pwc-archive/papers-with-abstracts huggingface dataset:
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks
Authors: Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann
Date: 2018-11-26 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: General Classification, image-classification, Image Classification, Knowledge Distillation, object-detection, Object Detection, Semantic Segmentation, Transfer Learning

We introduce an approach to training a given compact network. To this end, we leverage over-parameterization, which typically improves both neural network optimization and generalization. Specifically, we propose to expand each linear layer of the compact network into multiple consecutive linear layers, without adding any nonlinearity. As such, the resulting expanded network, or ExpandNet, can be contracted back to the compact one algebraically at inference. In particular, we introduce two convolutional expansion strategies and demonstrate their benefits on several tasks, including image classification, object detection, and semantic segmentation. As evidenced by our experiments, our approach outperforms both training the compact network from scratch and performing knowledge distillation from a teacher. Furthermore, our linear over-parameterization empirically reduces gradient confusion during training and improves the network generalization.
Robust Cross-View Gait Recognition with Evidence: A Discriminant Gait GAN (DiGGAN) Approach
Authors: BingZhang Hu, Yu Guan, Yan Gao, Yang Long, Nicholas Lane, Thomas Ploetz
Date: 2018-11-26 00:00:00
Tasks: Gait Identification, Gait Recognition, Generative Adversarial Network

Gait as a biometric trait has attracted much attention in many security and privacy applications such as identity recognition and authentication, during the last few decades. Because of its nature as a long-distance biometric trait, gait can be easily collected and used to identify individuals non-intrusively through CCTV cameras. However, it is very difficult to develop robust automated gait recognition systems, since gait may be affected by many covariate factors such as clothing, walking speed, camera view angle etc. Out of them, large view angle changes has been deemed as the most challenging factor as it can alter the overall gait appearance substantially. Existing works on gait recognition are far from enough to provide satisfying performances because of such view changes. Furthermore, very few works have considered evidences -- the demonstrable information revealing the reliabilities of decisions, which are regarded as important demands in machine learning-based recognition/authentication applications. To address these issues, in this paper we propose a Discriminant Gait Generative Adversarial Network, namely DiGGAN, which can effectively extract view-invariant features for cross-view gait recognition; and more importantly, to transfer gait images to different views -- serving as evidences and showing how the decisions have been made. Quantitative experiments have been conducted on the two most popular cross-view gait datasets, the OU-MVLP and CASIA-B, where the proposed DiGGAN has outperformed state-of-the-art methods. Qualitative analysis has also been provided and demonstrates the proposed DiGGAN's capability in providing evidences.
Sentence Encoding with Tree-constrained Relation Networks
Authors: Lei Yu, Cyprien de Masson d'Autume, Chris Dyer, Phil Blunsom, Lingpeng Kong, Wang Ling
Date: 2018-11-26 00:00:00
Tasks: General Classification, Machine Translation, Relation, Sentence, Sentence Classification, Sentence-Pair Classification, Translation

The meaning of a sentence is a function of the relations that hold between
its words. We instantiate this relational view of semantics in a series of
neural models based on variants of relation networks (RNs) which represent a
set of objects (for us, words forming a sentence) in terms of representations
of pairs of objects. We propose two extensions to the basic RN model for
natural language. First, building on the intuition that not all word pairs are
equally informative about the meaning of a sentence, we use constraints based
on both supervised and unsupervised dependency syntax to control which
relations influence the representation. Second, since higher-order relations
are poorly captured by a sum of pairwise relations, we use a recurrent
extension of RNs to propagate information so as to form representations of
higher order relations. Experiments on sentence classification, sentence pair
classification, and machine translation reveal that, while basic RNs are only
modestly effective for sentence representation, recurrent RNs with latent
syntax are a reliably powerful representational device.
Scan2Mesh: From Unstructured Range Scans to 3D Meshes
Authors: Angela Dai, Matthias Nießner
Date: 2018-11-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: Graph Neural Network

We introduce Scan2Mesh, a novel data-driven generative approach which
transforms an unstructured and potentially incomplete range scan into a
structured 3D mesh representation. The main contribution of this work is a
generative neural network architecture whose input is a range scan of a 3D
object and whose output is an indexed face set conditioned on the input scan.
In order to generate a 3D mesh as a set of vertices and face indices, the
generative model builds on a series of proxy losses for vertices, edges, and
faces. At each stage, we realize a one-to-one discrete mapping between the
predicted and ground truth data points with a combination of convolutional- and
graph neural network architectures. This enables our algorithm to predict a
compact mesh representation similar to those created through manual artist
effort using 3D modeling software. Our generated mesh results thus produce
sharper, cleaner meshes with a fundamentally different structure from those
generated through implicit functions, a first step in bridging the gap towards
artist-created CAD models.
A Framework for Implementing Machine Learning on Omics Data
Authors: Geoffroy Dubourg-Felonneau, Timothy Cannings, Fergal Cotter, Hannah Thompson, Nirmesh Patel, John W Cassidy, Harry W Clifford
Date: 2018-11-26 00:00:00
Tasks: BIG-bench Machine Learning

The potential benefits of applying machine learning methods to -omics data
are becoming increasingly apparent, especially in clinical settings. However,
the unique characteristics of these data are not always well suited to machine
learning techniques. These data are often generated across different
technologies in different labs, and frequently with high dimensionality. In
this paper we present a framework for combining -omics data sets, and for
handling high dimensional data, making -omics research more accessible to
machine learning applications. We demonstrate the success of this framework
through integration and analysis of multi-analyte data for a set of 3,533
breast cancers. We then use this data-set to predict breast cancer patient
survival for individuals at risk of an impending event, with higher accuracy
and lower variance than methods trained on individual data-sets. We hope that
our pipelines for data-set generation and transformation will open up -omics
data to machine learning researchers. We have made these freely available for
noncommercial use at www.ccg.ai.
Context-Aware Crowd Counting
Authors: Weizhe Liu, Mathieu Salzmann, Pascal Fua
Date: 2018-11-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: Crowd Counting

State-of-the-art methods for counting people in crowded scenes rely on deep
networks to estimate crowd density. They typically use the same filters over
the whole image or over large image patches. Only then do they estimate local
scale to compensate for perspective distortion. This is typically achieved by
training an auxiliary classifier to select, for predefined image patches, the
best kernel size among a limited set of choices. As such, these methods are not
end-to-end trainable and restricted in the scope of context they can leverage.
  In this paper, we introduce an end-to-end trainable deep architecture that
combines features obtained using multiple receptive field sizes and learns the
importance of each such feature at each image location. In other words, our
approach adaptively encodes the scale of the contextual information required to
accurately predict crowd density. This yields an algorithm that outperforms
state-of-the-art crowd counting methods, especially when perspective effects
are strong.
Deep Laplacian Pyramid Network for Text Images Super-Resolution
Authors: Hanh T. M. Tran, Tien Ho-Phuoc
Date: 2018-11-26 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Convolutional neural networks have recently demonstrated interesting results
for single image super-resolution. However, these networks were trained to deal
with super-resolution problem on natural images. In this paper, we adapt a deep
network, which was proposed for natural images superresolution, to single text
image super-resolution. To evaluate the network, we present our database for
single text image super-resolution. Moreover, we propose to combine Gradient
Difference Loss (GDL) with L1/L2 loss to enhance edges in super-resolution
image. Quantitative and qualitative evaluations on our dataset show that adding
the GDL improves the super-resolution results.
Compact and Efficient Encodings for Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models
Authors: Buser Say, Scott Sanner
Date: 2018-11-26 00:00:00
Tasks: ARC, Computational Efficiency

In this paper, we leverage the efficiency of Binarized Neural Networks (BNNs)
to learn complex state transition models of planning domains with discretized
factored state and action spaces. In order to directly exploit this transition
structure for planning, we present two novel compilations of the learned
factored planning problem with BNNs based on reductions to Weighted Partial
Maximum Boolean Satisfiability (FD-SAT-Plan+) as well as Binary Linear
Programming (FD-BLP-Plan+). Theoretically, we show that our SAT-based
Bi-Directional Neuron Activation Encoding is asymptotically the most compact
encoding in the literature and maintains the generalized arc-consistency
property through unit propagation -- an important property that facilitates
efficiency in SAT solvers. Experimentally, we validate the computational
efficiency of our Bi-Directional Neuron Activation Encoding in comparison to an
existing neuron activation encoding and demonstrate the effectiveness of
learning complex transition models with BNNs. We test the runtime efficiency of
both FD-SAT-Plan+ and FD-BLP-Plan+ on the learned factored planning problem
showing that FD-SAT-Plan+ scales better with increasing BNN size and
complexity. Finally, we present a finite-time incremental constraint generation
algorithm based on generalized landmark constraints to improve the planning
accuracy of our encodings through simulated or real-world interaction.
Combining neural and knowledge-based approaches to Named Entity Recognition in Polish
Authors: Sławomir Dadas
Date: 2018-11-26 00:00:00
Tasks: Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Embeddings

Named entity recognition (NER) is one of the tasks in natural language
processing that can greatly benefit from the use of external knowledge sources.
We propose a named entity recognition framework composed of knowledge-based
feature extractors and a deep learning model including contextual word
embeddings, long short-term memory (LSTM) layers and conditional random fields
(CRF) inference layer. We use an entity linking module to integrate our system
with Wikipedia. The combination of effective neural architecture and external
resources allows us to obtain state-of-the-art results on recognition of Polish
proper names. We evaluate our model on data from PolEval 2018 NER challenge on
which it outperforms other methods, reducing the error rate by 22.4% compared
to the winning solution. Our work shows that combining neural NER model and
entity linking model with a knowledge base is more effective in recognizing
named entities than using NER model alone.
Towards Machine Learning Prediction of Deep Brain Stimulation (DBS) Intra-operative Efficacy Maps
Authors: Camilo Bermudez, William Rodriguez, Yuankai Huo, Allison E. Hainline, Rui Li, Robert Shults, Pierre D. DHaese, Peter E. Konrad, Benoit M. Dawant, Bennett A. Landman
Date: 2018-11-26 00:00:00
Tasks: Anatomy, BIG-bench Machine Learning, General Classification

Deep brain stimulation (DBS) has the potential to improve the quality of life
of people with a variety of neurological diseases. A key challenge in DBS is in
the placement of a stimulation electrode in the anatomical location that
maximizes efficacy and minimizes side effects. Pre-operative localization of
the optimal stimulation zone can reduce surgical times and morbidity. Current
methods of producing efficacy probability maps follow an anatomical guidance on
magnetic resonance imaging (MRI) to identify the areas with the highest
efficacy in a population. In this work, we propose to revisit this problem as a
classification problem, where each voxel in the MRI is a sample informed by the
surrounding anatomy. We use a patch-based convolutional neural network to
classify a stimulation coordinate as having a positive reduction in symptoms
during surgery. We use a cohort of 187 patients with a total of 2,869
stimulation coordinates, upon which 3D patches were extracted and associated
with an efficacy score. We compare our results with a registration-based method
of surgical planning. We show an improvement in the classification of
intraoperative stimulation coordinates as a positive response in reduction of
symptoms with AUC of 0.670 compared to a baseline registration-based approach,
which achieves an AUC of 0.627 (p < 0.01). Although additional validation is
needed, the proposed classification framework and deep learning method appear
well-suited for improving pre-surgical planning and personalize treatment
strategies.
A Convolutional Neural Network based Live Object Recognition System as Blind Aid
Authors: Kedar Potdar, Chinmay D. Pai, Sukrut Akolkar
Date: 2018-11-26 00:00:00
Tasks: Object, object-detection, Object Detection, Object Recognition, Real-Time Object Detection

This paper introduces a live object recognition system that serves as a blind
aid. Visually impaired people heavily rely on their other senses such as touch
and auditory signals for understanding the environment around them. The act of
knowing what object is in front of the blind person without touching it (by
hand or some other tool) is very difficult. In some cases, the physical contact
between the person and object can be dangerous, and even lethal.
  This project employs a Convolutional Neural Network for recognition of
pre-trained objects on the ImageNet dataset. A camera, aligned with the
system's predetermined orientation serves as input to the computer system,
which has the object recognition Neural Network deployed to carry out real-time
object detection. Output from the network can then be parsed to present to the
visually impaired person either in the form of audio or Braille text.
Robustness against the channel effect in pathological voice detection
Authors: Yi-Te Hsu, Zining Zhu, Chi-Te Wang, Shih-Hau Fang, Frank Rudzicz, Yu Tsao
Date: 2018-11-26 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Many people are suffering from voice disorders, which can adversely affect
the quality of their lives. In response, some researchers have proposed
algorithms for automatic assessment of these disorders, based on voice signals.
However, these signals can be sensitive to the recording devices. Indeed, the
channel effect is a pervasive problem in machine learning for healthcare. In
this study, we propose a detection system for pathological voice, which is
robust against the channel effect. This system is based on a bidirectional LSTM
network. To increase the performance robustness against channel mismatch, we
integrate domain adversarial training (DAT) to eliminate the differences
between the devices. When we train on data recorded on a high-quality
microphone and evaluate on smartphone data without labels, our robust detection
system increases the PR-AUC from 0.8448 to 0.9455 (and 0.9522 with target
sample labels). To the best of our knowledge, this is the first study applying
unsupervised domain adaptation to pathological voice detection. Notably, our
system does not need target device sample labels, which allows for
generalization to many new devices.
Automatic segmentation of the Foveal Avascular Zone in ophthalmological OCT-A images
Authors: Macarena Díaz, Jorge Novo, Paula Cutrín, Francisco Gómez-Ulla, Manuel G. Penedo, Marcos Ortega
Date: 2018-11-26 00:00:00

Angiography by Optical Coherence Tomography is a non-invasive retinal imaging
modality of recent appearance that allows the visualization of the vascular
structure at predefined depths based on the detection of the blood movement.
OCT-A images constitute a suitable scenario to analyse the retinal vascular
properties of regions of interest, measuring the characteristics of the foveal
vascular and avascular zones. Extracted parameters of this region can be used
as prognostic factors that determine if the patient suffers from certain
pathologies, indicating the associated pathological degree. The manual
extraction of these biomedical parameters is a long, tedious and subjective
process, introducing a significant intra and inter-expert variability, which
penalizes the utility of the measurements. In addition, the absence of tools
that automatically facilitate these calculations encourages the creation of
computer-aided diagnosis frameworks that ease the doctor's work, increasing
their productivity and making viable the use of this type of vascular
biomarkers.
  We propose a fully automatic system that identifies and precisely segments
the region of the foveal avascular zone (FAZ) using a novel ophthalmological
image modality as is OCT-A. The system combines different image processing
techniques to firstly identify the region where the FAZ is contained and,
secondly, proceed with the extraction of its precise contour. The system was
validated using a representative set of 168 OCT-A images, providing accurate
results with the best correlation with the manual measurements of two experts
clinician of 0.93 as well as a Jaccard's index of 0.82 of the best experimental
case. This tool provides an accurate FAZ measurement with the desired
objectivity and reproducibility, being very useful for the analysis of relevant
vascular diseases through the study of the retinal microcirculation.
ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers
Authors: Dominika Tkaczyk, Rohit Gupta, Riccardo Cinti, Joeran Beel
Date: 2018-11-26 00:00:00
Tasks: Meta-Learning, Recommendation Systems

Bibliographic reference parsers extract machine-readable metadata such as
author names, title, journal, and year from bibliographic reference strings. To
extract the metadata, the parsers apply heuristics or machine learning.
However, no reference parser, and no algorithm, consistently gives the best
results in every scenario. For instance, one tool may be best in extracting
titles in ACM citation style, but only third best when APA is used. Another
tool may be best in extracting English author names, while another one is best
for noisy data (i.e. inconsistent citation styles). In this paper, which is an
extended version of our recent RecSys poster, we address the problem of
reference parsing from a recommender-systems and meta-learning perspective. We
propose ParsRec, a meta-learning based recommender-system that recommends the
potentially most effective parser for a given reference string. ParsRec
recommends one out of 10 open-source parsers: Anystyle-Parser, Biblio, CERMINE,
Citation, Citation-Parser, GROBID, ParsCit, PDFSSA4MET, Reference Tagger, and
Science Parse. We evaluate ParsRec on 105k references from chemistry. We
propose two approaches to meta-learning recommendations. The first approach
learns the best parser for an entire reference string. The second approach
learns the best parser for each metadata type in a reference string. The second
approach achieved a 2.6% increase in F1 (0.909 vs. 0.886) over the best single
parser (GROBID), reducing the false positive rate by 20.2% (0.075 vs. 0.094),
and the false negative rate by 18.9% (0.107 vs. 0.132).
The Architecture of Mr. DLib's Scientific Recommender-System API
Authors: Joeran Beel, Andrew Collins, Akiko Aizawa
Date: 2018-11-26 00:00:00
Tasks: Articles, Recommendation Systems

Recommender systems in academia are not widely available. This may be in part
due to the difficulty and cost of developing and maintaining recommender
systems. Many operators of academic products such as digital libraries and
reference managers avoid this effort, although a recommender system could
provide significant benefits to their users. In this paper, we introduce Mr.
DLib's "Recommendations as-a-Service" (RaaS) API that allows operators of
academic products to easily integrate a scientific recommender system into
their products. Mr. DLib generates recommendations for research articles but in
the future, recommendations may include call for papers, grants, etc. Operators
of academic products can request recommendations from Mr. DLib and display
these recommendations to their users. Mr. DLib can be integrated in just a few
hours or days; creating an equivalent recommender system from scratch would
require several months for an academic operator. Mr. DLib has been used by
GESIS Sowiport and by the reference manager JabRef. Mr. DLib is open source and
its goal is to facilitate the application of, and research on, scientific
recommender systems. In this paper, we present the motivation for Mr. DLib, the
architecture and details about the effectiveness. Mr. DLib has delivered 94m
recommendations over a span of two years with an average click-through rate of
0.12%.
Unsupervised learning with sparse space-and-time autoencoders
Authors: Benjamin Graham
Date: 2018-11-26 00:00:00
Tasks: Handwriting Recognition, Segmentation

We use spatially-sparse two, three and four dimensional convolutional
autoencoder networks to model sparse structures in 2D space, 3D space, and
3+1=4 dimensional space-time. We evaluate the resulting latent spaces by
testing their usefulness for downstream tasks. Applications are to handwriting
recognition in 2D, segmentation for parts in 3D objects, segmentation for
objects in 3D scenes, and body-part segmentation for 4D wire-frame models
generated from motion capture data.
EFANet: Exchangeable Feature Alignment Network for Arbitrary Style Transfer
Authors: Zhijie Wu, Chunjin Song, Yang Zhou, Minglun Gong, Hui Huang
Date: 2018-11-26 00:00:00
Tasks: Style Transfer

Style transfer has been an important topic both in computer vision and graphics. Since the seminal work of Gatys et al. first demonstrates the power of stylization through optimization in the deep feature space, quite a few approaches have achieved real-time arbitrary style transfer with straightforward statistic matching techniques. In this work, our key observation is that only considering features in the input style image for the global deep feature statistic matching or local patch swap may not always ensure a satisfactory style transfer; see e.g., Figure 1. Instead, we propose a novel transfer framework, EFANet, that aims to jointly analyze and better align exchangeable features extracted from content and style image pair. In this way, the style features from the style image seek for the best compatibility with the content information in the content image, leading to more structured stylization results. In addition, a new whitening loss is developed for purifying the computed content features and better fusion with styles in feature space. Qualitative and quantitative experiments demonstrate the advantages of our approach.