reasoning system that employs this logic as representation language
and present experimental results that showcase the benefit of limited belief.
Fast k-means based on KNN Graph
Authors: Cheng-Hao Deng, Wan-Lei Zhao
Date: 2017-05-04 00:00:00
Tasks: Clustering

In the era of big data, k-means clustering has been widely adopted as a basic
processing tool in various contexts. However, its computational cost could be
prohibitively high as the data size and the cluster number are large. It is
well known that the processing bottleneck of k-means lies in the operation of
seeking closest centroid in each iteration. In this paper, a novel solution
towards the scalability issue of k-means is presented. In the proposal, k-means
is supported by an approximate k-nearest neighbors graph. In the k-means
iteration, each data sample is only compared to clusters that its nearest
neighbors reside. Since the number of nearest neighbors we consider is much
less than k, the processing cost in this step becomes minor and irrelevant to
k. The processing bottleneck is therefore overcome. The most interesting thing
is that k-nearest neighbor graph is constructed by iteratively calling the fast
$k$-means itself. Comparing with existing fast k-means variants, the proposed
algorithm achieves hundreds to thousands times speed-up while maintaining high
clustering quality. As it is tested on 10 million 512-dimensional data, it
takes only 5.2 hours to produce 1 million clusters. In contrast, to fulfill the
same scale of clustering, it would take 3 years for traditional k-means.
Pixel Normalization from Numeric Data as Input to Neural Networks
Authors: Parth Sane, Ravindra Agrawal
Date: 2017-05-04 00:00:00
Tasks: GPU

Text to image transformation for input to neural networks requires
intermediate steps. This paper attempts to present a new approach to pixel
normalization so as to convert textual data into image, suitable as input for
neural networks. This method can be further improved by its Graphics Processing
Unit (GPU) implementation to provide significant speedup in computational time.