pwc-archive/papers-with-abstracts huggingface dataset:
Exact Reconstruction of Euclidean Distance Geometry Problem Using Low-rank Matrix Completion
Authors: Abiy Tasissa, Rongjie Lai
Date: 2018-04-12 00:00:00
Tasks: Computational chemistry, Low-Rank Matrix Completion, Matrix Completion

The Euclidean distance geometry problem arises in a wide variety of
applications, from determining molecular conformations in computational
chemistry to localization in sensor networks. When the distance information is
incomplete, the problem can be formulated as a nuclear norm minimization
problem. In this paper, this minimization program is recast as a matrix
completion problem of a low-rank $r$ Gram matrix with respect to a suitable
basis. The well known restricted isometry property can not be satisfied in this
scenario. Instead, a dual basis approach is introduced to theoretically analyze
the reconstruction problem. If the Gram matrix satisfies certain coherence
conditions with parameter $\nu$, the main result shows that the underlying
configuration of $n$ points can be recovered with very high probability from
$O(nr\nu\log^{2}(n))$ uniformly random samples. Computationally, simple and
fast algorithms are designed to solve the Euclidean distance geometry problem.
Numerical tests on different three dimensional data and protein molecules
validate effectiveness and efficiency of the proposed algorithms.
Model-Based Action Exploration for Learning Dynamic Motion Skills
Authors: Glen Berseth, Michiel Van de Panne
Date: 2018-01-11 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

Deep reinforcement learning has achieved great strides in solving challenging
motion control tasks. Recently, there has been significant work on methods for
exploiting the data gathered during training, but there has been less work on
how to best generate the data to learn from. For continuous action domains, the
most common method for generating exploratory actions involves sampling from a
Gaussian distribution centred around the mean action output by a policy.
Although these methods can be quite capable, they do not scale well with the
dimensionality of the action space, and can be dangerous to apply on hardware.
We consider learning a forward dynamics model to predict the result,
($x_{t+1}$), of taking a particular action, ($u$), given a specific observation
of the state, ($x_{t}$). With this model we perform internal look-ahead
predictions of outcomes and seek actions we believe have a reasonable chance of
success. This method alters the exploratory action space, thereby increasing
learning speed and enables higher quality solutions to difficult problems, such
as robotic locomotion and juggling.
Model identification for ARMA time series through convolutional neural networks
Authors: Wai Hoh Tang, Adrian Röllin
Date: 2018-04-12 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we use convolutional neural networks to address the problem of model identification for autoregressive moving average time series models. We compare the performance of several neural network architectures, trained on simulated time series, with likelihood based methods, in particular the Akaike and Bayesian information criteria. We find that our neural networks can significantly outperform these likelihood based methods in terms of accuracy and, by orders of magnitude, in terms of speed.
Learning to score the figure skating sports videos
Authors: Chengming Xu, Yanwei Fu, Bing Zhang, Zitian Chen, Yu-Gang Jiang, xiangyang xue
Date: 2018-02-08 00:00:00

This paper targets at learning to score the figure skating sports videos. To
address this task, we propose a deep architecture that includes two
complementary components, i.e., Self-Attentive LSTM and Multi-scale
Convolutional Skip LSTM. These two components can efficiently learn the local
and global sequential information in each video. Furthermore, we present a
large-scale figure skating sports video dataset -- FisV dataset. This dataset
includes 500 figure skating videos with the average length of 2 minutes and 50
seconds. Each video is annotated by two scores of nine different referees,
i.e., Total Element Score(TES) and Total Program Component Score (PCS). Our
proposed model is validated on FisV and MIT-skate datasets. The experimental
results show the effectiveness of our models in learning to score the figure
skating videos.
Combating catastrophic forgetting with developmental compression
Authors: Shawn L. E. Beaulieu, Sam Kriegman, Josh C. Bongard
Date: 2018-04-12 00:00:00

Generally intelligent agents exhibit successful behavior across problems in
several settings. Endemic in approaches to realize such intelligence in
machines is catastrophic forgetting: sequential learning corrupts knowledge
obtained earlier in the sequence, or tasks antagonistically compete for system
resources. Methods for obviating catastrophic forgetting have sought to
identify and preserve features of the system necessary to solve one problem
when learning to solve another, or to enforce modularity such that minimally
overlapping sub-functions contain task specific knowledge. While successful,
both approaches scale poorly because they require larger architectures as the
number of training instances grows, causing different parts of the system to
specialize for separate subsets of the data. Here we present a method for
addressing catastrophic forgetting called developmental compression. It
exploits the mild impacts of developmental mutations to lessen adverse changes
to previously-evolved capabilities and `compresses' specialized neural networks
into a generalized one. In the absence of domain knowledge, developmental
compression produces systems that avoid overt specialization, alleviating the
need to engineer a bespoke system for every task permutation and suggesting
better scalability than existing approaches. We validate this method on a robot
control problem and hope to extend this approach to other machine learning
domains in the future.
VITAL: VIsual Tracking via Adversarial Learning
Authors: Yibing Song, Chao Ma, Xiaohe Wu, Lijun Gong, Linchao Bao, WangMeng Zuo, Chunhua Shen, Rynson Lau, Ming-Hsuan Yang
Date: 2018-04-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification, Visual Tracking

The tracking-by-detection framework consists of two stages, i.e., drawing
samples around the target object in the first stage and classifying each sample
as the target object or as background in the second stage. The performance of
existing trackers using deep classification networks is limited by two aspects.
First, the positive samples in each frame are highly spatially overlapped, and
they fail to capture rich appearance variations. Second, there exists extreme
class imbalance between positive and negative samples. This paper presents the
VITAL algorithm to address these two problems via adversarial learning. To
augment positive samples, we use a generative network to randomly generate
masks, which are applied to adaptively dropout input features to capture a
variety of appearance changes. With the use of adversarial learning, our
network identifies the mask that maintains the most robust features of the
target objects over a long temporal span. In addition, to handle the issue of
class imbalance, we propose a high-order cost sensitive loss to decrease the
effect of easy negative samples to facilitate training the classification
network. Extensive experiments on benchmark datasets demonstrate that the
proposed tracker performs favorably against state-of-the-art approaches.
Deep Neural Networks Motivated by Partial Differential Equations
Authors: Lars Ruthotto, Eldad Haber
Date: 2018-04-12 00:00:00
Tasks: Denoising, image-classification, Image Classification, Image Segmentation, Semantic Segmentation

Partial differential equations (PDEs) are indispensable for modeling many
physical phenomena and also commonly used for solving image processing tasks.
In the latter area, PDE-based approaches interpret image data as
discretizations of multivariate functions and the output of image processing
algorithms as solutions to certain PDEs. Posing image processing problems in
the infinite dimensional setting provides powerful tools for their analysis and
solution. Over the last few decades, the reinterpretation of classical image
processing problems through the PDE lens has been creating multiple celebrated
approaches that benefit a vast area of tasks including image segmentation,
denoising, registration, and reconstruction.
  In this paper, we establish a new PDE-interpretation of a class of deep
convolutional neural networks (CNN) that are commonly used to learn from
speech, image, and video data. Our interpretation includes convolution residual
neural networks (ResNet), which are among the most promising approaches for
tasks such as image classification having improved the state-of-the-art
performance in prestigious benchmark challenges. Despite their recent
successes, deep ResNets still face some critical challenges associated with
their design, immense computational costs and memory requirements, and lack of
understanding of their reasoning.
  Guided by well-established PDE theory, we derive three new ResNet
architectures that fall into two new classes: parabolic and hyperbolic CNNs. We
demonstrate how PDE theory can provide new insights and algorithms for deep
learning and demonstrate the competitiveness of three new CNN architectures
using numerical experiments.
Incomplete Contracting and AI Alignment
Authors: Dylan Hadfield-Menell, Gillian Hadfield
Date: 2018-04-12 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

We suggest that the analysis of incomplete contracting developed by law and
economics researchers can provide a useful framework for understanding the AI
alignment problem and help to generate a systematic approach to finding
solutions. We first provide an overview of the incomplete contracting
literature and explore parallels between this work and the problem of AI
alignment. As we emphasize, misalignment between principal and agent is a core
focus of economic analysis. We highlight some technical results from the
economics literature on incomplete contracts that may provide insights for AI
alignment researchers. Our core contribution, however, is to bring to bear an
insight that economists have been urged to absorb from legal scholars and other
behavioral scientists: the fact that human contracting is supported by
substantial amounts of external structure, such as generally available
institutions (culture, law) that can supply implied terms to fill the gaps in
incomplete contracts. We propose a research agenda for AI alignment work that
focuses on the problem of how to build AI that can replicate the human
cognitive processes that connect individual incomplete contracts with this
supporting external structure.
A Closer Look at Spatiotemporal Convolutions for Action Recognition
Authors: Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann Lecun, Manohar Paluri
Date: 2017-11-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Classification, Action Recognition, Temporal Action Localization

In this paper we discuss several forms of spatiotemporal convolutions for
video analysis and study their effects on action recognition. Our motivation
stems from the observation that 2D CNNs applied to individual frames of the
video have remained solid performers in action recognition. In this work we
empirically demonstrate the accuracy advantages of 3D CNNs over 2D CNNs within
the framework of residual learning. Furthermore, we show that factorizing the
3D convolutional filters into separate spatial and temporal components yields
significantly advantages in accuracy. Our empirical study leads to the design
of a new spatiotemporal convolutional block "R(2+1)D" which gives rise to CNNs
that achieve results comparable or superior to the state-of-the-art on
Sports-1M, Kinetics, UCF101 and HMDB51.
A Capsule Network-based Embedding Model for Search Personalization
Authors: Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dinh Phung
Date: 2018-04-12 00:00:00

Search personalization aims to tailor search results to each specific user
based on the user's personal interests and preferences (i.e., the user
profile). Recent research approaches to search personalization by modelling the
potential 3-way relationship between the submitted query, the user and the
search results (i.e., documents). That relationship is then used to personalize
the search results to that user. In this paper, we introduce a novel embedding
model based on capsule network, which recently is a breakthrough in deep
learning, to model the 3-way relationships for search personalization. In the
model, each user (submitted query or returned document) is embedded by a vector
in the same vector space. The 3-way relationship is described as a triple of
(query, user, document) which is then modeled as a 3-column matrix containing
the three embedding vectors. After that, the 3-column matrix is fed into a deep
learning architecture to re-rank the search results returned by a basis ranker.
Experimental results on query logs from a commercial web search engine show
that our model achieves better performances than the basis ranker as well as
strong search personalization baselines.
Training a Ranking Function for Open-Domain Question Answering
Authors: Phu Mon Htut, Samuel R. Bowman, Kyunghyun Cho
Date: 2018-04-12 00:00:00
Proceeding: NAACL 2018 6
Tasks: Information Retrieval, Open-Domain Question Answering, Question Answering, Reading Comprehension, Retrieval, Semantic Similarity, Semantic Textual Similarity

In recent years, there have been amazing advances in deep learning methods
for machine reading. In machine reading, the machine reader has to extract the
answer from the given ground truth paragraph. Recently, the state-of-the-art
machine reading models achieve human level performance in SQuAD which is a
reading comprehension-style question answering (QA) task. The success of
machine reading has inspired researchers to combine information retrieval with
machine reading to tackle open-domain QA. However, these systems perform poorly
compared to reading comprehension-style QA because it is difficult to retrieve
the pieces of paragraphs that contain the answer to the question. In this
study, we propose two neural network rankers that assign scores to different
passages based on their likelihood of containing the answer to a given
question. Additionally, we analyze the relative importance of semantic
similarity and word level relevance matching in open-domain QA.
The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods
Authors: Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Tomi Kinnunen, Zhen-Hua Ling
Date: 2018-04-12 00:00:00
Tasks: Voice Conversion

We present the Voice Conversion Challenge 2018, designed as a follow up to
the 2016 edition with the aim of providing a common framework for evaluating
and comparing different state-of-the-art voice conversion (VC) systems. The
objective of the challenge was to perform speaker conversion (i.e. transform
the vocal identity) of a source speaker to a target speaker while maintaining
linguistic information. As an update to the previous challenge, we considered
both parallel and non-parallel data to form the Hub and Spoke tasks,
respectively. A total of 23 teams from around the world submitted their
systems, 11 of them additionally participated in the optional Spoke task. A
large-scale crowdsourced perceptual evaluation was then carried out to rate the
submitted converted speech in terms of naturalness and similarity to the target
speaker identity. In this paper, we present a brief summary of the
state-of-the-art techniques for VC, followed by a detailed explanation of the
challenge tasks and the results that were obtained.
Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation
Authors: Zhaoyang Lv, Kihwan Kim, Alejandro Troccoli, Deqing Sun, James M. Rehg, Jan Kautz
Date: 2018-04-12 00:00:00
Proceeding: ECCV 2018 9
Tasks: Optical Flow Estimation, Scene Flow Estimation, Scene Understanding

Estimation of 3D motion in a dynamic scene from a temporal pair of images is
a core task in many scene understanding problems. In real world applications, a
dynamic scene is commonly captured by a moving camera (i.e., panning, tilting
or hand-held), increasing the task complexity because the scene is observed
from different view points. The main challenge is the disambiguation of the
camera motion from scene motion, which becomes more difficult as the amount of
rigidity observed decreases, even with successful estimation of 2D image
correspondences. Compared to other state-of-the-art 3D scene flow estimation
methods, in this paper we propose to \emph{learn} the rigidity of a scene in a
supervised manner from a large collection of dynamic scene data, and directly
infer a rigidity mask from two sequential images with depths. With the learned
network, we show how we can effectively estimate camera motion and projected
scene flow using computed 2D optical flow and the inferred rigidity mask. For
training and testing the rigidity network, we also provide a new semi-synthetic
dynamic scene dataset (synthetic foreground objects with a real background) and
an evaluation split that accounts for the percentage of observed non-rigid
pixels. Through our evaluation we show the proposed framework outperforms
current state-of-the-art scene flow estimation methods in challenging dynamic
scenes.
Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media
Authors: Mai ElSherief, Vivek Kulkarni, Dana Nguyen, William Yang Wang, Elizabeth Belding
Date: 2018-04-11 00:00:00

While social media empowers freedom of expression and individual voices, it
also enables anti-social behavior, online harassment, cyberbullying, and hate
speech. In this paper, we deepen our understanding of online hate speech by
focusing on a largely neglected but crucial aspect of hate speech -- its
target: either "directed" towards a specific person or entity, or "generalized"
towards a group of people sharing a common protected characteristic. We perform
the first linguistic and psycholinguistic analysis of these two forms of hate
speech and reveal the presence of interesting markers that distinguish these
types of hate speech. Our analysis reveals that Directed hate speech, in
addition to being more personal and directed, is more informal, angrier, and
often explicitly attacks the target (via name calling) with fewer analytic
words and more words suggesting authority and influence. Generalized hate
speech, on the other hand, is dominated by religious hate, is characterized by
the use of lethal words such as murder, exterminate, and kill; and quantity
words such as million and many. Altogether, our work provides a data-driven
analysis of the nuances of online-hate speech that enables not only a deepened
understanding of hate speech and its social implications but also its
detection.
End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth Data
Authors: Meysam Madadi, Sergio Escalera, Xavier Baro, Jordi Gonzalez
Date: 2017-05-26 00:00:00
Tasks: 3D Pose Estimation, Data Augmentation, Pose Estimation

Despite recent advances in 3D pose estimation of human hands, especially
thanks to the advent of CNNs and depth cameras, this task is still far from
being solved. This is mainly due to the highly non-linear dynamics of fingers,
which make hand model training a challenging task. In this paper, we exploit a
novel hierarchical tree-like structured CNN, in which branches are trained to
become specialized in predefined subsets of hand joints, called local poses. We
further fuse local pose features, extracted from hierarchical CNN branches, to
learn higher order dependencies among joints in the final pose by end-to-end
training. Lastly, the loss function used is also defined to incorporate
appearance and physical constraints about doable hand motion and deformation.
Finally, we introduce a non-rigid data augmentation approach to increase the
amount of training depth data. Experimental results suggest that feeding a
tree-shaped CNN, specialized in local poses, into a fusion network for modeling
joints correlations and dependencies, helps to increase the precision of final
estimations, outperforming state-of-the-art results on NYU and SyntheticHand
datasets.
English Out-of-Vocabulary Lexical Evaluation Task
Authors: Han Wang, Ye Wang, Xinxiang Zhang, Mi Lu, Yoonsuck Choe, Jingjing Cao
Date: 2018-04-11 00:00:00
Tasks: Attribute, Classification, General Classification, Prediction

Unlike previous unknown nouns tagging task, this is the first attempt to focus on out-of-vocabulary (OOV) lexical evaluation tasks that do not require any prior knowledge. The OOV words are words that only appear in test samples. The goal of tasks is to provide solutions for OOV lexical classification and prediction. The tasks require annotators to conclude the attributes of the OOV words based on their related contexts. Then, we utilize unsupervised word embedding methods such as Word2Vec and Word2GM to perform the baseline experiments on the categorical classification task and OOV words attribute prediction tasks.
Machine learning of neuroimaging to diagnose cognitive impairment and dementia: a systematic review and comparative analysis
Authors: Enrico Pellegrini, Lucia Ballerini, Maria del C. Valdes Hernandez, Francesca M. Chappell, Victor González-Castro, Devasuda Anblagan, Samuel Danso, Susana Muñoz Maniega, Dominic Job, Cyril Pernet, Grant Mair, Tom MacGillivray, Emanuele Trucco, Joanna Wardlaw
Date: 2018-04-05 00:00:00
Tasks: BIG-bench Machine Learning

INTRODUCTION: Advanced machine learning methods might help to identify
dementia risk from neuroimaging, but their accuracy to date is unclear.
  METHODS: We systematically reviewed the literature, 2006 to late 2016, for
machine learning studies differentiating healthy ageing through to dementia of
various types, assessing study quality, and comparing accuracy at different
disease boundaries.
  RESULTS: Of 111 relevant studies, most assessed Alzheimer's disease (AD) vs
healthy controls, used ADNI data, support vector machines and only T1-weighted
sequences. Accuracy was highest for differentiating AD from healthy controls,
and poor for differentiating healthy controls vs MCI vs AD, or MCI converters
vs non-converters. Accuracy increased using combined data types, but not by
data source, sample size or machine learning method.
  DISCUSSION: Machine learning does not differentiate clinically-relevant
disease categories yet. More diverse datasets, combinations of different types
of data, and close clinical integration of machine learning would help to
advance the field.
A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI
Authors: Shervin Minaee, Yao Wang, Anna Choromanska, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W. Lui
Date: 2018-02-08 00:00:00
Tasks: Diffusion  MRI

Mild traumatic brain injury is a growing public health problem with an
estimated incidence of over 1.7 million people annually in US. Diagnosis is
based on clinical history and symptoms, and accurate, concrete measures of
injury are lacking. This work aims to directly use diffusion MR images obtained
within one month of trauma to detect injury, by incorporating deep learning
techniques. To overcome the challenge due to limited training data, we describe
each brain region using the bag of word representation, which specifies the
distribution of representative patch patterns. We apply a convolutional
auto-encoder to learn the patch-level features, from overlapping image patches
extracted from the MR images, to learn features from diffusion MR images of
brain using an unsupervised approach. Our experimental results show that the
bag of word representation using patch level features learnt by the auto
encoder provides similar performance as that using the raw patch patterns, both
significantly outperform earlier work relying on the mean values of MR metrics
in selected brain regions.