pwc-archive/papers-with-abstracts huggingface dataset:
Retrofitting Concept Vector Representations of Medical Concepts to Improve Estimates of Semantic Similarity and Relatedness
Authors: Zhiguo Yu, Byron C. Wallace, Todd Johnson, Trevor Cohen
Date: 2017-09-21 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Estimation of semantic similarity and relatedness between biomedical concepts
has utility for many informatics applications. Automated methods fall into two
categories: methods based on distributional statistics drawn from text corpora,
and methods using the structure of existing knowledge resources. Methods in the
former category disregard taxonomic structure, while those in the latter fail
to consider semantically relevant empirical information. In this paper, we
present a method that retrofits distributional context vector representations
of biomedical concepts using structural information from the UMLS
Metathesaurus, such that the similarity between vector representations of
linked concepts is augmented. We evaluated it on the UMNSRS benchmark. Our
results demonstrate that retrofitting of concept vector representations leads
to better correlation with human raters for both similarity and relatedness,
surpassing the best results reported to date. They also demonstrate a clear
improvement in performance on this reference standard for retrofitted vector
representations, as compared to those without retrofitting.
Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks
Authors: Sahand Sharifzadeh, Ioannis Chiotellis, Rudolph Triebel, Daniel Cremers
Date: 2016-12-12 00:00:00
Tasks: Autonomous Driving, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose an inverse reinforcement learning (IRL) approach using Deep
Q-Networks to extract the rewards in problems with large state spaces. We
evaluate the performance of this approach in a simulation-based autonomous
driving scenario. Our results resemble the intuitive relation between the
reward function and readings of distance sensors mounted at different poses on
the car. We also show that, after a few learning rounds, our simulated agent
generates collision-free motions and performs human-like lane change behaviour.
Efficient Column Generation for Cell Detection and Segmentation
Authors: Chong Zhang, Shaofei Wang, Miguel A. Gonzalez-Ballester, Julian Yarkony
Date: 2017-09-21 00:00:00
Tasks: Cell Detection, Instance Segmentation, Segmentation, Semantic Segmentation

We study the problem of instance segmentation in biological images with
crowded and compact cells. We formulate this task as an integer program where
variables correspond to cells and constraints enforce that cells do not
overlap. To solve this integer program, we propose a column generation
formulation where the pricing program is solved via exact optimization of very
small scale integer programs. Column generation is tightened using odd set
inequalities which fit elegantly into pricing problem optimization. Our column
generation approach achieves fast stable anytime inference for our instance
segmentation problems. We demonstrate on three distinct light microscopy
datasets, with several hundred cells each, that our proposed algorithm rapidly
achieves or exceeds state of the art accuracy.
Playing for Benchmarks
Authors: Stephan R. Richter, Zeeshan Hayder, Vladlen Koltun
Date: 2017-09-21 00:00:00
Proceeding: ICCV 2017 10
Tasks: Instance Segmentation, object-detection, Object Detection, Optical Flow Estimation, Semantic Segmentation, Visual Odometry

We present a benchmark suite for visual perception. The benchmark is based on
more than 250K high-resolution video frames, all annotated with ground-truth
data for both low-level and high-level vision tasks, including optical flow,
semantic instance segmentation, object detection and tracking, object-level 3D
scene layout, and visual odometry. Ground-truth data for all tasks is available
for every frame. The data was collected while driving, riding, and walking a
total of 184 kilometers in diverse ambient conditions in a realistic virtual
world. To create the benchmark, we have developed a new approach to collecting
ground-truth data from simulated worlds without access to their source code or
content. We conduct statistical analyses that show that the composition of the
scenes in the benchmark closely matches the composition of corresponding
physical environments. The realism of the collected data is further validated
via perceptual experiments. We analyze the performance of state-of-the-art
methods for multiple tasks, providing reference baselines and highlighting
challenges for future research. The supplementary video can be viewed at
https://youtu.be/T9OybWv923Y
A statistical interpretation of spectral embedding: the generalised random dot product graph
Authors: Patrick Rubin-Delanchy, Joshua Cape, Minh Tang, Carey E. Priebe
Date: 2017-09-16 00:00:00
Tasks: Link Prediction, Position, Stochastic Block Model

Spectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph. This paper proposes a generalisation of the latent position network model known as the random dot product graph, to allow interpretation of those vector representations as latent position estimates. The generalisation is needed to model heterophilic connectivity (e.g., `opposites attract') and to cope with negative eigenvalues more generally. We show that, whether the adjacency or normalised Laplacian matrix is used, spectral embedding produces uniformly consistent latent position estimates with asymptotically Gaussian error (up to identifiability). The standard and mixed membership stochastic block models are special cases in which the latent positions take only $K$ distinct vector values, representing communities, or live in the $(K-1)$-simplex with those vertices, respectively. Under the stochastic block model, our theory suggests spectral clustering using a Gaussian mixture model (rather than $K$-means) and, under mixed membership, fitting the minimum volume enclosing simplex, existing recommendations previously only supported under non-negative-definite assumptions. Empirical improvements in link prediction (over the random dot product graph), and the potential to uncover richer latent structure (than posited under the standard or mixed membership stochastic block models) are demonstrated in a cyber-security example.
Speech Recognition Challenge in the Wild: Arabic MGB-3
Authors: Ahmed Ali, Stephan Vogel, Steve Renals
Date: 2017-09-21 00:00:00
Tasks: Arabic Speech Recognition, Dialect Identification, speech-recognition, Speech Recognition

This paper describes the Arabic MGB-3 Challenge - Arabic Speech Recognition
in the Wild. Unlike last year's Arabic MGB-2 Challenge, for which the
recognition task was based on more than 1,200 hours broadcast TV news
recordings from Aljazeera Arabic TV programs, MGB-3 emphasises dialectal Arabic
using a multi-genre collection of Egyptian YouTube videos. Seven genres were
used for the data collection: comedy, cooking, family/kids, fashion, drama,
sports, and science (TEDx). A total of 16 hours of videos, split evenly across
the different genres, were divided into adaptation, development and evaluation
data sets. The Arabic MGB-Challenge comprised two tasks: A) Speech
transcription, evaluated on the MGB-3 test set, along with the 10 hour MGB-2
test set to report progress on the MGB-2 evaluation; B) Arabic dialect
identification, introduced this year in order to distinguish between four major
Arabic dialects - Egyptian, Levantine, North African, Gulf, as well as Modern
Standard Arabic. Two hours of audio per dialect were released for development
and a further two hours were used for evaluation. For dialect identification,
both lexical features and i-vector bottleneck features were shared with
participants in addition to the raw audio recordings. Overall, thirteen teams
submitted ten systems to the challenge. We outline the approaches adopted in
each system, and summarise the evaluation results.
Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer's Disease
Authors: Jorge Samper-González, Ninon Burgos, Sabrina Fontanella, Hugo Bertin, Marie-Odile Habert, Stanley Durrleman, Theodoros Evgeniou, Olivier Colliot
Date: 2017-09-21 00:00:00
Tasks: Benchmarking, Classification, General Classification

In recent years, the number of papers on Alzheimer's disease classification
has increased dramatically, generating interesting methodological ideas on the
use machine learning and feature extraction methods. However, practical impact
is much more limited and, eventually, one could not tell which of these
approaches are the most efficient. While over 90\% of these works make use of
ADNI an objective comparison between approaches is impossible due to variations
in the subjects included, image pre-processing, performance metrics and
cross-validation procedures. In this paper, we propose a framework for
reproducible classification experiments using multimodal MRI and PET data from
ADNI. The core components are: 1) code to automatically convert the full ADNI
database into BIDS format; 2) a modular architecture based on Nipype in order
to easily plug-in different classification and feature extraction tools; 3)
feature extraction pipelines for MRI and PET data; 4) baseline classification
approaches for unimodal and multimodal features. This provides a flexible
framework for benchmarking different feature extraction and classification
tools in a reproducible manner. We demonstrate its use on all (1519) baseline
T1 MR images and all (1102) baseline FDG PET images from ADNI 1, GO and 2 with
SPM-based feature extraction pipelines and three different classification
techniques (linear SVM, anatomically regularized SVM and multiple kernel
learning SVM). The highest accuracies achieved were: 91% for AD vs CN, 83% for
MCIc vs CN, 75% for MCIc vs MCInc, 94% for AD-A$\beta$+ vs CN-A$\beta$- and 72%
for MCIc-A$\beta$+ vs MCInc-A$\beta$+. The code is publicly available at
https://gitlab.icm-institute.org/aramislab/AD-ML (depends on the Clinica
software platform, publicly available at http://www.clinica.run).
Neural network identification of people hidden from view with a single-pixel, single-photon detector
Authors: Piergiorgio Caramazza, Alessandro Boccolini, Daniel Buschek, Matthias Hullin, Catherine Higham, Robert Henderson, Roderick Murray-Smith, Daniele Faccio
Date: 2017-09-21 00:00:00
Tasks: Network Identification, Position, Retrieval

Light scattered from multiple surfaces can be used to retrieve information of
hidden environments. However, full three-dimensional retrieval of an object
hidden from view by a wall has only been achieved with scanning systems and
requires intensive computational processing of the retrieved data. Here we use
a non-scanning, single-photon single-pixel detector in combination with an
artificial neural network: this allows us to locate the position and to also
simultaneously provide the actual identity of a hidden person, chosen from a
database of people (N=3). Artificial neural networks applied to specific
computational imaging problems can therefore enable novel imaging capabilities
with hugely simplified hardware and processing times
Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning
Authors: Maximilian Hüttenrauch, Adrian Šošić, Gerhard Neumann
Date: 2017-09-21 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Swarm systems constitute a challenging problem for reinforcement learning
(RL) as the algorithm needs to learn decentralized control policies that can
cope with limited local sensing and communication abilities of the agents.
While it is often difficult to directly define the behavior of the agents,
simple communication protocols can be defined more easily using prior knowledge
about the given task. In this paper, we propose a number of simple
communication protocols that can be exploited by deep reinforcement learning to
find decentralized control policies in a multi-robot swarm environment. The
protocols are based on histograms that encode the local neighborhood relations
of the agents and can also transmit task-specific information, such as the
shortest distance and direction to a desired target. In our framework, we use
an adaptation of Trust Region Policy Optimization to learn complex
collaborative tasks, such as formation building and building a communication
link. We evaluate our findings in a simulated 2D-physics environment, and
compare the implications of different communication protocols.
Convolutional neural networks that teach microscopes how to image
Authors: Roarke Horstmeyer, Richard Y. Chen, Barbara Kappes, Benjamin Judkewitz
Date: 2017-09-21 00:00:00

Deep learning algorithms offer a powerful means to automatically analyze the
content of medical images. However, many biological samples of interest are
primarily transparent to visible light and contain features that are difficult
to resolve with a standard optical microscope. Here, we use a convolutional
neural network (CNN) not only to classify images, but also to optimize the
physical layout of the imaging device itself. We increase the classification
accuracy of a microscope's recorded images by merging an optical model of image
formation into the pipeline of a CNN. The resulting network simultaneously
determines an ideal illumination arrangement to highlight important sample
features during image acquisition, along with a set of convolutional weights to
classify the detected images post-capture. We demonstrate our joint
optimization technique with an experimental microscope configuration that
automatically identifies malaria-infected cells with 5-10% higher accuracy than
standard and alternative microscope lighting designs.
Human Pose Estimation using Global and Local Normalization
Authors: Ke Sun, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Dong Liu, Jingdong Wang
Date: 2017-09-21 00:00:00
Proceeding: ICCV 2017 10
Tasks: Pose Estimation

In this paper, we address the problem of estimating the positions of human
joints, i.e., articulated pose estimation. Recent state-of-the-art solutions
model two key issues, joint detection and spatial configuration refinement,
together using convolutional neural networks. Our work mainly focuses on
spatial configuration refinement by reducing variations of human poses
statistically, which is motivated by the observation that the scattered
distribution of the relative locations of joints e.g., the left wrist is
distributed nearly uniformly in a circular area around the left shoulder) makes
the learning of convolutional spatial models hard. We present a two-stage
normalization scheme, human body normalization and limb normalization, to make
the distribution of the relative joint locations compact, resulting in easier
learning of convolutional spatial models and more accurate pose estimation. In
addition, our empirical results show that incorporating multi-scale supervision
and multi-scale fusion into the joint detection network is beneficial.
Experiment results demonstrate that our method consistently outperforms
state-of-the-art methods on the benchmarks.
Temporal Multimodal Fusion for Video Emotion Classification in the Wild
Authors: Valentin Vielzeuf, Stéphane Pateux, Frédéric Jurie
Date: 2017-09-21 00:00:00
Tasks: Classification, Emotion Classification, General Classification

This paper addresses the question of emotion classification. The task
consists in predicting emotion labels (taken among a set of possible labels)
best describing the emotions contained in short video clips. Building on a
standard framework -- lying in describing videos by audio and visual features
used by a supervised classifier to infer the labels -- this paper investigates
several novel directions. First of all, improved face descriptors based on 2D
and 3D Convo-lutional Neural Networks are proposed. Second, the paper explores
several fusion methods, temporal and multimodal, including a novel hierarchical
method combining features and scores. In addition, we carefully reviewed the
different stages of the pipeline and designed a CNN architecture adapted to the
task; this is important as the size of the training set is small compared to
the difficulty of the problem, making generalization difficult. The so-obtained
model ranked 4th at the 2017 Emotion in the Wild challenge with the accuracy of
58.8 %.
Visual Question Generation as Dual Task of Visual Question Answering
Authors: Yikang Li, Nan Duan, Bolei Zhou, Xiao Chu, Wanli Ouyang, Xiaogang Wang
Date: 2017-09-21 00:00:00
Proceeding: CVPR 2018 6
Tasks: Question Answering, Question Generation, Question-Generation, Visual Question Answering, Visual Question Answering (VQA)

Recently visual question answering (VQA) and visual question generation (VQG)
are two trending topics in the computer vision, which have been explored
separately. In this work, we propose an end-to-end unified framework, the
Invertible Question Answering Network (iQAN), to leverage the complementary
relations between questions and answers in images by jointly training the model
on VQA and VQG tasks. Corresponding parameter sharing scheme and regular terms
are proposed as constraints to explicitly leverage Q,A's dependencies to guide
the training process. After training, iQAN can take either question or answer
as input, then output the counterpart. Evaluated on the large-scale visual
question answering datasets CLEVR and VQA2, our iQAN improves the VQA accuracy
over the baselines. We also show the dual learning framework of iQAN can be
generalized to other VQA architectures and consistently improve the results
over both the VQA and VQG tasks.
A Thorough Formalization of Conceptual Spaces
Authors: Lucas Bechberger, Kai-Uwe Kühnberger
Date: 2017-06-20 00:00:00

The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
high-dimensional space and concepts are represented by convex regions in this
space. After pointing out a problem with the convexity requirement, we propose
a formalization of conceptual spaces based on fuzzy star-shaped sets. Our
formalization uses a parametric definition of concepts and extends the original
framework by adding means to represent correlations between different domains
in a geometric way. Moreover, we define computationally efficient operations on
concepts (intersection, union, and projection onto a subspace) and show that
these operations can support both learning and reasoning processes.
Lazy stochastic principal component analysis
Authors: Michael Wojnowicz, Dinh Nguyen, Li Li, Xuan Zhao
Date: 2017-09-21 00:00:00
Tasks: Dimensionality Reduction

Stochastic principal component analysis (SPCA) has become a popular
dimensionality reduction strategy for large, high-dimensional datasets. We
derive a simplified algorithm, called Lazy SPCA, which has reduced
computational complexity and is better suited for large-scale distributed
computation. We prove that SPCA and Lazy SPCA find the same approximations to
the principal subspace, and that the pairwise distances between samples in the
lower-dimensional space is invariant to whether SPCA is executed lazily or not.
Empirical studies find downstream predictive performance to be identical for
both methods, and superior to random projections, across a range of predictive
models (linear regression, logistic lasso, and random forests). In our largest
experiment with 4.6 million samples, Lazy SPCA reduced 43.7 hours of
computation to 9.9 hours. Overall, Lazy SPCA relies exclusively on matrix
multiplications, besides an operation on a small square matrix whose size
depends only on the target dimensionality.
Semi-Automated Nasal PAP Mask Sizing using Facial Photographs
Authors: Benjamin Johnston, Alistair McEwan, Philip de Chazal
Date: 2017-09-21 00:00:00

We present a semi-automated system for sizing nasal Positive Airway Pressure
(PAP) masks based upon a neural network model that was trained with facial
photographs of both PAP mask users and non-users. It demonstrated an accuracy
of 72% in correctly sizing a mask and 96% accuracy sizing to within 1 mask size
group. The semi-automated system performed comparably to sizing from manual
measurements taken from the same images which produced 89% and 100% accuracy
respectively.
Feature Engineering for Predictive Modeling using Reinforcement Learning
Authors: Udayan Khurana, Horst Samulowitz, Deepak Turaga
Date: 2017-09-21 00:00:00
Tasks: Automated Feature Engineering, Efficient Exploration, Feature Engineering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Feature engineering is a crucial step in the process of predictive modeling.
It involves the transformation of given feature space, typically using
mathematical functions, with the objective of reducing the modeling error for a
given target. However, there is no well-defined basis for performing effective
feature engineering. It involves domain knowledge, intuition, and most of all,
a lengthy process of trial and error. The human attention involved in
overseeing this process significantly influences the cost of model generation.
We present a new framework to automate feature engineering. It is based on
performance driven exploration of a transformation graph, which systematically
and compactly enumerates the space of given options. A highly efficient
exploration strategy is derived through reinforcement learning on past
examples.
Deep Recurrent NMF for Speech Separation by Unfolding Iterative Thresholding
Authors: Scott Wisdom, Thomas Powers, James Pitton, Les Atlas
Date: 2017-09-21 00:00:00
Tasks: Speech Separation

In this paper, we propose a novel recurrent neural network architecture for
speech separation. This architecture is constructed by unfolding the iterations
of a sequential iterative soft-thresholding algorithm (ISTA) that solves the
optimization problem for sparse nonnegative matrix factorization (NMF) of
spectrograms. We name this network architecture deep recurrent NMF (DR-NMF).
The proposed DR-NMF network has three distinct advantages. First, DR-NMF
provides better interpretability than other deep architectures, since the
weights correspond to NMF model parameters, even after training. This
interpretability also provides principled initializations that enable faster
training and convergence to better solutions compared to conventional random
initialization. Second, like many deep networks, DR-NMF is an order of
magnitude faster at test time than NMF, since computation of the network output
only requires evaluating a few layers at each time step. Third, when a limited
amount of training data is available, DR-NMF exhibits stronger generalization
and separation performance compared to sparse NMF and state-of-the-art
long-short term memory (LSTM) networks. When a large amount of training data is
available, DR-NMF achieves lower yet competitive separation performance
compared to LSTM networks.