pwc-archive/papers-with-abstracts huggingface dataset:
C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0 Dataset
Authors: Aishwarya Agrawal, Aniruddha Kembhavi, Dhruv Batra, Devi Parikh
Date: 2017-04-26 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual Question Answering (VQA) has received a lot of attention over the past
couple of years. A number of deep learning models have been proposed for this
task. However, it has been shown that these models are heavily driven by
superficial correlations in the training data and lack compositionality -- the
ability to answer questions about unseen compositions of seen concepts. This
compositionality is desirable and central to intelligence. In this paper, we
propose a new setting for Visual Question Answering where the test
question-answer pairs are compositionally novel compared to training
question-answer pairs. To facilitate developing models under this setting, we
present a new compositional split of the VQA v1.0 dataset, which we call
Compositional VQA (C-VQA). We analyze the distribution of questions and answers
in the C-VQA splits. Finally, we evaluate several existing VQA models under
this new setting and show that the performances of these models degrade by a
significant amount compared to the original VQA setting.
A Reverse Hex Solver
Authors: Kenny Young, Ryan B. Hayward
Date: 2017-04-26 00:00:00
Tasks: Position

We present Solrex,an automated solver for the game of Reverse Hex.Reverse
Hex, also known as Rex, or Misere Hex, is the variant of the game of Hex in
which the player who joins her two sides loses the game. Solrex performs a
mini-max search of the state space using Scalable Parallel Depth First Proof
Number Search, enhanced by the pruning of inferior moves and the early
detection of certain winning strategies. Solrex is implemented on the same code
base as the Hex program Solver, and can solve arbitrary positions on board
sizes up to 6x6, with the hardest position taking less than four hours on four
threads.
Enhanced LSTM for Natural Language Inference
Authors: Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, Diana Inkpen
Date: 2016-09-20 00:00:00
Proceeding: ACL 2017 7
Tasks: Natural Language Inference

Reasoning and inference are central to human and artificial intelligence.
Modeling inference in human language is very challenging. With the availability
of large annotated data (Bowman et al., 2015), it has recently become feasible
to train neural network based inference models, which have shown to be very
effective. In this paper, we present a new state-of-the-art result, achieving
the accuracy of 88.6% on the Stanford Natural Language Inference Dataset.
Unlike the previous top models that use very complicated network architectures,
we first demonstrate that carefully designing sequential inference models based
on chain LSTMs can outperform all previous models. Based on this, we further
show that by explicitly considering recursive architectures in both local
inference modeling and inference composition, we achieve additional
improvement. Particularly, incorporating syntactic parsing information
contributes to our best result---it further improves the performance even when
added to the already very strong model.
Accelerating Stochastic Gradient Descent For Least Squares Regression
Authors: Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford
Date: 2017-04-26 00:00:00
Tasks: regression, Stochastic Optimization

There is widespread sentiment that it is not possible to effectively utilize
fast gradient methods (e.g. Nesterov's acceleration, conjugate gradient, heavy
ball) for the purposes of stochastic optimization due to their instability and
error accumulation, a notion made precise in d'Aspremont 2008 and Devolder,
Glineur, and Nesterov 2014. This work considers these issues for the special
case of stochastic approximation for the least squares regression problem, and
our main result refutes the conventional wisdom by showing that acceleration
can be made robust to statistical errors. In particular, this work introduces
an accelerated stochastic gradient method that provably achieves the minimax
optimal statistical risk faster than stochastic gradient descent. Critical to
the analysis is a sharp characterization of accelerated stochastic gradient
descent as a stochastic process. We hope this characterization gives insights
towards the broader question of designing simple and effective accelerated
stochastic methods for more general convex and non-convex optimization
problems.
New region force for variational models in image segmentation and high dimensional data clustering
Authors: Ke Wei, Ke Yin, Xue-Cheng Tai, Tony F. Chan
Date: 2017-04-26 00:00:00
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation

We propose an effective framework for multi-phase image segmentation and
semi-supervised data clustering by introducing a novel region force term into
the Potts model. Assume the probability that a pixel or a data point belongs to
each class is known a priori. We show that the corresponding indicator function
obeys the Bernoulli distribution and the new region force function can be
computed as the negative log-likelihood function under the Bernoulli
distribution. We solve the Potts model by the primal-dual hybrid gradient
method and the augmented Lagrangian method, which are based on two different
dual problems of the same primal problem. Empirical evaluations of the Potts
model with the new region force function on benchmark problems show that it is
competitive with existing variational methods in both image segmentation and
semi-supervised data clustering.
Multi-Task Learning of Keyphrase Boundary Classification
Authors: Isabelle Augenstein, Anders Søgaard
Date: 2017-04-03 00:00:00
Proceeding: ACL 2017 7
Tasks: Articles, Classification, General Classification, Multi-Task Learning

Keyphrase boundary classification (KBC) is the task of detecting keyphrases
in scientific articles and labelling them with respect to predefined types.
Although important in practice, this task is so far underexplored, partly due
to the lack of labelled data. To overcome this, we explore several auxiliary
tasks, including semantic super-sense tagging and identification of multi-word
expressions, and cast the task as a multi-task learning problem with deep
recurrent neural networks. Our multi-task models perform significantly better
than previous state of the art approaches on two scientific KBC datasets,
particularly for long keyphrases.
PonyGE2: Grammatical Evolution in Python
Authors: Michael Fenton, James McDermott, David Fagan, Stefan Forstenlechner, Michael O'Neill, Erik Hemberg
Date: 2017-03-24 00:00:00

Grammatical Evolution (GE) is a population-based evolutionary algorithm,
where a formal grammar is used in the genotype to phenotype mapping process.
PonyGE2 is an open source implementation of GE in Python, developed at UCD's
Natural Computing Research and Applications group. It is intended as an
advertisement and a starting-point for those new to GE, a reference for
students and researchers, a rapid-prototyping medium for our own experiments,
and a Python workout. As well as providing the characteristic genotype to
phenotype mapping of GE, a search algorithm engine is also provided. A number
of sample problems and tutorials on how to use and adapt PonyGE2 have been
developed.
Network-based coverage of mutational profiles reveals cancer genes
Authors: Borislav H. Hristov, Mona Singh
Date: 2017-04-26 00:00:00

A central goal in cancer genomics is to identify the somatic alterations that
underpin tumor initiation and progression. This task is challenging as the
mutational profiles of cancer genomes exhibit vast heterogeneity, with many
alterations observed within each individual, few shared somatically mutated
genes across individuals, and important roles in cancer for both frequently and
infrequently mutated genes. While commonly mutated cancer genes are readily
identifiable, those that are rarely mutated across samples are difficult to
distinguish from the large numbers of other infrequently mutated genes. Here,
we introduce a method that considers per-individual mutational profiles within
the context of protein-protein interaction networks in order to identify small
connected subnetworks of genes that, while not individually frequently mutated,
comprise pathways that are perturbed across (i.e., "cover") a large fraction of
the individuals. We devise a simple yet intuitive objective function that
balances identifying a small subset of genes with covering a large fraction of
individuals. We show how to solve this problem optimally using integer linear
programming and also give a fast heuristic algorithm that works well in
practice. We perform a large-scale evaluation of our resulting method, nCOP, on
6,038 TCGA tumor samples across 24 different cancer types. We demonstrate that
our approach nCOP is more effective in identifying cancer genes than both
methods that do not utilize any network information as well as state-of-the-art
network-based methods that aggregate mutational information across individuals.
Overall, our work demonstrates the power of combining per-individual mutational
information with interaction networks in order to uncover genes functionally
relevant in cancers, and in particular those genes that are less frequently
mutated.
A Generalization of Convolutional Neural Networks to Graph-Structured Data
Authors: Yotam Hechtlinger, Purvasha Chakravarti, Jining Qin
Date: 2017-04-26 00:00:00
Tasks: General Classification, regression

This paper introduces a generalization of Convolutional Neural Networks
(CNNs) from low-dimensional grid data, such as images, to graph-structured
data. We propose a novel spatial convolution utilizing a random walk to uncover
the relations within the input, analogous to the way the standard convolution
uses the spatial neighborhood of a pixel on the grid. The convolution has an
intuitive interpretation, is efficient and scalable and can also be used on
data with varying graph structure. Furthermore, this generalization can be
applied to many standard regression or classification problems, by learning the
the underlying graph. We empirically demonstrate the performance of the
proposed CNN on MNIST, and challenge the state-of-the-art on Merck molecular
activity data set.
Compact Descriptors for Video Analysis: the Emerging MPEG Standard
Authors: Ling-Yu Duan, Vijay Chandrasekhar, Shiqi Wang, Yihang Lou, Jie Lin, Yan Bai, Tiejun Huang, Alex ChiChung Kot, Wen Gao
Date: 2017-04-26 00:00:00

This paper provides an overview of the on-going compact descriptors for video
analysis standard (CDVA) from the ISO/IEC moving pictures experts group (MPEG).
MPEG-CDVA targets at defining a standardized bitstream syntax to enable
interoperability in the context of video analysis applications. During the
developments of MPEGCDVA, a series of techniques aiming to reduce the
descriptor size and improve the video representation ability have been
proposed. This article describes the new standard that is being developed and
reports the performance of these key technical contributions.
Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network
Authors: Mohammadreza Soltaninejad, Lei Zhang, Tryphon Lambrou, Nigel Allinson, Xujiong Ye
Date: 2017-04-26 00:00:00
Tasks: Brain Tumor Segmentation, Tumor Segmentation

In this paper, we propose a novel learning based method for automated
segmenta-tion of brain tumor in multimodal MRI images. The machine learned
features from fully convolutional neural network (FCN) and hand-designed texton
fea-tures are used to classify the MRI image voxels. The score map with
pixel-wise predictions is used as a feature map which is learned from
multimodal MRI train-ing dataset using the FCN. The learned features are then
applied to random for-ests to classify each MRI image voxel into normal brain
tissues and different parts of tumor. The method was evaluated on BRATS 2013
challenge dataset. The results show that the application of the random forest
classifier to multimodal MRI images using machine-learned features based on FCN
and hand-designed features based on textons provides promising segmentations.
The Dice overlap measure for automatic brain tumor segmentation against ground
truth is 0.88, 080 and 0.73 for complete tumor, core and enhancing tumor,
respectively.
Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation
Authors: Rafael Izbicki, Ann B. Lee
Date: 2017-04-26 00:00:00
Tasks: Astronomy, Density Estimation, regression, Vocal Bursts Intensity Prediction

There is a growing demand for nonparametric conditional density estimators
(CDEs) in fields such as astronomy and economics. In astronomy, for example,
one can dramatically improve estimates of the parameters that dictate the
evolution of the Universe by working with full conditional densities instead of
regression (i.e., conditional mean) estimates. More generally, standard
regression falls short in any prediction problem where the distribution of the
response is more complex with multi-modality, asymmetry or heteroscedastic
noise. Nevertheless, much of the work on high-dimensional inference concerns
regression and classification only, whereas research on density estimation has
lagged behind. Here we propose FlexCode, a fully nonparametric approach to
conditional density estimation that reformulates CDE as a non-parametric
orthogonal series problem where the expansion coefficients are estimated by
regression. By taking such an approach, one can efficiently estimate
conditional densities and not just expectations in high dimensions by drawing
upon the success in high-dimensional regression. Depending on the choice of
regression procedure, our method can adapt to a variety of challenging
high-dimensional settings with different structures in the data (e.g., a large
number of irrelevant components and nonlinear manifold structure) as well as
different data types (e.g., functional data, mixed data types and sample sets).
We study the theoretical and empirical performance of our proposed method, and
we compare our approach with traditional conditional density estimators on
simulated as well as real-world data, such as photometric galaxy data, Twitter
data, and line-of-sight velocities in a galaxy cluster.
STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling
Authors: Yang He, Wei-Chen Chiu, Margret Keuper, Mario Fritz
Date: 2016-04-08 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Segmentation, Optical Flow Estimation, RGBD Semantic Segmentation, Segmentation, Semantic Segmentation, Superpixels

We propose a novel superpixel-based multi-view convolutional neural network
for semantic image segmentation. The proposed network produces a high quality
segmentation of a single image by leveraging information from additional views
of the same scene. Particularly in indoor videos such as captured by robotic
platforms or handheld and bodyworn RGBD cameras, nearby video frames provide
diverse viewpoints and additional context of objects and scenes. To leverage
such information, we first compute region correspondences by optical flow and
image boundary-based superpixels. Given these region correspondences, we
propose a novel spatio-temporal pooling layer to aggregate information over
space and time. We evaluate our approach on the NYU--Depth--V2 and the SUN3D
datasets and compare it to various state-of-the-art single-view and multi-view
approaches. Besides a general improvement over the state-of-the-art, we also
show the benefits of making use of unlabeled frames during training for
multi-view as well as single-view prediction.
A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations
Authors: Samuel Rönnqvist, Niko Schenk, Christian Chiarcos
Date: 2017-04-26 00:00:00
Proceeding: ACL 2017 7

We introduce an attention-based Bi-LSTM for Chinese implicit discourse
relations and demonstrate that modeling argument pairs as a joint sequence can
outperform word order-agnostic approaches. Our model benefits from a partial
sampling scheme and is conceptually simple, yet achieves state-of-the-art
performance on the Chinese Discourse Treebank. We also visualize its attention
activity to illustrate the model's ability to selectively focus on the relevant
parts of an input sequence.
A Faster Patch Ordering Method for Image Denoising
Authors: Badre Munir
Date: 2017-04-26 00:00:00
Tasks: Denoising, Image Denoising, Traveling Salesman Problem

Among the patch-based image denoising processing methods, smooth ordering of
local patches (patch ordering) has been shown to give state-of-art results. For
image denoising the patch ordering method forms two large TSPs (Traveling
Salesman Problem) comprised of nodes in N-dimensional space. Ten approximate
solutions of the two large TSPs are then used in a filtering process to form
the reconstructed image. Use of large TSPs makes patch ordering a
computationally intensive method. A modified patch ordering method for image
denoising is proposed. In the proposed method, several smaller-sized TSPs are
formed and the filtering process varied to work with solutions of these smaller
TSPs. In terms of PSNR, denoising results of the proposed method differed by
0.032 dB to 0.016 dB on average. In original method, solving TSPs was observed
to consume 85% of execution time. In proposed method, the time for solving TSPs
can be reduced to half of the time required in original method. The proposed
method can denoise images in 40% less time.
Enriching Complex Networks with Word Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts
Authors: Leandro B. dos Santos, Edilson A. Corrêa Jr, Osvaldo N. Oliveira Jr, Diego R. Amancio, Letícia L. Mansur, Sandra M. Aluísio
Date: 2017-04-26 00:00:00
Tasks: Binary Classification, Word Embeddings

Mild Cognitive Impairment (MCI) is a mental disorder difficult to diagnose.
Linguistic features, mainly from parsers, have been used to detect MCI, but
this is not suitable for large-scale assessments. MCI disfluencies produce
non-grammatical speech that requires manual or high precision automatic
correction of transcripts. In this paper, we modeled transcripts into complex
networks and enriched them with word embedding (CNE) to better represent short
texts produced in neuropsychological assessments. The network measurements were
applied with well-known classifiers to automatically identify MCI in
transcripts, in a binary classification task. A comparison was made with the
performance of traditional approaches using Bag of Words (BoW) and linguistic
features for three datasets: DementiaBank in English, and Cinderella and
Arizona-Battery in Portuguese. Overall, CNE provided higher accuracy than using
only complex networks, while Support Vector Machine was superior to other
classifiers. CNE provided the highest accuracies for DementiaBank and
Cinderella, but BoW was more efficient for the Arizona-Battery dataset probably
owing to its short narratives. The approach using linguistic features yielded
higher accuracy if the transcriptions of the Cinderella dataset were manually
revised. Taken together, the results indicate that complex networks enriched
with embedding is promising for detecting MCI in large-scale assessments
Modeling Events as Machines
Authors: Sabah Al-Fedaghi
Date: 2017-04-26 00:00:00
Tasks: Philosophy

The notion of events has occupied a central role in modeling and has an
influence in computer science and philosophy. Recent developments in
diagrammatic modeling have made it possible to examine conceptual
representation of events. This paper explores some aspects of the notion of
events that are produced by applying a new diagrammatic methodology with a
focus on the interaction of events with such concepts as time and space,
objects. The proposed description applies to abstract machines where events
form the dynamic phases of a system. The results of this nontechnical research
can be utilized in many fields where the notion of an event is typically used
in interdisciplinary application.
A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories
Authors: Weiyao Lin, Yang Zhou, Hongteng Xu, Junchi Yan, Mingliang Xu, Jianxin Wu, Zicheng Liu
Date: 2016-09-10 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Anomaly Detection, Clustering, Temporal Action Localization, Trajectory Clustering

Trajectory analysis is essential in many applications. In this paper, we
address the problem of representing motion trajectories in a highly informative
way, and consequently utilize it for analyzing trajectories. Our approach first
leverages the complete information from given trajectories to construct a
thermal transfer field which provides a context-rich way to describe the global
motion pattern in a scene. Then, a 3D tube is derived which depicts an input
trajectory by integrating its surrounding motion patterns contained in the
thermal transfer field. The 3D tube effectively: 1) maintains the movement
information of a trajectory, 2) embeds the complete contextual motion pattern
around a trajectory, 3) visualizes information about a trajectory in a clear
and unified way. We further introduce a droplet-based process. It derives a
droplet vector from a 3D tube, so as to characterize the high-dimensional 3D
tube information in a simple but effective way. Finally, we apply our
tube-and-droplet representation to trajectory analysis applications including
trajectory clustering, trajectory classification & abnormality detection, and
3D action recognition. Experimental comparisons with state-of-the-art
algorithms demonstrate the effectiveness of our approach.
Understanding the Feedforward Artificial Neural Network Model From the Perspective of Network Flow
Authors: Dawei Dai, Weimin Tan, Hong Zhan
Date: 2017-04-26 00:00:00

In recent years, deep learning based on artificial neural network (ANN) has
achieved great success in pattern recognition. However, there is no clear
understanding of such neural computational models. In this paper, we try to
unravel "black-box" structure of Ann model from network flow. Specifically, we
consider the feed forward Ann as a network flow model, which consists of many
directional class-pathways. Each class-pathway encodes one class. The
class-pathway of a class is obtained by connecting the activated neural nodes
in each layer from input to output, where activation value of neural node
(node-value) is defined by the weights of each layer in a trained
ANN-classifier. From the perspective of the class-pathway, training an
ANN-classifier can be regarded as the formulation process of class-pathways of
different classes. By analyzing the the distances of each two class-pathways in
a trained ANN-classifiers, we try to answer the questions, why the classifier
performs so? At last, from the neural encodes view, we define the importance of
each neural node through the class-pathways, which is helpful to optimize the
structure of a classifier. Experiments for two types of ANN model including
multi-layer MLP and CNN verify that the network flow based on class-pathway is
a reasonable explanation for ANN models.