pwc-archive/papers-with-abstracts huggingface dataset:
Deep Reinforcement Learning for Dialogue Generation
Authors: Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, Dan Jurafsky
Date: 2016-06-05 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Chatbot, Deep Reinforcement Learning, Dialogue Generation, Diversity, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent neural models of dialogue generation offer great promise for
generating responses for conversational agents, but tend to be shortsighted,
predicting utterances one at a time while ignoring their influence on future
outcomes. Modeling the future direction of a dialogue is crucial to generating
coherent, interesting dialogues, a need which led traditional NLP models of
dialogue to draw on reinforcement learning. In this paper, we show how to
integrate these goals, applying deep reinforcement learning to model future
reward in chatbot dialogue. The model simulates dialogues between two virtual
agents, using policy gradient methods to reward sequences that display three
useful conversational properties: informativity (non-repetitive turns),
coherence, and ease of answering (related to forward-looking function). We
evaluate our model on diversity, length as well as with human judges, showing
that the proposed algorithm generates more interactive responses and manages to
foster a more sustained conversation in dialogue simulation. This work marks a
first step towards learning a neural conversational model based on the
long-term success of dialogues.
Machine Learning Techniques for Stackelberg Security Games: a Survey
Authors: Giuseppe De Nittis, Francesco Trovò
Date: 2016-09-29 00:00:00
Tasks: BIG-bench Machine Learning, Survey

The present survey aims at presenting the current machine learning techniques
employed in security games domains. Specifically, we focused on papers and
works developed by the Teamcore of University of Southern California, which
deepened different directions in this field. After a brief introduction on
Stackelberg Security Games (SSGs) and the poaching setting, the rest of the
work presents how to model a boundedly rational attacker taking into account
her human behavior, then describes how to face the problem of having attacker's
payoffs not defined and how to estimate them and, finally, presents how online
learning techniques have been exploited to learn a model of the attacker.
Word Segmentation on Micro-blog Texts with External Lexicon and Heterogeneous Data
Authors: Qingrong Xia, Zhenghua Li, Jiayuan Chao, Min Zhang
Date: 2016-08-04 00:00:00
Tasks: Segmentation

This paper describes our system designed for the NLPCC 2016 shared task on
word segmentation on micro-blog texts.
Network structure, metadata and the prediction of missing nodes and annotations
Authors: Darko Hric, Tiago P. Peixoto, Santo Fortunato
Date: 2016-04-01 00:00:00
Tasks: Community Detection

The empirical validation of community detection methods is often based on
available annotations on the nodes that serve as putative indicators of the
large-scale network structure. Most often, the suitability of the annotations
as topological descriptors itself is not assessed, and without this it is not
possible to ultimately distinguish between actual shortcomings of the community
detection algorithms on one hand, and the incompleteness, inaccuracy or
structured nature of the data annotations themselves on the other. In this work
we present a principled method to access both aspects simultaneously. We
construct a joint generative model for the data and metadata, and a
nonparametric Bayesian framework to infer its parameters from annotated
datasets. We assess the quality of the metadata not according to its direct
alignment with the network communities, but rather in its capacity to predict
the placement of edges in the network. We also show how this feature can be
used to predict the connections to missing nodes when only the metadata is
available, as well as missing metadata. By investigating a wide range of
datasets, we show that while there are seldom exact agreements between metadata
tokens and the inferred data groups, the metadata is often informative of the
network structure nevertheless, and can improve the prediction of missing
nodes. This shows that the method uncovers meaningful patterns in both the data
and metadata, without requiring or expecting a perfect agreement between the
two.
Semantic Parsing with Semi-Supervised Sequential Autoencoders
Authors: Tomáš Kočiský, Gábor Melis, Edward Grefenstette, Chris Dyer, Wang Ling, Phil Blunsom, Karl Moritz Hermann
Date: 2016-09-29 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Semantic Parsing

We present a novel semi-supervised approach for sequence transduction and
apply it to semantic parsing. The unsupervised component is based on a
generative model in which latent sentences generate the unpaired logical forms.
We apply this method to a number of semantic parsing tasks focusing on domains
with limited access to labelled training data and extend those datasets with
synthetically generated logical forms.
FALDOI: A new minimization strategy for large displacement variational optical flow
Authors: Roberto P. Palomares, Enric Meinhardt-Llopis, Coloma Ballester, Gloria Haro
Date: 2016-02-29 00:00:00
Tasks: Optical Flow Estimation

We propose a large displacement optical flow method that introduces a new
strategy to compute a good local minimum of any optical flow energy functional.
The method requires a given set of discrete matches, which can be extremely
sparse, and an energy functional which locally guides the interpolation from
those matches. In particular, the matches are used to guide a structured
coordinate-descent of the energy functional around these keypoints. It results
in a two-step minimization method at the finest scale which is very robust to
the inevitable outliers of the sparse matcher and able to capture large
displacements of small objects. Its benefits over other variational methods
that also rely on a set of sparse matches are its robustness against very few
matches, high levels of noise and outliers. We validate our proposal using
several optical flow variational models. The results consistently outperform
the coarse-to-fine approaches and achieve good qualitative and quantitative
performance on the standard optical flow benchmarks.
Deep Learning in Multi-Layer Architectures of Dense Nuclei
Authors: Yonghua Yin, Erol Gelenbe
Date: 2016-09-22 00:00:00
Tasks: Deep Learning

We assume that, within the dense clusters of neurons that can be found in
nuclei, cells may interconnect via soma-to-soma interactions, in addition to
conventional synaptic connections. We illustrate this idea with a multi-layer
architecture (MLA) composed of multiple clusters of recurrent sub-networks of
spiking Random Neural Networks (RNN) with dense soma-to-soma interactions, and
use this RNN-MLA architecture for deep learning. The inputs to the clusters are
first normalised by adjusting the external arrival rates of spikes to each
cluster. Then we apply this architecture to learning from multi-channel
datasets. Numerical results based on both images and sensor based data, show
the value of this novel architecture for deep learning.
Online Optimization with Costly and Noisy Measurements using Random Fourier Expansions
Authors: Laurens Bliek, Hans R. G. W. Verstraete, Michel Verhaegen, Sander Wahls
Date: 2016-03-31 00:00:00
Tasks: Bayesian Optimization

This paper analyzes DONE, an online optimization algorithm that iteratively
minimizes an unknown function based on costly and noisy measurements. The
algorithm maintains a surrogate of the unknown function in the form of a random
Fourier expansion (RFE). The surrogate is updated whenever a new measurement is
available, and then used to determine the next measurement point. The algorithm
is comparable to Bayesian optimization algorithms, but its computational
complexity per iteration does not depend on the number of measurements. We
derive several theoretical results that provide insight on how the
hyper-parameters of the algorithm should be chosen. The algorithm is compared
to a Bayesian optimization algorithm for a benchmark problem and three
applications, namely, optical coherence tomography, optical beam-forming
network tuning, and robot arm control. It is found that the DONE algorithm is
significantly faster than Bayesian optimization in the discussed problems,
while achieving a similar or better performance.
Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs
Authors: R. Tapiador, A. Rios-Navarro, A. Linares-Barranco, Minkyu Kim, Deepak Kadetotad, Jae-sun Seo
Date: 2016-09-29 00:00:00
Tasks: CPU, GPU

Deep learning has significantly advanced the state of the art in artificial
intelligence, gaining wide popularity from both industry and academia. Special
interest is around Convolutional Neural Networks (CNN), which take inspiration
from the hierarchical structure of the visual cortex, to form deep layers of
convolutional operations, along with fully connected classifiers. Hardware
implementations of these deep CNN architectures are challenged with memory
bottlenecks that require many convolution and fully-connected layers demanding
large amount of communication for parallel computation. Multi-core CPU based
solutions have demonstrated their inadequacy for this problem due to the memory
wall and low parallelism. Many-core GPU architectures show superior performance
but they consume high power and also have memory constraints due to
inconsistencies between cache and main memory. FPGA design solutions are also
actively being explored, which allow implementing the memory hierarchy using
embedded BlockRAM. This boosts the parallel use of shared memory elements
between multiple processing units, avoiding data replicability and
inconsistencies. This makes FPGAs potentially powerful solutions for real-time
classification of CNNs. Both Altera and Xilinx have adopted OpenCL co-design
framework from GPU for FPGA designs as a pseudo-automatic development solution.
In this paper, a comprehensive evaluation and comparison of Altera and Xilinx
OpenCL frameworks for a 5-layer deep CNN is presented. Hardware resources,
temporal performance and the OpenCL architecture for CNNs are discussed. Xilinx
demonstrates faster synthesis, better FPGA resource utilization and more
compact boards. Altera provides multi-platforms tools, mature design community
and better execution times.
Nonnegative autoencoder with simplified random neural network
Authors: Yonghua Yin, Erol Gelenbe
Date: 2016-09-25 00:00:00

This paper proposes new nonnegative (shallow and multi-layer) autoencoders by
combining the spiking Random Neural Network (RNN) model, the network
architecture typical used in deep-learning area and the training technique
inspired from nonnegative matrix factorization (NMF). The shallow autoencoder
is a simplified RNN model, which is then stacked into a multi-layer
architecture. The learning algorithm is based on the weight update rules in
NMF, subject to the nonnegative probability constraints of the RNN. The
autoencoders equipped with this learning algorithm are tested on typical image
datasets including the MNIST, Yale face and CIFAR-10 datasets, and also using
16 real-world datasets from different areas. The results obtained through these
tests yield the desired high learning and recognition accuracy. Also, numerical
simulations of the stochastic spiking behavior of this RNN auto encoder, show
that it can be implemented in a highly-distributed manner.
Effective LSTMs for Target-Dependent Sentiment Classification
Authors: Duyu Tang, Bing Qin, Xiaocheng Feng, Ting Liu
Date: 2015-12-03 00:00:00
Proceeding: COLING 2016 12
Tasks: Aspect-Based Sentiment Analysis (ABSA), General Classification, Sentence, Sentiment Classification

Target-dependent sentiment classification remains a challenge: modeling the
semantic relatedness of a target with its context words in a sentence.
Different context words have different influences on determining the sentiment
polarity of a sentence towards the target. Therefore, it is desirable to
integrate the connections between target word and context words when building a
learning system. In this paper, we develop two target dependent long short-term
memory (LSTM) models, where target information is automatically taken into
account. We evaluate our methods on a benchmark dataset from Twitter. Empirical
results show that modeling sentence representation with standard LSTM does not
perform well. Incorporating target information into LSTM can significantly
boost the classification accuracy. The target-dependent LSTM models achieve
state-of-the-art performances without using syntactic parser or external
sentiment lexicons.
Robust Moving Objects Detection in Lidar Data Exploiting Visual Cues
Authors: Gheorghii Postica, Andrea Romanoni, Matteo Matteucci
Date: 2016-09-29 00:00:00
Tasks: Object Tracking

Detecting moving objects in dynamic scenes from sequences of lidar scans is
an important task in object tracking, mapping, localization, and navigation.
Many works focus on changes detection in previously observed scenes, while a
very limited amount of literature addresses moving objects detection. The
state-of-the-art method exploits Dempster-Shafer Theory to evaluate the
occupancy of a lidar scan and to discriminate points belonging to the static
scene from moving ones. In this paper we improve both speed and accuracy of
this method by discretizing the occupancy representation, and by removing false
positives through visual cues. Many false positives lying on the ground plane
are also removed thanks to a novel ground plane removal algorithm. Efficiency
is improved through an octree indexing strategy. Experimental evaluation
against the KITTI public dataset shows the effectiveness of our approach, both
qualitatively and quantitatively with respect to the state- of-the-art.
Multi-label Methods for Prediction with Sequential Data
Authors: Jesse Read, Luca Martino, Jaakko Hollmén
Date: 2016-09-27 00:00:00
Tasks: General Classification, Prediction

The number of methods available for classification of multi-label data has
increased rapidly over recent years, yet relatively few links have been made
with the related task of classification of sequential data. If labels indices
are considered as time indices, the problems can often be seen as equivalent.
In this paper we detect and elaborate on connections between multi-label
methods and Markovian models, and study the suitability of multi-label methods
for prediction in sequential data. From this study we draw upon the most
suitable techniques from the area and develop two novel competitive approaches
which can be applied to either kind of data. We carry out an empirical
evaluation investigating performance on real-world sequential-prediction tasks:
electricity demand, and route prediction. As well as showing that several
popular multi-label algorithms are in fact easily applicable to sequencing
tasks, our novel approaches, which benefit from a unified view of these areas,
prove very competitive against established methods.
Heuristic with elements of tabu search for Truck and Trailer Routing Problem
Authors: Ivan S. Grechikhin
Date: 2016-09-29 00:00:00
Tasks: Combinatorial Optimization

Vehicle Routing Problem is a well-known problem in logistics and
transportation, and the variety of such problems is explained by the fact that
it occurs in many real-life situations. It is an NP-hard combinatorial
optimization problem and finding an exact optimal solution is practically
impossible. In this work, Site-Dependent Truck and Trailer Routing Problem with
hard and soft Time Windows and Split Deliveries is considered (SDTTRPTWSD). In
this article, we develop a heuristic with the elements of Tabu Search for
solving SDTTRPTWSD. The heuristic uses the concept of neighborhoods and visits
infeasible solutions during the search. A greedy heuristic is applied to
construct an initial solution.
Block-diagonal covariance selection for high-dimensional Gaussian graphical models
Authors: Emilie Devijver, Mélina Gallopin
Date: 2015-11-12 00:00:00
Tasks: Dimensionality Reduction, Model Selection, Vocal Bursts Intensity Prediction

Gaussian graphical models are widely utilized to infer and visualize networks
of dependencies between continuous variables. However, inferring the graph is
difficult when the sample size is small compared to the number of variables. To
reduce the number of parameters to estimate in the model, we propose a
non-asymptotic model selection procedure supported by strong theoretical
guarantees based on an oracle inequality and a minimax lower bound. The
covariance matrix of the model is approximated by a block-diagonal matrix. The
structure of this matrix is detected by thresholding the sample covariance
matrix, where the threshold is selected using the slope heuristic. Based on the
block-diagonal structure of the covariance matrix, the estimation problem is
divided into several independent problems: subsequently, the network of
dependencies between variables is inferred using the graphical lasso algorithm
in each block. The performance of the procedure is illustrated on simulated
data. An application to a real gene expression dataset with a limited sample
size is also presented: the dimension reduction allows attention to be
objectively focused on interactions among smaller subsets of genes, leading to
a more parsimonious and interpretable modular network.
Kernel Methods on Approximate Infinite-Dimensional Covariance Operators for Image Classification
Authors: Hà Quang Minh, Marco San Biagio, Loris Bazzani, Vittorio Murino
Date: 2016-09-29 00:00:00
Tasks: General Classification, image-classification, Image Classification, Object Recognition

This paper presents a novel framework for visual object recognition using
infinite-dimensional covariance operators of input features in the paradigm of
kernel methods on infinite-dimensional Riemannian manifolds. Our formulation
provides in particular a rich representation of image features by exploiting
their non-linear correlations. Theoretically, we provide a finite-dimensional
approximation of the Log-Hilbert-Schmidt (Log-HS) distance between covariance
operators that is scalable to large datasets, while maintaining an effective
discriminating capability. This allows us to efficiently approximate any
continuous shift-invariant kernel defined using the Log-HS distance. At the
same time, we prove that the Log-HS inner product between covariance operators
is only approximable by its finite-dimensional counterpart in a very limited
scenario. Consequently, kernels defined using the Log-HS inner product, such as
polynomial kernels, are not scalable in the same way as shift-invariant
kernels. Computationally, we apply the approximate Log-HS distance formulation
to covariance operators of both handcrafted and convolutional features,
exploiting both the expressiveness of these features and the power of the
covariance representation. Empirically, we tested our framework on the task of
image classification on twelve challenging datasets. In almost all cases, the
results obtained outperform other state of the art methods, demonstrating the
competitiveness and potential of our framework.
Training Dependency Parsers with Partial Annotation
Authors: Zhenghua Li, Yue Zhang, Jiayuan Chao, Min Zhang
Date: 2016-09-29 00:00:00
Tasks: Dependency Parsing

Recently, these has been a surge on studying how to obtain partially
annotated data for model supervision. However, there still lacks a systematic
study on how to train statistical models with partial annotation (PA). Taking
dependency parsing as our case study, this paper describes and compares two
straightforward approaches for three mainstream dependency parsers. The first
approach is previously proposed to directly train a log-linear graph-based
parser (LLGPar) with PA based on a forest-based objective. This work for the
first time proposes the second approach to directly training a linear
graph-based parse (LGPar) and a linear transition-based parser (LTPar) with PA
based on the idea of constrained decoding. We conduct extensive experiments on
Penn Treebank under three different settings for simulating PA, i.e., random
dependencies, most uncertain dependencies, and dependencies with divergent
outputs from the three parsers. The results show that LLGPar is most effective
in learning from PA and LTPar lags behind the graph-based counterparts by large
margin. Moreover, LGPar and LTPar can achieve best performance by using LLGPar
to complete PA into full annotation (FA).
Modelling depth for nonparametric foreground segmentation using RGBD devices
Authors: Gabriel Moyà-Alcover, Ahmed Elgammal, Antoni Jaume-i-Capó, Javier Varona
Date: 2016-09-29 00:00:00
Tasks: Foreground Segmentation, Segmentation

The problem of detecting changes in a scene and segmenting the foreground
from background is still challenging, despite previous work. Moreover, new RGBD
capturing devices include depth cues, which could be incorporated to improve
foreground segmentation. In this work, we present a new nonparametric approach
where a unified model mixes the device multiple information cues. In order to
unify all the device channel cues, a new probabilistic depth data model is also
proposed where we show how handle the inaccurate data to improve foreground
segmentation. A new RGBD video dataset is presented in order to introduce a new
standard for comparison purposes of this kind of algorithms. Results show that
the proposed approach can handle several practical situations and obtain good
results in all cases.
A comparative study of complexity of handwritten Bharati characters with that of major Indian scripts
Authors: Manali Naik, V. Srinivasa Chakravarthy
Date: 2016-09-29 00:00:00

We present Bharati, a simple, novel script that can represent the characters
of a majority of contemporary Indian scripts. The shapes/motifs of Bharati
characters are drawn from some of the simplest characters of existing Indian
scripts. Bharati characters are designed such that they strictly reflect the
underlying phonetic organization, thereby attributing to the script qualities
of simplicity, familiarity, ease of acquisition and use. Thus, employing
Bharati script as a common script for a majority of Indian languages can
ameliorate several existing communication bottlenecks in India. We perform a
complexity analysis of handwritten Bharati script and compare its complexity
with that of 9 major Indian scripts. The measures of complexity are derived
from a theory of handwritten characters based on Catastrophe theory. Bharati
script is shown to be simpler than the 9 major Indian scripts in most measures
of complexity.
CNN-aware Binary Map for General Semantic Segmentation
Authors: Mahdyar Ravanbakhsh, Hossein Mousavi, Moin Nabi, Mohammad Rastegari, Carlo Regazzoni
Date: 2016-09-29 00:00:00
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation

In this paper we introduce a novel method for general semantic segmentation
that can benefit from general semantics of Convolutional Neural Network (CNN).
Our segmentation proposes visually and semantically coherent image segments. We
use binary encoding of CNN features to overcome the difficulty of the
clustering on the high-dimensional CNN feature space. These binary codes are
very robust against noise and non-semantic changes in the image. These binary
encoding can be embedded into the CNN as an extra layer at the end of the
network. This results in real-time segmentation. To the best of our knowledge
our method is the first attempt on general semantic image segmentation using
CNN. All the previous papers were limited to few number of category of the
images (e.g. PASCAL VOC). Experiments show that our segmentation algorithm
outperform the state-of-the-art non-semantic segmentation methods by large
margin.