pwc-archive/papers-with-abstracts huggingface dataset:
Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods
Authors: Hugues Thomas, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, Yann Le Gall
Date: 2018-08-01 00:00:00
Tasks: Classification, General Classification, Semantic Segmentation

This paper introduces a new definition of multiscale neighborhoods in 3D
point clouds. This definition, based on spherical neighborhoods and
proportional subsampling, allows the computation of features with a consistent
geometrical meaning, which is not the case when using k-nearest neighbors. With
an appropriate learning strategy, the proposed features can be used in a random
forest to classify 3D points. In this semantic classification task, we show
that our multiscale features outperform state-of-the-art features using the
same experimental conditions. Furthermore, their classification power competes
with more elaborate classification approaches including Deep Learning methods.
Low-Latency Neural Speech Translation
Authors: Jan Niehues, Ngoc-Quan Pham, Thanh-Le Ha, Matthias Sperber, Alex Waibel
Date: 2018-08-01 00:00:00
Tasks: Machine Translation, Multi-Task Learning, NMT, Sentence, Translation

Through the development of neural machine translation, the quality of machine
translation systems has been improved significantly. By exploiting advancements
in deep learning, systems are now able to better approximate the complex
mapping from source sentences to target sentences. But with this ability, new
challenges also arise. An example is the translation of partial sentences in
low-latency speech translation. Since the model has only seen complete
sentences in training, it will always try to generate a complete sentence,
though the input may only be a partial sentence. We show that NMT systems can
be adapted to scenarios where no task-specific training data is available.
Furthermore, this is possible without losing performance on the original
training data. We achieve this by creating artificial data and by using
multi-task learning. After adaptation, we are able to reduce the number of
corrections displayed during incremental output construction by 45%, without a
decrease in translation quality.
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks
Authors: Yasar Sinan Nasir, Dongning Guo
Date: 2018-08-01 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

This work demonstrates the potential of deep reinforcement learning
techniques for transmit power control in wireless networks. Existing techniques
typically find near-optimal power allocations by solving a challenging
optimization problem. Most of these algorithms are not scalable to large
networks in real-world scenarios because of their computational complexity and
instantaneous cross-cell channel state information (CSI) requirement. In this
paper, a distributively executed dynamic power allocation scheme is developed
based on model-free deep reinforcement learning. Each transmitter collects CSI
and quality of service (QoS) information from several neighbors and adapts its
own transmit power accordingly. The objective is to maximize a weighted
sum-rate utility function, which can be particularized to achieve maximum
sum-rate or proportionally fair scheduling. Both random variations and delays
in the CSI are inherently addressed using deep Q-learning. For a typical
network architecture, the proposed algorithm is shown to achieve near-optimal
power allocation in real time based on delayed CSI measurements available to
the agents. The proposed scheme is especially suitable for practical scenarios
where the system model is inaccurate and CSI delay is non-negligible.
A Multi-channel Network with Image Retrieval for Accurate Brain Tissue Segmentation
Authors: Yao Sun, Yang Deng, Yue Xu, Shuo Zhang, Mingwang Zhu, Kehong Yuan
Date: 2018-08-01 00:00:00
Tasks: Image Retrieval, Retrieval, Segmentation

Magnetic Resonance Imaging (MRI) is widely used in the pathological and
functional studies of the brain, such as epilepsy, tumor diagnosis, etc.
Automated accurate brain tissue segmentation like cerebro-spinal fluid (CSF),
gray matter (GM), white matter (WM) is the basis of these studies and many
researchers are seeking it to the best. Based on the truth that multi-channel
segmentation network with its own ground truth achieves up to average dice
ratio 0.98, we propose a novel method that we add a fourth channel with the
ground truth of the most similar image's obtained by CBIR from the database.
The results show that the method improves the segmentation performance, as
measured by average dice ratio, by approximately 0.01 in the MRBrainS18
database. In addition, our method is concise and robust, which can be used to
any network architecture that needs not be modified a lot.
From Thumbnails to Summaries - A single Deep Neural Network to Rule Them All
Authors: Hongxiang Gu, Viswanathan Swaminathan
Date: 2018-08-01 00:00:00
Tasks: All, Decoder, Management

Video summaries come in many forms, from traditional single-image thumbnails,
animated thumbnails, storyboards, to trailer-like video summaries. Content
creators use the summaries to display the most attractive portion of their
videos; the users use them to quickly evaluate if a video is worth watching.
All forms of summaries are essential to video viewers, content creators, and
advertisers. Often video content management systems have to generate multiple
versions of summaries that vary in duration and presentational forms. We
present a framework ReconstSum that utilizes LSTM-based autoencoder
architecture to extract and select a sparse subset of video frames or keyshots
that optimally represent the input video in an unsupervised manner. The encoder
selects a subset from the input video while the decoder seeks to reconstruct
the video from the selection. The goal is to minimize the difference between
the original input video and the reconstructed video. Our method is easily
extendable to generate a variety of applications including static video
thumbnails, animated thumbnails, storyboards and "trailer-like" highlights. We
specifically study and evaluate two most popular use cases: thumbnail
generation and storyboard generation. We demonstrate that our methods generate
better results than the state-of-the-art techniques in both use cases.
Towards fully automated protein structure elucidation with NMR spectroscopy
Authors: Piotr Klukowski, Adam Gonczarek
Date: 2018-07-31 00:00:00
Tasks: Combinatorial Optimization

Nuclear magnetic resonance (NMR) spectroscopy is one of the leading
techniques for protein studies. The method features a number of properties,
allowing to explain macromolecular interactions mechanistically and resolve
structures with atomic resolution. However, due to laborious data analysis, a
full potential of NMR spectroscopy remains unexploited. Here we present an
approach aiming at automation of two major bottlenecks in the analysis
pipeline, namely, peak picking and chemical shift assignment. Our approach
combines deep learning, non-parametric models and combinatorial optimization,
and is able to detect signals of interest in a multidimensional NMR data with
high accuracy and match them with atoms in medium-length protein sequences,
which is a preliminary step to solve protein spatial structure.
The impact of imbalanced training data on machine learning for author name disambiguation
Authors: Jinseok Kim, Jenna Kim
Date: 2018-07-30 00:00:00
Tasks: BIG-bench Machine Learning, Computational Efficiency, regression

In supervised machine learning for author name disambiguation, negative
training data are often dominantly larger than positive training data. This
paper examines how the ratios of negative to positive training data can affect
the performance of machine learning algorithms to disambiguate author names in
bibliographic records. On multiple labeled datasets, three classifiers -
Logistic Regression, Na\"ive Bayes, and Random Forest - are trained through
representative features such as coauthor names, and title words extracted from
the same training data but with various positive-negative training data ratios.
Results show that increasing negative training data can improve disambiguation
performance but with a few percent of performance gains and sometimes degrade
it. Logistic Regression and Na\"ive Bayes learn optimal disambiguation models
even with a base ratio (1:1) of positive and negative training data. Also, the
performance improvement by Random Forest tends to quickly saturate roughly
after 1:10 ~ 1:15. These findings imply that contrary to the common practice
using all training data, name disambiguation algorithms can be trained using
part of negative training data without degrading much disambiguation
performance while increasing computational efficiency. This study calls for
more attention from author name disambiguation scholars to methods for machine
learning from imbalanced data.
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
Authors: Christos Sakaridis, Dengxin Dai, Simon Hecker, Luc van Gool
Date: 2018-08-03 00:00:00
Proceeding: ECCV 2018 9
Tasks: Scene Understanding, Semantic Segmentation

This work addresses the problem of semantic scene understanding under dense
fog. Although considerable progress has been made in semantic scene
understanding, it is mainly related to clear-weather scenes. Extending
recognition methods to adverse weather conditions such as fog is crucial for
outdoor applications. In this paper, we propose a novel method, named
Curriculum Model Adaptation (CMAda), which gradually adapts a semantic
segmentation model from light synthetic fog to dense real fog in multiple
steps, using both synthetic and real foggy data. In addition, we present three
other main stand-alone contributions: 1) a novel method to add synthetic fog to
real, clear-weather scenes using semantic input; 2) a new fog density
estimator; 3) the Foggy Zurich dataset comprising $3808$ real foggy images,
with pixel-level semantic annotations for $16$ images with dense fog. Our
experiments show that 1) our fog simulation slightly outperforms a
state-of-the-art competing simulation with respect to the task of semantic
foggy scene understanding (SFSU); 2) CMAda improves the performance of
state-of-the-art models for SFSU significantly by leveraging unlabeled real
foggy data. The datasets and code are publicly available.
The Text-Based Adventure AI Competition
Authors: Timothy Atkinson, Hendrik Baier, Tara Copplestone, Sam Devlin, Jerry Swan
Date: 2018-08-03 00:00:00
Tasks: Board Games, Natural Language Understanding

In 2016, 2017, and 2018 at the IEEE Conference on Computational Intelligence
in Games, the authors of this paper ran a competition for agents that can play
classic text-based adventure games. This competition fills a gap in existing
game AI competitions that have typically focussed on traditional card/board
games or modern video games with graphical interfaces. By providing a platform
for evaluating agents in text-based adventures, the competition provides a
novel benchmark for game AI with unique challenges for natural language
understanding and generation. This paper summarises the three competitions ran
in 2016, 2017, and 2018 (including details of open source implementations of
both the competition framework and our competitors) and presents the results of
an improved evaluation of these competitors across 20 games.
CornerNet: Detecting Objects as Paired Keypoints
Authors: Hei Law, Jia Deng
Date: 2018-08-03 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, object-detection, Object Detection

We propose CornerNet, a new approach to object detection where we detect an
object bounding box as a pair of keypoints, the top-left corner and the
bottom-right corner, using a single convolution neural network. By detecting
objects as paired keypoints, we eliminate the need for designing a set of
anchor boxes commonly used in prior single-stage detectors. In addition to our
novel formulation, we introduce corner pooling, a new type of pooling layer
that helps the network better localize corners. Experiments show that CornerNet
achieves a 42.2% AP on MS COCO, outperforming all existing one-stage detectors.
A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity Prediction
Authors: Md. Shad Akhtar, Deepanway Ghosal, Asif Ekbal, Pushpak Bhattacharyya, Sadao Kurohashi
Date: 2018-08-03 00:00:00
Tasks: Emotion Classification, General Classification, Sentiment Analysis

In this paper, through multi-task ensemble framework we address three
problems of emotion and sentiment analysis i.e. "emotion classification &
intensity", "valence, arousal & dominance for emotion" and "valence & arousal}
for sentiment". The underlying problems cover two granularities (i.e.
coarse-grained and fine-grained) and a diverse range of domains (i.e. tweets,
Facebook posts, news headlines, blogs, letters etc.). The ensemble model aims
to leverage the learned representations of three deep learning models (i.e.
CNN, LSTM and GRU) and a hand-crafted feature representation for the
predictions. Experimental results on the benchmark datasets show the efficacy
of our proposed multi-task ensemble frameworks. We obtain the performance
improvement of 2-3 points on an average over single-task systems for most of
the problems and domains.
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data
Authors: Yuanzhi Li, YIngyu Liang
Date: 2018-08-03 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: General Classification, Multi-class Classification

Neural networks have many successful applications, while much less theoretical understanding has been gained. Towards bridging this gap, we study the problem of learning a two-layer overparameterized ReLU neural network for multi-class classification via stochastic gradient descent (SGD) from random initialization. In the overparameterized setting, when the data comes from mixtures of well-separated distributions, we prove that SGD learns a network with a small generalization error, albeit the network has enough capacity to fit arbitrary labels. Furthermore, the analysis provides interesting insights into several aspects of learning neural networks and can be verified based on empirical studies on synthetic data and on the MNIST dataset.
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation
Authors: Tanya Nair, Doina Precup, Douglas L. Arnold, Tal Arbel
Date: 2018-08-03 00:00:00
Tasks: Lesion Detection, Lesion Segmentation, Prognosis, Segmentation

Deep learning (DL) networks have recently been shown to outperform other
segmentation methods on various public, medical-image challenge datasets
[3,11,16], especially for large pathologies. However, in the context of
diseases such as Multiple Sclerosis (MS), monitoring all the focal lesions
visible on MRI sequences, even very small ones, is essential for disease
staging, prognosis, and evaluating treatment efficacy. Moreover, producing
deterministic outputs hinders DL adoption into clinical routines. Uncertainty
estimates for the predictions would permit subsequent revision by clinicians.
We present the first exploration of multiple uncertainty estimates based on
Monte Carlo (MC) dropout [4] in the context of deep networks for lesion
detection and segmentation in medical images. Specifically, we develop a 3D MS
lesion segmentation CNN, augmented to provide four different voxel-based
uncertainty measures based on MC dropout. We train the network on a
proprietary, large-scale, multi-site, multi-scanner, clinical MS dataset, and
compute lesion-wise uncertainties by accumulating evidence from voxel-wise
uncertainties within detected lesions. We analyze the performance of
voxel-based segmentation and lesion-level detection by choosing operating
points based on the uncertainty. Empirical evidence suggests that uncertainty
measures consistently allow us to choose superior operating points compared
only using the network's sigmoid output as a probability.
Structured Neural Network Dynamics for Model-based Control
Authors: Alexander Broad, Ian Abraham, Todd Murphey, Brenna Argall
Date: 2018-08-03 00:00:00
Tasks: continuous-control, Continuous Control, model, Model Predictive Control

We present a structured neural network architecture that is inspired by
linear time-varying dynamical systems. The network is designed to mimic the
properties of linear dynamical systems which makes analysis and control simple.
The architecture facilitates the integration of learned system models with
gradient-based model predictive control algorithms, and removes the requirement
of computing potentially costly derivatives online. We demonstrate the efficacy
of this modeling technique in computing autonomous control policies through
evaluation in a variety of standard continuous control domains.
Robust Spectral Filtering and Anomaly Detection
Authors: Jakub Marecek, Tigran Tchrakian
Date: 2018-08-03 00:00:00
Tasks: Anomaly Detection

We consider a setting, where the output of a linear dynamical system (LDS)
is, with an unknown but fixed probability, replaced by noise. There, we present
a robust method for the prediction of the outputs of the LDS and identification
of the samples of noise, and prove guarantees on its statistical performance.
One application lies in anomaly detection: the samples of noise, unlikely to
have been generated by the dynamics, can be flagged to operators of the system
for further study.
Content-driven, unsupervised clustering of news articles through multiscale graph partitioning
Authors: M. Tarik Altuncu, Sophia N. Yaliraki, Mauricio Barahona
Date: 2018-08-03 00:00:00
Tasks: Articles, Clustering, Community Detection, graph partitioning

The explosion in the amount of news and journalistic content being generated
across the globe, coupled with extended and instantaneous access to information
through online media, makes it difficult and time-consuming to monitor news
developments and opinion formation in real time. There is an increasing need
for tools that can pre-process, analyse and classify raw text to extract
interpretable content; specifically, identifying topics and content-driven
groupings of articles. We present here such a methodology that brings together
powerful vector embeddings from Natural Language Processing with tools from
Graph Theory that exploit diffusive dynamics on graphs to reveal natural
partitions across scales. Our framework uses a recent deep neural network text
analysis methodology (Doc2vec) to represent text in vector form and then
applies a multi-scale community detection method (Markov Stability) to
partition a similarity graph of document vectors. The method allows us to
obtain clusters of documents with similar content, at different levels of
resolution, in an unsupervised manner. We showcase our approach with the
analysis of a corpus of 9,000 news articles published by Vox Media over one
year. Our results show consistent groupings of documents according to content
without a priori assumptions about the number or type of clusters to be found.
The multilevel clustering reveals a quasi-hierarchy of topics and subtopics
with increased intelligibility and improved topic coherence as compared to
external taxonomy services and standard topic detection methods.
Generalization Error in Deep Learning
Authors: Daniel Jakubovitz, Raja Giryes, Miguel R. D. Rodrigues
Date: 2018-08-03 00:00:00
Tasks: Deep Learning, speech-recognition, Speech Recognition, Translation

Deep learning models have lately shown great performance in various fields
such as computer vision, speech recognition, speech translation, and natural
language processing. However, alongside their state-of-the-art performance, it
is still generally unclear what is the source of their generalization ability.
Thus, an important question is what makes deep neural networks able to
generalize well from the training set to new data. In this article, we provide
an overview of the existing theory and bounds for the characterization of the
generalization error of deep neural networks, combining both classical and more
recent theoretical and empirical results.
Efficient Purely Convolutional Text Encoding
Authors: Szymon Malik, Adrian Lancucki, Jan Chorowski
Date: 2018-08-03 00:00:00

In this work, we focus on a lightweight convolutional architecture that
creates fixed-size vector embeddings of sentences. Such representations are
useful for building NLP systems, including conversational agents. Our work
derives from a recently proposed recursive convolutional architecture for
auto-encoding text paragraphs at byte level. We propose alternations that
significantly reduce training time, the number of parameters, and improve
auto-encoding accuracy. Finally, we evaluate the representations created by our
model on tasks from SentEval benchmark suite, and show that it can serve as a
better, yet fairly low-resource alternative to popular bag-of-words embeddings.
Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions
Authors: Konda Reddy Mopuri, Phani Krishna Uppala, R. Venkatesh Babu
Date: 2018-08-03 00:00:00
Proceeding: ECCV 2018 9

Deep learning models are susceptible to input specific noise, called
adversarial perturbations. Moreover, there exist input-agnostic noise, called
Universal Adversarial Perturbations (UAP) that can affect inference of the
models over most input samples. Given a model, there exist broadly two
approaches to craft UAPs: (i) data-driven: that require data, and (ii)
data-free: that do not require data samples. Data-driven approaches require
actual samples from the underlying data distribution and craft UAPs with high
success (fooling) rate. However, data-free approaches craft UAPs without
utilizing any data samples and therefore result in lesser success rates. In
this paper, for data-free scenarios, we propose a novel approach that emulates
the effect of data samples with class impressions in order to craft UAPs using
data-driven objectives. Class impression for a given pair of category and model
is a generic representation (in the input space) of the samples belonging to
that category. Further, we present a neural network based generative model that
utilizes the acquired class impressions to learn crafting UAPs. Experimental
evaluation demonstrates that the learned generative model, (i) readily crafts
UAPs via simple feed-forwarding through neural network layers, and (ii)
achieves state-of-the-art success rates for data-free scenario and closer to
that for data-driven setting without actually utilizing any data samples.
A Two-Dimensional (2-D) Learning Framework for Particle Swarm based Feature Selection
Authors: Faizal Hafiz, Akshya Swain, Nitish Patel, Chirag Naik
Date: 2018-08-03 00:00:00
Tasks: feature selection

This paper proposes a new generalized two dimensional learning approach for
particle swarm based feature selection. The core idea of the proposed approach
is to include the information about the subset cardinality into the learning
framework by extending the dimension of the velocity. The 2D-learning framework
retains all the key features of the original PSO, despite the extra learning
dimension. Most of the popular variants of PSO can easily be adapted into this
2D learning framework for feature selection problems. The efficacy of the
proposed learning approach has been evaluated considering several benchmark
data and two induction algorithms: Naive-Bayes and k-Nearest Neighbor. The
results of the comparative investigation including the time-complexity analysis
with GA, ACO and five other PSO variants illustrate that the proposed 2D
learning approach gives feature subset with relatively smaller cardinality and
better classification performance with shorter run times.