pwc-archive/papers-with-abstracts huggingface dataset:
Domain Aware Neural Dialog System
Authors: Sajal Choudhary, Prerna Srivastava, Lyle Ungar, João Sedoc
Date: 2017-08-02 00:00:00

We investigate the task of building a domain aware chat system which
generates intelligent responses in a conversation comprising of different
domains. The domain, in this case, is the topic or theme of the conversation.
To achieve this, we present DOM-Seq2Seq, a domain aware neural network model
based on the novel technique of using domain-targeted sequence-to-sequence
models (Sutskever et al., 2014) and a domain classifier. The model captures
features from current utterance and domains of the previous utterances to
facilitate the formation of relevant responses. We evaluate our model on
automatic metrics and compare our performance with the Seq2Seq model.
(k,q)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity Prior
Authors: Evan Schwab, René Vidal, Nicolas Charon
Date: 2017-07-21 00:00:00
Tasks: compressed sensing

Advanced diffusion magnetic resonance imaging (dMRI) techniques, like
diffusion spectrum imaging (DSI) and high angular resolution diffusion imaging
(HARDI), remain underutilized compared to diffusion tensor imaging because the
scan times needed to produce accurate estimations of fiber orientation are
significantly longer. To accelerate DSI and HARDI, recent methods from
compressed sensing (CS) exploit a sparse underlying representation of the data
in the spatial and angular domains to undersample in the respective k- and
q-spaces. State-of-the-art frameworks, however, impose sparsity in the spatial
and angular domains separately and involve the sum of the corresponding sparse
regularizers. In contrast, we propose a unified (k,q)-CS formulation which
imposes sparsity jointly in the spatial-angular domain to further increase
sparsity of dMRI signals and reduce the required subsampling rate. To
efficiently solve this large-scale global reconstruction problem, we introduce
a novel adaptation of the FISTA algorithm that exploits dictionary
separability. We show on phantom and real HARDI data that our approach achieves
significantly more accurate signal reconstructions than the state of the art
while sampling only 2-4% of the (k,q)-space, allowing for the potential of new
levels of dMRI acceleration.
Fingerprint Extraction Using Smartphone Camera
Authors: Saksham Gupta, Sukhad Anand, Atul Rai
Date: 2017-08-02 00:00:00
Tasks: fingerprint verification, Image Enhancement

In the previous decade, there has been a considerable rise in the usage of
smartphones.Due to exorbitant advancement in technology, computational speed
and quality of image capturing has increased considerably. With an increase in
the need for remote fingerprint verification, smartphones can be used as a
powerful alternative for fingerprint authentication instead of conventional
optical sensors. In this research, wepropose a technique to capture
finger-images from the smartphones and pre-process them in such a way that it
can be easily matched with the optical sensor images.Effective finger-image
capturing, image enhancement, fingerprint pattern extraction, core point
detection and image alignment techniques have been discussed. The proposed
approach has been validated on FVC 2004 DB1 & DB2 dataset and the results show
the efficacy of the methodology proposed. The method can be deployed for
real-time commercial usage.
Towards Semantic Modeling of Contradictions and Disagreements: A Case Study of Medical Guidelines
Authors: Wlodek Zadrozny, Hossein Hematialam, Luciana Garbayo
Date: 2017-08-02 00:00:00
Proceeding: WS 2017 1
Tasks: Information Retrieval, Retrieval

We introduce a formal distinction between contradictions and disagreements in
natural language texts, motivated by the need to formally reason about
contradictory medical guidelines. This is a novel and potentially very useful
distinction, and has not been discussed so far in NLP and logic. We also
describe a NLP system capable of automated finding contradictory medical
guidelines; the system uses a combination of text analysis and information
retrieval modules. We also report positive evaluation results on a small corpus
of contradictory medical recommendations.
Latent tree models
Authors: Piotr Zwiernik
Date: 2017-08-02 00:00:00
Tasks: Clustering

Latent tree models are graphical models defined on trees, in which only a
subset of variables is observed. They were first discussed by Judea Pearl as
tree-decomposable distributions to generalise star-decomposable distributions
such as the latent class model. Latent tree models, or their submodels, are
widely used in: phylogenetic analysis, network tomography, computer vision,
causal modeling, and data clustering. They also contain other well-known
classes of models like hidden Markov models, Brownian motion tree model, the
Ising model on a tree, and many popular models used in phylogenetics. This
article offers a concise introduction to the theory of latent tree models. We
emphasise the role of tree metrics in the structural description of this model
class, in designing learning algorithms, and in understanding fundamental
limits of what and when can be learned.
An End-to-End Compression Framework Based on Convolutional Neural Networks
Authors: Feng Jiang, Wen Tao, Shaohui Liu, Jie Ren, Xun Guo, Debin Zhao
Date: 2017-08-02 00:00:00
Tasks: Denoising, Image Compression

Deep learning, e.g., convolutional neural networks (CNNs), has achieved great
success in image processing and computer vision especially in high level vision
applications such as recognition and understanding. However, it is rarely used
to solve low-level vision problems such as image compression studied in this
paper. Here, we move forward a step and propose a novel compression framework
based on CNNs. To achieve high-quality image compression at low bit rates, two
CNNs are seamlessly integrated into an end-to-end compression framework. The
first CNN, named compact convolutional neural network (ComCNN), learns an
optimal compact representation from an input image, which preserves the
structural information and is then encoded using an image codec (e.g., JPEG,
JPEG2000 or BPG). The second CNN, named reconstruction convolutional neural
network (RecCNN), is used to reconstruct the decoded image with high-quality in
the decoding end. To make two CNNs effectively collaborate, we develop a
unified end-to-end learning algorithm to simultaneously learn ComCNN and
RecCNN, which facilitates the accurate reconstruction of the decoded image
using RecCNN. Such a design also makes the proposed compression framework
compatible with existing image coding standards. Experimental results validate
that the proposed compression framework greatly outperforms several compression
frameworks that use existing image coding standards with state-of-the-art
deblocking or denoising post-processing methods.
From Bayesian Sparsity to Gated Recurrent Nets
Authors: Hao He, Bo Xin, David Wipf
Date: 2017-06-09 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: 3D geometry

The iterations of many first-order algorithms, when applied to minimizing
common regularized regression functions, often resemble neural network layers
with pre-specified weights. This observation has prompted the development of
learning-based approaches that purport to replace these iterations with
enhanced surrogates forged as DNN models from available training data. For
example, important NP-hard sparse estimation problems have recently benefitted
from this genre of upgrade, with simple feedforward or recurrent networks
ousting proximal gradient-based iterations. Analogously, this paper
demonstrates that more powerful Bayesian algorithms for promoting sparsity,
which rely on complex multi-loop majorization-minimization techniques, mirror
the structure of more sophisticated long short-term memory (LSTM) networks, or
alternative gated feedback networks previously designed for sequence
prediction. As part of this development, we examine the parallels between
latent variable trajectories operating across multiple time-scales during
optimization, and the activations within deep network structures designed to
adaptively model such characteristic sequences. The resulting insights lead to
a novel sparse estimation system that, when granted training data, can estimate
optimal solutions efficiently in regimes where other algorithms fail, including
practical direction-of-arrival (DOA) and 3D geometry recovery problems. The
underlying principles we expose are also suggestive of a learning process for a
richer class of multi-loop algorithms in other domains.
Enterprise to Computer: Star Trek chatbot
Authors: Grishma Jena, Mansi Vashisht, Abheek Basu, Lyle Ungar, João Sedoc
Date: 2017-08-02 00:00:00
Tasks: Chatbot, Decoder

Human interactions and human-computer interactions are strongly influenced by
style as well as content. Adding a persona to a chatbot makes it more
human-like and contributes to a better and more engaging user experience. In
this work, we propose a design for a chatbot that captures the "style" of Star
Trek by incorporating references from the show along with peculiar tones of the
fictional characters therein. Our Enterprise to Computer bot (E2Cbot) treats
Star Trek dialog style and general dialog style differently, using two
recurrent neural network Encoder-Decoder models. The Star Trek dialog style
uses sequence to sequence (SEQ2SEQ) models (Sutskever et al., 2014; Bahdanau et
al., 2014) trained on Star Trek dialogs. The general dialog style uses Word
Graph to shift the response of the SEQ2SEQ model into the Star Trek domain. We
evaluate the bot both in terms of perplexity and word overlap with Star Trek
vocabulary and subjectively using human evaluators.
Land Cover Classification from Multi-temporal, Multi-spectral Remotely Sensed Imagery using Patch-Based Recurrent Neural Networks
Authors: Atharva Sharma, Xiuwen Liu, Xiaojun Yang
Date: 2017-08-02 00:00:00
Tasks: General Classification, image-classification, Image Classification, Land Cover Classification

Sustainability of the global environment is dependent on the accurate land
cover information over large areas. Even with the increased number of satellite
systems and sensors acquiring data with improved spectral, spatial, radiometric
and temporal characteristics and the new data distribution policy, most
existing land cover datasets were derived from a pixel-based single-date
multi-spectral remotely sensed image with low accuracy. To improve the
accuracy, the bottleneck is how to develop an accurate and effective image
classification technique. By incorporating and utilizing the complete
multi-spectral, multi-temporal and spatial information in remote sensing images
and considering their inherit spatial and sequential interdependence, we
propose a new patch-based RNN (PB-RNN) system tailored for multi-temporal
remote sensing data. The system is designed by incorporating distinctive
characteristics in multi-temporal remote sensing data. In particular, it uses
multi-temporal-spectral-spatial samples and deals with pixels contaminated by
clouds/shadow present in the multi-temporal data series. Using a Florida
Everglades ecosystem study site covering an area of 771 square kilo-meters, the
proposed PB-RNN system has achieved a significant improvement in the
classification accuracy over pixel-based RNN system, pixel-based single-imagery
NN system, pixel-based multi-images NN system, patch-based single-imagery NN
system and patch-based multi-images NN system. For example, the proposed system
achieves 97.21% classification accuracy while a pixel-based single-imagery NN
system achieves 64.74%. By utilizing methods like the proposed PB-RNN one, we
believe that much more accurate land cover datasets can be produced over large
areas efficiently.
Variational Generative Stochastic Networks with Collaborative Shaping
Authors: Philip Bachman, Doina Precup
Date: 2017-08-02 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Rolling Shutter Correction

We develop an approach to training generative models based on unrolling a
variational auto-encoder into a Markov chain, and shaping the chain's
trajectories using a technique inspired by recent work in Approximate Bayesian
computation. We show that the global minimizer of the resulting objective is
achieved when the generative model reproduces the target distribution. To allow
finer control over the behavior of the models, we add a regularization term
inspired by techniques used for regularizing certain types of policy search in
reinforcement learning. We present empirical results on the MNIST and TFD
datasets which show that our approach offers state-of-the-art performance, both
quantitatively and from a qualitative point of view.
AMR Parsing using Stack-LSTMs
Authors: Miguel Ballesteros, Yaser Al-Onaizan
Date: 2017-07-24 00:00:00
Proceeding: EMNLP 2017 9
Tasks: AMR Parsing, POS

We present a transition-based AMR parser that directly generates AMR parses
from plain text. We use Stack-LSTMs to represent our parser state and make
decisions greedily. In our experiments, we show that our parser achieves very
competitive scores on English using only AMR training data. Adding additional
information, such as POS tags and dependency trees, improves the results
further.
Defense semantics of argumentation: encoding reasons for accepting arguments
Authors: Beishui Liao, Leendert van der Torre
Date: 2017-04-30 00:00:00

In this paper we show how the defense relation among abstract arguments can
be used to encode the reasons for accepting arguments. After introducing a
novel notion of defenses and defense graphs, we propose a defense semantics
together with a new notion of defense equivalence of argument graphs, and
compare defense equivalence with standard equivalence and strong equivalence,
respectively. Then, based on defense semantics, we define two kinds of reasons
for accepting arguments, i.e., direct reasons and root reasons, and a notion of
root equivalence of argument graphs. Finally, we show how the notion of root
equivalence can be used in argumentation summarization.
Dependency Grammar Induction with Neural Lexicalization and Big Training Data
Authors: Wenjuan Han, Yong Jiang, Kewei Tu
Date: 2017-08-02 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Dependency Grammar Induction

We study the impact of big models (in terms of the degree of lexicalization)
and big data (in terms of the training corpus size) on dependency grammar
induction. We experimented with L-DMV, a lexicalized version of Dependency
Model with Valence and L-NDMV, our lexicalized extension of the Neural
Dependency Model with Valence. We find that L-DMV only benefits from very small
degrees of lexicalization and moderate sizes of training corpora. L-NDMV can
benefit from big training data and lexicalization of greater degrees,
especially when enhanced with good model initialization, and it achieves a
result that is competitive with the current state-of-the-art.
Deep Deterministic Policy Gradient for Urban Traffic Light Control
Authors: Noe Casas
Date: 2017-03-27 00:00:00

Traffic light timing optimization is still an active line of research despite
the wealth of scientific literature on the topic, and the problem remains
unsolved for any non-toy scenario. One of the key issues with traffic light
optimization is the large scale of the input information that is available for
the controlling agent, namely all the traffic data that is continually sampled
by the traffic detectors that cover the urban network. This issue has in the
past forced researchers to focus on agents that work on localized parts of the
traffic network, typically on individual intersections, and to coordinate every
individual agent in a multi-agent setup. In order to overcome the large scale
of the available state information, we propose to rely on the ability of deep
Learning approaches to handle large input spaces, in the form of Deep
Deterministic Policy Gradient (DDPG) algorithm. We performed several
experiments with a range of models, from the very simple one (one intersection)
to the more complex one (a big city section).
Am I a Baller? Basketball Performance Assessment from First-Person Videos
Authors: Gedas Bertasius, Hyun Soo Park, Stella X. Yu, Jianbo Shi
Date: 2016-11-16 00:00:00
Proceeding: ICCV 2017 10

This paper presents a method to assess a basketball player's performance from
his/her first-person video. A key challenge lies in the fact that the
evaluation metric is highly subjective and specific to a particular evaluator.
We leverage the first-person camera to address this challenge. The
spatiotemporal visual semantics provided by a first-person view allows us to
reason about the camera wearer's actions while he/she is participating in an
unscripted basketball game. Our method takes a player's first-person video and
provides a player's performance measure that is specific to an evaluator's
preference.
  To achieve this goal, we first use a convolutional LSTM network to detect
atomic basketball events from first-person videos. Our network's ability to
zoom-in to the salient regions addresses the issue of a severe camera wearer's
head movement in first-person videos. The detected atomic events are then
passed through the Gaussian mixtures to construct a highly non-linear visual
spatiotemporal basketball assessment feature. Finally, we use this feature to
learn a basketball assessment model from pairs of labeled first-person
basketball videos, for which a basketball expert indicates, which of the two
players is better.
  We demonstrate that despite not knowing the basketball evaluator's criterion,
our model learns to accurately assess the players in real-world games.
Furthermore, our model can also discover basketball events that contribute
positively and negatively to a player's performance.
Unsupervised Learning of Important Objects from First-Person Videos
Authors: Gedas Bertasius, Hyun Soo Park, Stella X. Yu, Jianbo Shi
Date: 2016-11-16 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, Segmentation, Semantic Segmentation

A first-person camera, placed at a person's head, captures, which objects are
important to the camera wearer. Most prior methods for this task learn to
detect such important objects from the manually labeled first-person data in a
supervised fashion. However, important objects are strongly related to the
camera wearer's internal state such as his intentions and attention, and thus,
only the person wearing the camera can provide the importance labels. Such a
constraint makes the annotation process costly and limited in scalability.
  In this work, we show that we can detect important objects in first-person
images without the supervision by the camera wearer or even third-person
labelers. We formulate an important detection problem as an interplay between
the 1) segmentation and 2) recognition agents. The segmentation agent first
proposes a possible important object segmentation mask for each image, and then
feeds it to the recognition agent, which learns to predict an important object
mask using visual semantics and spatial features.
  We implement such an interplay between both agents via an alternating
cross-pathway supervision scheme inside our proposed Visual-Spatial Network
(VSN). Our VSN consists of spatial ("where") and visual ("what") pathways, one
of which learns common visual semantics while the other focuses on the spatial
location cues. Our unsupervised learning is accomplished via a cross-pathway
supervision, where one pathway feeds its predictions to a segmentation agent,
which proposes a candidate important object segmentation mask that is then used
by the other pathway as a supervisory signal. We show our method's success on
two different important object datasets, where our method achieves similar or
better results as the supervised methods.
Structure-measure: A New Way to Evaluate Foreground Maps
Authors: Deng-Ping Fan, Ming-Ming Cheng, Yun Liu, Tao Li, Ali Borji
Date: 2017-08-02 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection, Semantic Segmentation, Small Object Detection, Video Object Detection, Video Salient Object Detection

Foreground map evaluation is crucial for gauging the progress of object
segmentation algorithms, in particular in the filed of salient object detection
where the purpose is to accurately detect and segment the most salient object
in a scene. Several widely-used measures such as Area Under the Curve (AUC),
Average Precision (AP) and the recently proposed Fbw have been utilized to
evaluate the similarity between a non-binary saliency map (SM) and a
ground-truth (GT) map. These measures are based on pixel-wise errors and often
ignore the structural similarities. Behavioral vision studies, however, have
shown that the human visual system is highly sensitive to structures in scenes.
Here, we propose a novel, efficient, and easy to calculate measure known an
structural similarity measure (Structure-measure) to evaluate non-binary
foreground maps. Our new measure simultaneously evaluates region-aware and
object-aware structural similarity between a SM and a GT map. We demonstrate
superiority of our measure over existing ones using 5 meta-measures on 5
benchmark datasets.
InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop Closure
Authors: Victor Adrian Prisacariu, Olaf Kähler, Stuart Golodetz, Michael Sapienza, Tommaso Cavallari, Philip H. S. Torr, David W. Murray
Date: 2017-08-02 00:00:00
Tasks: 3D Reconstruction, GPU, Simultaneous Localization and Mapping

Volumetric models have become a popular representation for 3D scenes in
recent years. One breakthrough leading to their popularity was KinectFusion,
which focuses on 3D reconstruction using RGB-D sensors. However, monocular SLAM
has since also been tackled with very similar approaches. Representing the
reconstruction volumetrically as a TSDF leads to most of the simplicity and
efficiency that can be achieved with GPU implementations of these systems.
However, this representation is memory-intensive and limits applicability to
small-scale reconstructions. Several avenues have been explored to overcome
this. With the aim of summarizing them and providing for a fast, flexible 3D
reconstruction pipeline, we propose a new, unifying framework called InfiniTAM.
The idea is that steps like camera tracking, scene representation and
integration of new data can easily be replaced and adapted to the user's needs.
  This report describes the technical implementation details of InfiniTAM v3,
the third version of our InfiniTAM system. We have added various new features,
as well as making numerous enhancements to the low-level code that
significantly improve our camera tracking performance. The new features that we
expect to be of most interest are (i) a robust camera tracking module; (ii) an
implementation of Glocker et al.'s keyframe-based random ferns camera
relocaliser; (iii) a novel approach to globally-consistent TSDF-based
reconstruction, based on dividing the scene into rigid submaps and optimising
the relative poses between them; and (iv) an implementation of Keller et al.'s
surfel-based reconstruction approach.
Dynamic Entity Representations in Neural Language Models
Authors: Yangfeng Ji, Chenhao Tan, Sebastian Martschat, Yejin Choi, Noah A. Smith
Date: 2017-08-02 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Coreference Resolution, Language Modeling, Language Modelling

Understanding a long document requires tracking how entities are introduced
and evolve over time. We present a new type of language model, EntityNLM, that
can explicitly model entities, dynamically update their representations, and
contextually generate their mentions. Our model is generative and flexible; it
can model an arbitrary number of entities in context while generating each
entity mention at an arbitrary length. In addition, it can be used for several
different tasks such as language modeling, coreference resolution, and entity
prediction. Experimental results with all these tasks demonstrate that our
model consistently outperforms strong baselines and prior work.