pwc-archive/papers-with-abstracts huggingface dataset:
High-speed Tracking with Multi-kernel Correlation Filters
Authors: Ming Tang, Bin Yu, Fan Zhang, Jinqiao Wang
Date: 2018-06-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: Video Object Tracking, Vocal Bursts Intensity Prediction

Correlation filter (CF) based trackers are currently ranked top in terms of
their performances. Nevertheless, only some of them, such as
KCF~\cite{henriques15} and MKCF~\cite{tangm15}, are able to exploit the
powerful discriminability of non-linear kernels. Although MKCF achieves more
powerful discriminability than KCF through introducing multi-kernel learning
(MKL) into KCF, its improvement over KCF is quite limited and its computational
burden increases significantly in comparison with KCF. In this paper, we will
introduce the MKL into KCF in a different way than MKCF. We reformulate the MKL
version of CF objective function with its upper bound, alleviating the negative
mutual interference of different kernels significantly. Our novel MKCF tracker,
MKCFup, outperforms KCF and MKCF with large margins and can still work at very
high fps. Extensive experiments on public datasets show that our method is
superior to state-of-the-art algorithms for target objects of small move at
very high speed.
Feature Learning and Classification in Neuroimaging: Predicting Cognitive Impairment from Magnetic Resonance Imaging
Authors: Shan Shi, Farouk Nathoo
Date: 2018-06-17 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Due to the rapid innovation of technology and the desire to find and employ
biomarkers for neurodegenerative disease, high-dimensional data classification
problems are routinely encountered in neuroimaging studies. To avoid
over-fitting and to explore relationships between disease and potential
biomarkers, feature learning and selection plays an important role in
classifier construction and is an important area in machine learning. In this
article, we review several important feature learning and selection techniques
including lasso-based methods, PCA, the two-sample t-test, and stacked
auto-encoders. We compare these approaches using a numerical study involving
the prediction of Alzheimer's disease from Magnetic Resonance Imaging.
One-to-one Mapping between Stimulus and Neural State: Memory and Classification
Authors: Sizhong Lan
Date: 2018-05-23 00:00:00
Tasks: General Classification

Synaptic strength can be seen as probability to propagate impulse, and
according to synaptic plasticity, function could exist from propagation
activity to synaptic strength. If the function satisfies constraints such as
continuity and monotonicity, neural network under external stimulus will always
go to fixed point, and there could be one-to-one mapping between external
stimulus and synaptic strength at fixed point. In other words, neural network
"memorizes" external stimulus in its synapses. A biological classifier is
proposed to utilize this mapping.
Negative Learning Rates and P-Learning
Authors: Devon Merrill
Date: 2016-03-27 00:00:00
Tasks: regression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a method of training a differentiable function approximator for a
regression task using negative examples. We effect this training using negative
learning rates. We also show how this method can be used to perform direct
policy learning in a reinforcement learning setting.
Measuring Semantic Coherence of a Conversation
Authors: Svitlana Vakulenko, Maarten de Rijke, Michael Cochez, Vadim Savenkov, Axel Polleres
Date: 2018-06-17 00:00:00
Tasks: Knowledge Graphs

Conversational systems have become increasingly popular as a way for humans
to interact with computers. To be able to provide intelligent responses,
conversational systems must correctly model the structure and semantics of a
conversation. We introduce the task of measuring semantic (in)coherence in a
conversation with respect to background knowledge, which relies on the
identification of semantic relations between concepts introduced during a
conversation. We propose and evaluate graph-based and machine learning-based
approaches for measuring semantic coherence using knowledge graphs, their
vector space embeddings and word embedding models, as sources of background
knowledge. We demonstrate how these approaches are able to uncover different
coherence patterns in conversations on the Ubuntu Dialogue Corpus.
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning
Authors: Kelvin Xu, Ellis Ratner, Anca Dragan, Sergey Levine, Chelsea Finn
Date: 2018-05-31 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A significant challenge for the practical application of reinforcement learning in the real world is the need to specify an oracle reward function that correctly defines a task. Inverse reinforcement learning (IRL) seeks to avoid this challenge by instead inferring a reward function from expert behavior. While appealing, it can be impractically expensive to collect datasets of demonstrations that cover the variation common in the real world (e.g. opening any type of door). Thus in practice, IRL must commonly be performed with only a limited set of demonstrations where it can be exceedingly difficult to unambiguously recover a reward function. In this work, we exploit the insight that demonstrations from other tasks can be used to constrain the set of possible reward functions by learning a "prior" that is specifically optimized for the ability to infer expressive reward functions from limited numbers of demonstrations. We demonstrate that our method can efficiently recover rewards from images for novel tasks and provide intuition as to how our approach is analogous to learning a prior.
Gated Path Planning Networks
Authors: Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric Xing, Ruslan Salakhutdinov
Date: 2018-06-17 00:00:00
Proceeding: ICML 2018 7
Tasks: Sensitivity

Value Iteration Networks (VINs) are effective differentiable path planning
modules that can be used by agents to perform navigation while still
maintaining end-to-end differentiability of the entire architecture. Despite
their effectiveness, they suffer from several disadvantages including training
instability, random seed sensitivity, and other optimization problems. In this
work, we reframe VINs as recurrent-convolutional networks which demonstrates
that VINs couple recurrent convolutions with an unconventional max-pooling
activation. From this perspective, we argue that standard gated recurrent
update equations could potentially alleviate the optimization issues plaguing
VIN. The resulting architecture, which we call the Gated Path Planning Network,
is shown to empirically outperform VIN on a variety of metrics such as learning
speed, hyperparameter sensitivity, iteration count, and even generalization.
Furthermore, we show that this performance gap is consistent across different
maze transition types, maze sizes and even show success on a challenging 3D
environment, where the planner is only provided with first-person RGB images.
An Improved Text Sentiment Classification Model Using TF-IDF and Next Word Negation
Authors: Bijoyan Das, Sarit Chakraborty
Date: 2018-06-17 00:00:00
Tasks: Classification, General Classification, Negation, Sentiment Analysis, Sentiment Classification, text-classification, Text Classification

With the rapid growth of Text sentiment analysis, the demand for automatic
classification of electronic documents has increased by leaps and bound. The
paradigm of text classification or text mining has been the subject of many
research works in recent time. In this paper we propose a technique for text
sentiment classification using term frequency- inverse document frequency
(TF-IDF) along with Next Word Negation (NWN). We have also compared the
performances of binary bag of words model, TF-IDF model and TF-IDF with next
word negation (TF-IDF-NWN) model for text classification. Our proposed model is
then applied on three different text mining algorithms and we found the Linear
Support vector machine (LSVM) is the most appropriate to work with our proposed
model. The achieved results show significant increase in accuracy compared to
earlier methods.
Fast Kernelized Correlation Filters without Boundary Effect
Authors: Ming Tang, Linyu Zheng, Bin Yu, Jinqiao Wang
Date: 2018-06-17 00:00:00
Tasks: Visual Tracking

In recent years, correlation filter based trackers (CF trackers) have attracted much attention from the vision community because of their top performance in both localization accuracy and efficiency. The society of visual tracking, however, still needs to deal with the following difficulty on CF trackers: avoiding or eliminating the boundary effect completely, in the meantime, exploiting non-linear kernels and running efficiently. In this paper, we propose a fast kernelized correlation filter without boundary effect (nBEKCF) to solve this problem. To avoid the boundary effect thoroughly, a set of \emph{real} and \emph{dense} patches is sampled through the traditional sliding window and used as the training samples to train nBEKCF to fit a Gaussian response map. Non-linear kernels can be applied naturally in nBEKCF due to its different theoretical foundation from the existing CF trackers'. To achieve the fast training and detection, a set of cyclic bases is introduced to construct the filter. Two algorithms, ACSII and CCIM, are developed to significantly accelerate the calculation of kernel correlation matrices. ACSII and CCIM fully exploit the density of training samples and cyclic structure of bases, and totally run in space domain. The efficiency of CCIM exceeds that of the FFT counterpart remarkably in our task. Extensive experiments on six public datasets, OTB-2013, OTB-2015, NfS, VOT2018, GOT10k, and TrackingNet, show that compared to the CF trackers designed to relax the boundary effect, BACF and SRDCF, our nBEKCF achieves higher localization accuracy without tricks, in the meanwhile, runs at higher FPS.
MedGAN: Medical Image Translation using GANs
Authors: Karim Armanious, Chenming Jiang, Marc Fischer, Thomas Küstner, Konstantin Nikolaou, Sergios Gatidis, Bin Yang
Date: 2018-06-17 00:00:00
Tasks: Decoder, Denoising, Image Denoising, Image-to-Image Translation, Medical Image Analysis, Style Transfer, Translation

Image-to-image translation is considered a new frontier in the field of
medical image analysis, with numerous potential applications. However, a large
portion of recent approaches offers individualized solutions based on
specialized task-specific architectures or require refinement through
non-end-to-end training. In this paper, we propose a new framework, named
MedGAN, for medical image-to-image translation which operates on the image
level in an end-to-end manner. MedGAN builds upon recent advances in the field
of generative adversarial networks (GANs) by merging the adversarial framework
with a new combination of non-adversarial losses. We utilize a discriminator
network as a trainable feature extractor which penalizes the discrepancy
between the translated medical images and the desired modalities. Moreover,
style-transfer losses are utilized to match the textures and fine-structures of
the desired target images to the translated images. Additionally, we present a
new generator architecture, titled CasNet, which enhances the sharpness of the
translated medical outputs through progressive refinement via encoder-decoder
pairs. Without any application-specific modifications, we apply MedGAN on three
different tasks: PET-CT translation, correction of MR motion artefacts and PET
image denoising. Perceptual analysis by radiologists and quantitative
evaluations illustrate that the MedGAN outperforms other existing translation
approaches.
Task-Relevant Object Discovery and Categorization for Playing First-person Shooter Games
Authors: Junchi Liang, Abdeslam Boularias
Date: 2018-06-17 00:00:00
Tasks: Object Discovery, Optical Flow Estimation

We consider the problem of learning to play first-person shooter (FPS) video
games using raw screen images as observations and keyboard inputs as actions.
The high-dimensionality of the observations in this type of applications leads
to prohibitive needs of training data for model-free methods, such as the deep
Q-network (DQN), and its recurrent variant DRQN. Thus, recent works focused on
learning low-dimensional representations that may reduce the need for data.
This paper presents a new and efficient method for learning such
representations. Salient segments of consecutive frames are detected from their
optical flow, and clustered based on their feature descriptors. The clusters
typically correspond to different discovered categories of objects. Segments
detected in new frames are then classified based on their nearest clusters.
Because only a few categories are relevant to a given task, the importance of a
category is defined as the correlation between its occurrence and the agent's
performance. The result is encoded as a vector indicating objects that are in
the frame and their locations, and used as a side input to DRQN. Experiments on
the game Doom provide a good evidence for the benefit of this approach.
Scraping and Preprocessing Commercial Auction Data for Fraud Classification
Authors: Ahmad Alzahrani, Samira Sadaoui
Date: 2018-06-02 00:00:00
Tasks: Classification, General Classification

In the last three decades, we have seen a significant increase in trading
goods and services through online auctions. However, this business created an
attractive environment for malicious moneymakers who can commit different types
of fraud activities, such as Shill Bidding (SB). The latter is predominant
across many auctions but this type of fraud is difficult to detect due to its
similarity to normal bidding behaviour. The unavailability of SB datasets makes
the development of SB detection and classification models burdensome.
Furthermore, to implement efficient SB detection models, we should produce SB
data from actual auctions of commercial sites. In this study, we first scraped
a large number of eBay auctions of a popular product. After preprocessing the
raw auction data, we build a high-quality SB dataset based on the most reliable
SB strategies. The aim of our research is to share the preprocessed auction
dataset as well as the SB training (unlabelled) dataset, thereby researchers
can apply various machine learning techniques by using authentic data of
auctions and fraud.
Multi-variable LSTM neural network for autoregressive exogenous model
Authors: Tian Guo, Tao Lin
Date: 2018-06-17 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we propose multi-variable LSTM capable of accurate forecasting
and variable importance interpretation for time series with exogenous
variables. Current attention mechanism in recurrent neural networks mostly
focuses on the temporal aspect of data and falls short of characterizing
variable importance. To this end, the multi-variable LSTM equipped with
tensorized hidden states is developed to learn hidden states for individual
variables, which give rise to our mixture temporal and variable attention.
Based on such attention mechanism, we infer and quantify variable importance.
Extensive experiments using real datasets with Granger-causality test and the
synthetic dataset with ground truth demonstrate the prediction performance and
interpretability of multi-variable LSTM in comparison to a variety of
baselines. It exhibits the prospect of multi-variable LSTM as an end-to-end
framework for both forecasting and knowledge discovery.
Geodesic Convex Optimization: Differentiation on Manifolds, Geodesics, and Convexity
Authors: Nisheeth K. Vishnoi
Date: 2018-06-17 00:00:00

Convex optimization is a vibrant and successful area due to the existence of
a variety of efficient algorithms that leverage the rich structure provided by
convexity. Convexity of a smooth set or a function in a Euclidean space is
defined by how it interacts with the standard differential structure in this
space -- the Hessian of a convex function has to be positive semi-definite
everywhere. However, in recent years, there is a growing demand to understand
non-convexity and develop computational methods to optimize non-convex
functions. Intriguingly, there is a type of non-convexity that disappears once
one introduces a suitable differentiable structure and redefines convexity with
respect to the straight lines, or {\em geodesics}, with respect to this
structure. Such convexity is referred to as {\em geodesic convexity}. Interest
in studying it arises due to recent reformulations of some non-convex problems
as geodesically convex optimization problems over geodesically convex sets.
Geodesics on manifolds have been extensively studied in various branches of
Mathematics and Physics. However, unlike convex optimization, understanding
geodesics and geodesic convexity from a computational point of view largely
remains a mystery. The goal of this exposition is to introduce the first part
of geodesic convex optimization -- geodesic convexity -- in a self-contained
manner. We first present a variety of notions from differential and Riemannian
geometry such as differentiation on manifolds, geodesics, and then introduce
geodesic convexity. We conclude by showing that certain non-convex optimization
problems such as computing the Brascamp-Lieb constant and the operator scaling
problem have geodesically convex formulations.
On the mathematics of beauty: beautiful images
Authors: A. M. Khalili
Date: 2017-05-13 00:00:00

In this paper, we will study the simplest kind of beauty which can be found in simple visual patterns. The proposed approach shows that aesthetically appealing patterns deliver higher amount of information over multiple levels in comparison with less aesthetically appealing patterns when the same amount of energy is used. The proposed approach is used to classify aesthetically appealing patterns.
Multimodal Grounding for Language Processing
Authors: Lisa Beinborn, Teresa Botschen, Iryna Gurevych
Date: 2018-06-17 00:00:00
Proceeding: COLING 2018 8
Tasks: Survey

This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language. We categorize the information flow in multimodal processing with respect to cognitive models of human information processing and analyze different methods for combining multimodal representations. Based on this methodological inventory, we discuss the benefit of multimodal grounding for a variety of language processing tasks and the challenges that arise. We particularly focus on multimodal grounding of verbs which play a crucial role for the compositional power of language.
How Could Polyhedral Theory Harness Deep Learning?
Authors: Thiago Serra, Christian Tjandraatmadja, Srikumar Ramalingam
Date: 2018-06-17 00:00:00
Tasks: Deep Learning

The holy grail of deep learning is to come up with an automatic method to
design optimal architectures for different applications. In other words, how
can we effectively dimension and organize neurons along the network layers
based on the computational resources, input size, and amount of training data?
We outline promising research directions based on polyhedral theory and
mixed-integer representability that may offer an analytical approach to this
question, in contrast to the empirical techniques often employed.
Initialization of ReLUs for Dynamical Isometry
Authors: Rebekka Burkholz, Alina Dubatovka
Date: 2018-06-17 00:00:00
Proceeding: NeurIPS 2019 12

Deep learning relies on good initialization schemes and hyperparameter choices prior to training a neural network. Random weight initializations induce random network ensembles, which give rise to the trainability, training speed, and sometimes also generalization ability of an instance. In addition, such ensembles provide theoretical insights into the space of candidate models of which one is selected during training. The results obtained so far rely on mean field approximations that assume infinite layer width and that study average squared signals. We derive the joint signal output distribution exactly, without mean field assumptions, for fully-connected networks with Gaussian weights and biases, and analyze deviations from the mean field results. For rectified linear units, we further discuss limitations of the standard initialization scheme, such as its lack of dynamical isometry, and propose a simple alternative that overcomes these by initial parameter sharing.
Incorporating Chinese Characters of Words for Lexical Sememe Prediction
Authors: Huiming Jin, Hao Zhu, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Fen Lin, Leyu Lin
Date: 2018-06-17 00:00:00
Proceeding: ACL 2018 7
Tasks: Common Sense Reasoning, Prediction

Sememes are minimum semantic units of concepts in human languages, such that
each word sense is composed of one or multiple sememes. Words are usually
manually annotated with their sememes by linguists, and form linguistic
common-sense knowledge bases widely used in various NLP tasks. Recently, the
lexical sememe prediction task has been introduced. It consists of
automatically recommending sememes for words, which is expected to improve
annotation efficiency and consistency. However, existing methods of lexical
sememe prediction typically rely on the external context of words to represent
the meaning, which usually fails to deal with low-frequency and
out-of-vocabulary words. To address this issue for Chinese, we propose a novel
framework to take advantage of both internal character information and external
context information of words. We experiment on HowNet, a Chinese sememe
knowledge base, and demonstrate that our framework outperforms state-of-the-art
baselines by a large margin, and maintains a robust performance even for
low-frequency words.
Neural Style Transfer: A Review
Authors: Yongcheng Jing, Yezhou Yang, Zunlei Feng, Jingwen Ye, Yizhou Yu, Mingli Song
Date: 2017-05-11 00:00:00
Tasks: Style Transfer

The seminal work of Gatys et al. demonstrated the power of Convolutional
Neural Networks (CNNs) in creating artistic imagery by separating and
recombining image content and style. This process of using CNNs to render a
content image in different styles is referred to as Neural Style Transfer
(NST). Since then, NST has become a trending topic both in academic literature
and industrial applications. It is receiving increasing attention and a variety
of approaches are proposed to either improve or extend the original NST
algorithm. In this paper, we aim to provide a comprehensive overview of the
current progress towards NST. We first propose a taxonomy of current algorithms
in the field of NST. Then, we present several evaluation methods and compare
different NST algorithms both qualitatively and quantitatively. The review
concludes with a discussion of various applications of NST and open problems
for future research. A list of papers discussed in this review, corresponding
codes, pre-trained models and more comparison results are publicly available at
https://github.com/ycjing/Neural-Style-Transfer-Papers.