pwc-archive/papers-with-abstracts huggingface dataset:
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper
Authors: Luigi Nardi, Bruno Bodin, Sajad Saeedi, Emanuele Vespa, Andrew J. Davison, Paul H. J. Kelly
Date: 2017-02-02 00:00:00
Tasks: Active Learning, GPU, Scene Understanding

In this paper we investigate an emerging application, 3D scene understanding,
likely to be significant in the mobile space in the near future. The goal of
this exploration is to reduce execution time while meeting our quality of
result objectives. In previous work we showed for the first time that it is
possible to map this application to power constrained embedded systems,
highlighting that decision choices made at the algorithmic design-level have
the most impact.
  As the algorithmic design space is too large to be exhaustively evaluated, we
use a previously introduced multi-objective Random Forest Active Learning
prediction framework dubbed HyperMapper, to find good algorithmic designs. We
show that HyperMapper generalizes on a recent cutting edge 3D scene
understanding algorithm and on a modern GPU-based computer architecture.
HyperMapper is able to beat an expert human hand-tuning the algorithmic
parameters of the class of Computer Vision applications taken under
consideration in this paper automatically. In addition, we use crowd-sourcing
using a 3D scene understanding Android app to show that the Pareto front
obtained on an embedded system can be used to accelerate the same application
on all the 83 smart-phones and tablets crowd-sourced with speedups ranging from
2 to over 12.
Learning to Diagnose with LSTM Recurrent Neural Networks
Authors: Zachary C. Lipton, David C. Kale, Charles Elkan, Randall Wetzel
Date: 2015-11-11 00:00:00
Tasks: Time Series, Time Series Analysis

Clinical medical data, especially in the intensive care unit (ICU), consist
of multivariate time series of observations. For each patient visit (or
episode), sensor data and lab test results are recorded in the patient's
Electronic Health Record (EHR). While potentially containing a wealth of
insights, the data is difficult to mine effectively, owing to varying length,
irregular sampling and missing data. Recurrent Neural Networks (RNNs),
particularly those using Long Short-Term Memory (LSTM) hidden units, are
powerful and increasingly popular models for learning from sequence data. They
effectively model varying length sequences and capture long range dependencies.
We present the first study to empirically evaluate the ability of LSTMs to
recognize patterns in multivariate time series of clinical measurements.
Specifically, we consider multilabel classification of diagnoses, training a
model to classify 128 diagnoses given 13 frequently but irregularly sampled
clinical measurements. First, we establish the effectiveness of a simple LSTM
network for modeling clinical data. Then we demonstrate a straightforward and
effective training strategy in which we replicate targets at each sequence
step. Trained only on raw time series, our models outperform several strong
baselines, including a multilayer perceptron trained on hand-engineered
features.
Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks
Authors: Zachary C. Lipton, David C. Kale, Randall C. Wetzel
Date: 2015-10-26 00:00:00
Tasks: General Classification, Time Series, Time Series Analysis

We present a novel application of LSTM recurrent neural networks to
multilabel classification of diagnoses given variable-length time series of
clinical measurements. Our method outperforms a strong baseline on a variety of
metrics.
IOD-CNN: Integrating Object Detection Networks for Event Recognition
Authors: Sungmin Eum, Hyungtae Lee, Heesung Kwon, David Doermann
Date: 2017-03-21 00:00:00
Tasks: Object, object-detection, Object Detection

Many previous methods have showed the importance of considering semantically
relevant objects for performing event recognition, yet none of the methods have
exploited the power of deep convolutional neural networks to directly integrate
relevant object information into a unified network. We present a novel unified
deep CNN architecture which integrates architecturally different, yet
semantically-related object detection networks to enhance the performance of
the event recognition task. Our architecture allows the sharing of the
convolutional layers and a fully connected layer which effectively integrates
event recognition, rigid object detection and non-rigid object detection.
Massive Exploration of Neural Machine Translation Architectures
Authors: Denny Britz, Anna Goldie, Minh-Thang Luong, Quoc Le
Date: 2017-03-11 00:00:00
Proceeding: EMNLP 2017 9
Tasks: GPU, Machine Translation, NMT, Translation

Neural Machine Translation (NMT) has shown remarkable progress over the past
few years with production systems now being deployed to end-users. One major
drawback of current architectures is that they are expensive to train,
typically requiring days to weeks of GPU time to converge. This makes
exhaustive hyperparameter search, as is commonly done with other neural network
architectures, prohibitively expensive. In this work, we present the first
large-scale analysis of NMT architecture hyperparameters. We report empirical
results and variance numbers for several hundred experimental runs,
corresponding to over 250,000 GPU hours on the standard WMT English to German
translation task. Our experiments lead to novel insights and practical advice
for building and extending NMT architectures. As part of this contribution, we
release an open-source NMT framework that enables researchers to easily
experiment with novel techniques and reproduce state of the art results.
Simple Online and Realtime Tracking with a Deep Association Metric
Authors: Nicolai Wojke, Alex Bewley, Dietrich Paulus
Date: 2017-03-21 00:00:00
Tasks: 3D Multi-Object Tracking, Large-Scale Person Re-Identification, Multiple Object Tracking, Object Tracking, Person Re-Identification, Video Instance Segmentation

Simple Online and Realtime Tracking (SORT) is a pragmatic approach to
multiple object tracking with a focus on simple, effective algorithms. In this
paper, we integrate appearance information to improve the performance of SORT.
Due to this extension we are able to track objects through longer periods of
occlusions, effectively reducing the number of identity switches. In spirit of
the original framework we place much of the computational complexity into an
offline pre-training stage where we learn a deep association metric on a
large-scale person re-identification dataset. During online application, we
establish measurement-to-track associations using nearest neighbor queries in
visual appearance space. Experimental evaluation shows that our extensions
reduce the number of identity switches by 45%, achieving overall competitive
performance at high frame rates.
Deep Learning for Explicitly Modeling Optimization Landscapes
Authors: Shumeet Baluja
Date: 2017-03-21 00:00:00
Tasks: Deep Learning, Scheduling

In all but the most trivial optimization problems, the structure of the
solutions exhibit complex interdependencies between the input parameters.
Decades of research with stochastic search techniques has shown the benefit of
explicitly modeling the interactions between sets of parameters and the overall
quality of the solutions discovered. We demonstrate a novel method, based on
learning deep networks, to model the global landscapes of optimization
problems. To represent the search space concisely and accurately, the deep
networks must encode information about the underlying parameter interactions
and their contributions to the quality of the solution. Once the networks are
trained, the networks are probed to reveal parameter combinations with high
expected performance with respect to the optimization task. These estimates are
used to initialize fast, randomized, local search algorithms, which in turn
expose more information about the search space that is subsequently used to
refine the models. We demonstrate the technique on multiple optimization
problems that have arisen in a variety of real-world domains, including:
packing, graphics, job scheduling, layout and compression. The problems include
combinatoric search spaces, discontinuous and highly non-linear spaces, and
span binary, higher-cardinality discrete, as well as continuous parameters.
Strengths, limitations, and extensions of the approach are extensively
discussed and demonstrated.
Ontology Based Pivoted normalization using Vector Based Approach for information Retrieval
Authors: Vishal Jain, Dr. Mayank Singh
Date: 2017-03-21 00:00:00
Tasks: Information Retrieval, Retrieval

The proposed methodology is procedural i.e. it follows finite number of steps
that extracts relevant documents according to users query. It is based on
principles of Data Mining for analyzing web data. Data Mining first adapts
integration of data to generate warehouse. Then, it extracts useful information
with the help of algorithm. The task of representing extracted documents is
done by using Vector Based Statistical Approach that represents each document
in set of Terms.
Using Word Embeddings in Twitter Election Classification
Authors: Xiao Yang, Craig Macdonald, Iadh Ounis
Date: 2016-06-22 00:00:00
Tasks: Articles, Classification, General Classification, Sentiment Analysis, Sentiment Classification, Word Embeddings

Word embeddings and convolutional neural networks (CNN) have attracted
extensive attention in various classification tasks for Twitter, e.g. sentiment
classification. However, the effect of the configuration used to train and
generate the word embeddings on the classification performance has not been
studied in the existing literature. In this paper, using a Twitter election
classification task that aims to detect election-related tweets, we investigate
the impact of the background dataset used to train the embedding models, the
context window size and the dimensionality of word embeddings on the
classification performance. By comparing the classification results of two word
embedding models, which are trained using different background corpora (e.g.
Wikipedia articles and Twitter microposts), we show that the background data
type should align with the Twitter classification dataset to achieve a better
performance. Moreover, by evaluating the results of word embeddings models
trained using various context window sizes and dimensionalities, we found that
large context window and dimension sizes are preferable to improve the
performance. Our experimental results also show that using word embeddings and
CNN leads to statistically significant improvements over various baselines such
as random, SVM with TF-IDF and SVM with word embeddings.
Improving Statistical Multimedia Information Retrieval Model by using Ontology
Authors: Gagandeep Singh Narula, Vishal Jain
Date: 2017-03-21 00:00:00
Tasks: Information Retrieval, Retrieval

A typical IR system that delivers and stores information is affected by
problem of matching between user query and available content on web. Use of
Ontology represents the extracted terms in form of network graph consisting of
nodes, edges, index terms etc. The above mentioned IR approaches provide
relevance thus satisfying users query. The paper also emphasis on analyzing
multimedia documents and performs calculation for extracted terms using
different statistical formulas. The proposed model developed reduces semantic
gap and satisfies user needs efficiently.
An Army of Me: Sockpuppets in Online Discussion Communities
Authors: Srijan Kumar, Justin Cheng, Jure Leskovec, V. S. Subrahmanian
Date: 2017-03-21 00:00:00

In online discussion communities, users can interact and share information
and opinions on a wide variety of topics. However, some users may create
multiple identities, or sockpuppets, and engage in undesired behavior by
deceiving others or manipulating discussions. In this work, we study
sockpuppetry across nine discussion communities, and show that sockpuppets
differ from ordinary users in terms of their posting behavior, linguistic
traits, as well as social network structure. Sockpuppets tend to start fewer
discussions, write shorter posts, use more personal pronouns such as "I", and
have more clustered ego-networks. Further, pairs of sockpuppets controlled by
the same individual are more likely to interact on the same discussion at the
same time than pairs of ordinary users. Our analysis suggests a taxonomy of
deceptive behavior in discussion communities. Pairs of sockpuppets can vary in
their deceptiveness, i.e., whether they pretend to be different users, or their
supportiveness, i.e., if they support arguments of other sockpuppets controlled
by the same user. We apply these findings to a series of prediction tasks,
notably, to identify whether a pair of accounts belongs to the same underlying
user or not. Altogether, this work presents a data-driven view of deception in
online discussion communities and paves the way towards the automatic detection
of sockpuppets.
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments
Authors: Shichao Yang, Yu Song, Michael Kaess, Sebastian Scherer
Date: 2017-03-21 00:00:00
Tasks: Motion Planning, Scene Understanding, Simultaneous Localization and Mapping, State Estimation

Existing simultaneous localization and mapping (SLAM) algorithms are not
robust in challenging low-texture environments because there are only few
salient features. The resulting sparse or semi-dense map also conveys little
information for motion planning. Though some work utilize plane or scene layout
for dense map regularization, they require decent state estimation from other
sources. In this paper, we propose real-time monocular plane SLAM to
demonstrate that scene understanding could improve both state estimation and
dense mapping especially in low-texture environments. The plane measurements
come from a pop-up 3D plane model applied to each single image. We also combine
planes with point based SLAM to improve robustness. On a public TUM dataset,
our algorithm generates a dense semantic 3D model with pixel depth error of 6.2
cm while existing SLAM algorithms fail. On a 60 m long dataset with loops, our
method creates a much better 3D model with state estimation error of 0.67%.
A Holistic Approach for Optimizing DSP Block Utilization of a CNN implementation on FPGA
Authors: Kamel Abdelouahab, Cedric Bourrasset, Maxime Pelcat, François Berry, Jean-Charles Quinton, Jocelyn Serot
Date: 2017-03-21 00:00:00
Tasks: Optical Character Recognition (OCR)

Deep Neural Networks are becoming the de-facto standard models for image
understanding, and more generally for computer vision tasks. As they involve
highly parallelizable computations, CNN are well suited to current fine grain
programmable logic devices. Thus, multiple CNN accelerators have been
successfully implemented on FPGAs. Unfortunately, FPGA resources such as logic
elements or DSP units remain limited. This work presents a holistic method
relying on approximate computing and design space exploration to optimize the
DSP block utilization of a CNN implementation on an FPGA. This method was
tested when implementing a reconfigurable OCR convolutional neural network on
an Altera Stratix V device and varying both data representation and CNN
topology in order to find the best combination in terms of DSP block
utilization and classification accuracy. This exploration generated dataflow
architectures of 76 CNN topologies with 5 different fixed point representation.
Most efficient implementation performs 883 classifications/sec at 256 x 256
resolution using 8% of the available DSP blocks.
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
Authors: Abhishek Das, Satwik Kottur, José M. F. Moura, Stefan Lee, Dhruv Batra
Date: 2017-03-20 00:00:00
Proceeding: ICCV 2017 10
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Visual Dialog, Visual Question Answering, Visual Question Answering (VQA)

We introduce the first goal-driven training for visual question answering and
dialog agents. Specifically, we pose a cooperative 'image guessing' game
between two agents -- Qbot and Abot -- who communicate in natural language
dialog so that Qbot can select an unseen image from a lineup of images. We use
deep reinforcement learning (RL) to learn the policies of these agents
end-to-end -- from pixels to multi-agent multi-round dialog to game reward.
  We demonstrate two experimental results.
  First, as a 'sanity check' demonstration of pure RL (from scratch), we show
results on a synthetic world, where the agents communicate in ungrounded
vocabulary, i.e., symbols with no pre-specified meanings (X, Y, Z). We find
that two bots invent their own communication protocol and start using certain
symbols to ask/answer about certain visual attributes (shape/color/style).
Thus, we demonstrate the emergence of grounded language and communication among
'visual' dialog agents with no human supervision.
  Second, we conduct large-scale real-image experiments on the VisDial dataset,
where we pretrain with supervised dialog data and show that the RL 'fine-tuned'
agents significantly outperform SL agents. Interestingly, the RL Qbot learns to
ask questions that Abot is good at, ultimately resulting in more informative
dialog and a better team.
A theory of interpretive clustering in free recall
Authors: Francesco Fumarola
Date: 2016-11-27 00:00:00
Tasks: Clustering, Position

A stochastic model of short-term verbal memory is proposed, in which the
psychological state of the subject is encoded as the instantaneous position of
a particle diffusing over a semantic graph with a probabilistic structure. The
model is particularly suitable for studying the dependence of free-recall
observables on semantic properties of the words to be recalled. Besides
predicting some well-known experimental features (contiguity effect, forward
asymmetry, word-length effect), a novel prediction is obtained on the
relationship between the contiguity effect and the syllabic length of words;
shorter words, by way of their wider semantic range, are predicted to be
characterized by stronger forward contiguity. A fresh analysis of archival data
allows to confirm this prediction.
Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration
Authors: Marco Grzegorczyk, Andrej Aderhold, Dirk Husmeier
Date: 2017-03-21 00:00:00
Tasks: Numerical Integration

Thermodynamic integration (TI) for computing marginal likelihoods is based on
an inverse annealing path from the prior to the posterior distribution. In many
cases, the resulting estimator suffers from high variability, which
particularly stems from the prior regime. When comparing complex models with
differences in a comparatively small number of parameters, intrinsic errors
from sampling fluctuations may outweigh the differences in the log marginal
likelihood estimates. In the present article, we propose a thermodynamic
integration scheme that directly targets the log Bayes factor. The method is
based on a modified annealing path between the posterior distributions of the
two models compared, which systematically avoids the high variance prior
regime. We combine this scheme with the concept of non-equilibrium TI to
minimise discretisation errors from numerical integration. Results obtained on
Bayesian regression models applied to standard benchmark data, and a complex
hierarchical model applied to biopathway inference, demonstrate a significant
reduction in estimator variance over state-of-the-art TI methods.
A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines
Authors: Michael R. Smith, Aaron J. Hill, Kristofor D. Carlson, Craig M. Vineyard, Jonathon Donaldson, David R. Follett, Pamela L. Follett, John H. Naegle, Conrad D. James, James B. Aimone
Date: 2017-03-21 00:00:00
Tasks: speech-recognition, Speech Recognition

Information in neural networks is represented as weighted connections, or
synapses, between neurons. This poses a problem as the primary computational
bottleneck for neural networks is the vector-matrix multiply when inputs are
multiplied by the neural network weights. Conventional processing architectures
are not well suited for simulating neural networks, often requiring large
amounts of energy and time. Additionally, synapses in biological neural
networks are not binary connections, but exhibit a nonlinear response function
as neurotransmitters are emitted and diffuse between neurons. Inspired by
neuroscience principles, we present a digital neuromorphic architecture, the
Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex
synaptic response functions without requiring additional hardware components.
We consider the paradigm of spiking neurons with temporally coded information
as opposed to non-spiking rate coded neurons used in most neural networks. In
this paradigm we examine liquid state machines applied to speech recognition
and show how a liquid state machine with temporal dynamics maps onto the
STPU-demonstrating the flexibility and efficiency of the STPU for instantiating
neural algorithms.
An Accelerated Analog Neuromorphic Hardware System Emulating NMDA- and Calcium-Based Non-Linear Dendrites
Authors: Johannes Schemmel, Laura Kriener, Paul Müller, Karlheinz Meier
Date: 2017-03-21 00:00:00

This paper presents an extension of the BrainScaleS accelerated analog
neuromorphic hardware model. The scalable neuromorphic architecture is extended
by the support for multi-compartment models and non-linear dendrites. These
features are part of a \SI{65}{\nano\meter} prototype ASIC. It allows to
emulate different spike types observed in cortical pyramidal neurons: NMDA
plateau potentials, calcium and sodium spikes. By replicating some of the
structures of these cells, they can be configured to perform coincidence
detection within a single neuron. Built-in plasticity mechanisms can modify not
only the synaptic weights, but also the dendritic synaptic composition to
efficiently train large multi-compartment neurons. Transistor-level simulations
demonstrate the functionality of the analog implementation and illustrate
analogies to biological measurements.
Developpement de Methodes Automatiques pour la Reutilisation des Composants Logiciels
Authors: Kouakou Ive Arsene Koffi, Konan Marcellin Brou, Souleymane Oumtanaga
Date: 2017-03-21 00:00:00

The large amount of information and the increasing complexity of applications
constrain developers to have stand-alone and reusable components from libraries
and component markets.Our approach consists in developing methods to evaluate
the quality of the software component of these libraries, on the one hand and
moreover to optimize the financial cost and the adaptation's time of these
selected components. Our objective function defines a metric that maximizes the
value of the software component quality by minimizing the financial cost and
maintenance time. This model should make it possible to classify the components
and order them in order to choose the most optimized.
  MOTS-CLES : d{\'e}veloppement de m{\'e}thode, r{\'e}utilisation, composants
logiciels, qualit{\'e} de composant
  KEYWORDS:method development, reuse, software components, component quality .
Exact Structure Learning of Bayesian Networks by Optimal Path Extension
Authors: Subhadeep Karan, Jaroslaw Zola
Date: 2016-08-09 00:00:00

Bayesian networks are probabilistic graphical models often used in big data
analytics. The problem of exact structure learning is to find a network
structure that is optimal under certain scoring criteria. The problem is known
to be NP-hard and the existing methods are both computationally and memory
intensive. In this paper, we introduce a new approach for exact structure
learning. Our strategy is to leverage relationship between a partial network
structure and the remaining variables to constraint the number of ways in which
the partial network can be optimally extended. Via experimental results, we
show that the method provides up to three times improvement in runtime, and
orders of magnitude reduction in memory consumption over the current best
algorithms.