pwc-archive/papers-with-abstracts huggingface dataset:
Optimal spectral transportation with application to music transcription
Authors: Rémi Flamary, Cédric Févotte, Nicolas Courty, Valentin Emiya
Date: 2016-09-30 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Music Transcription

Many spectral unmixing methods rely on the non-negative decomposition of
spectral data onto a dictionary of spectral templates. In particular,
state-of-the-art music transcription systems decompose the spectrogram of the
input signal onto a dictionary of representative note spectra. The typical
measures of fit used to quantify the adequacy of the decomposition compare the
data and template entries frequency-wise. As such, small displacements of
energy from a frequency bin to another as well as variations of timber can
disproportionally harm the fit. We address these issues by means of optimal
transportation and propose a new measure of fit that treats the frequency
distributions of energy holistically as opposed to frequency-wise. Building on
the harmonic nature of sound, the new measure is invariant to shifts of energy
to harmonically-related frequencies, as well as to small and local
displacements of energy. Equipped with this new measure of fit, the dictionary
of note templates can be considerably simplified to a set of Dirac vectors
located at the target fundamental frequencies (musical pitch values). This in
turns gives ground to a very fast and simple decomposition algorithm that
achieves state-of-the-art performance on real musical data.
Learning Semantically Coherent and Reusable Kernels in Convolution Neural Nets for Sentence Classification
Authors: Madhusudan Lakshmana, Sundararajan Sellamanickam, Shirish Shevade, Keerthi Selvaraj
Date: 2016-08-01 00:00:00
Tasks: Clustering, General Classification, Sentence, Sentence Classification

The state-of-the-art CNN models give good performance on sentence
classification tasks. The purpose of this work is to empirically study
desirable properties such as semantic coherence, attention mechanism and
reusability of CNNs in these tasks. Semantically coherent kernels are
preferable as they are a lot more interpretable for explaining the decision of
the learned CNN model. We observe that the learned kernels do not have semantic
coherence. Motivated by this observation, we propose to learn kernels with
semantic coherence using clustering scheme combined with Word2Vec
representation and domain knowledge such as SentiWordNet. We suggest a
technique to visualize attention mechanism of CNNs for decision explanation
purpose. Reusable property enables kernels learned on one problem to be used in
another problem. This helps in efficient learning as only a few additional
domain specific filters may have to be learned. We demonstrate the efficacy of
our core ideas of learning semantically coherent kernels and leveraging
reusable kernels for efficient learning on several benchmark datasets.
Experimental results show the usefulness of our approach by achieving
performance close to the state-of-the-art methods but with semantic and
reusable properties.
Matching of Images with Rotation Transformation Based on the Virtual Electromagnetic Interaction
Authors: Xiaodong Zhuang, N. E. Mastorakis
Date: 2016-10-10 00:00:00
Tasks: Image Registration

A novel approach of image matching for rotating transformation is presented
and studied. The approach is inspired by electromagnetic interaction force
between physical currents. The virtual current in images is proposed based on
the significant edge lines extracted as the fundamental structural feature of
images. The virtual electromagnetic force and the corresponding moment is
studied between two images after the extraction of the virtual currents in the
images. Then image matching for rotating transformation is implemented by
exploiting the interaction between the virtual currents in the two images to be
matched. The experimental results prove the effectiveness of the novel idea,
which indicates the promising application of the proposed method in image
registration.
Training a Probabilistic Graphical Model with Resistive Switching Electronic Synapses
Authors: S. Burc Eryilmaz, Emre Neftci, Siddharth Joshi, Sang-Bum Kim, Matthew BrightSky, Hsiang-Lan Lung, Chung Lam, Gert Cauwenberghs, H. -S. Philip Wong
Date: 2016-09-27 00:00:00

Current large scale implementations of deep learning and data mining require
thousands of processors, massive amounts of off-chip memory, and consume
gigajoules of energy. Emerging memory technologies such as nanoscale
two-terminal resistive switching memory devices offer a compact, scalable and
low power alternative that permits on-chip co-located processing and memory in
fine-grain distributed parallel architecture. Here we report first use of
resistive switching memory devices for implementing and training a Restricted
Boltzmann Machine (RBM), a generative probabilistic graphical model as a key
component for unsupervised learning in deep networks. We experimentally
demonstrate a 45-synapse RBM realized with 90 resistive switching phase change
memory (PCM) elements trained with a bio-inspired variant of the Contrastive
Divergence (CD) algorithm, implementing Hebbian and anti-Hebbian weight
updates. The resistive PCM devices show a two-fold to ten-fold reduction in
error rate in a missing pixel pattern completion task trained over 30 epochs,
compared to untrained case. Measured programming energy consumption is 6.1 nJ
per epoch with the resistive switching PCM devices, a factor of ~150 times
lower than conventional processor-memory systems. We analyze and discuss the
dependence of learning performance on cycle-to-cycle variations as well as
number of gradual levels in the PCM analog memory devices.
Image Segmentation Based on the Self-Balancing Mechanism in Virtual 3D Elastic Mesh
Authors: Xiaodong Zhuang, N. E. Mastorakis, Jieru Chi, Hanping Wang
Date: 2016-10-10 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

In this paper, a novel model of 3D elastic mesh is presented for image
segmentation. The model is inspired by stress and strain in physical elastic
objects, while the repulsive force and elastic force in the model are defined
slightly different from the physical force to suit the segmentation problem
well. The self-balancing mechanism in the model guarantees the stability of the
method in segmentation. The shape of the elastic mesh at balance state is used
for region segmentation, in which the sign distribution of the points'z
coordinate values is taken as the basis for segmentation. The effectiveness of
the proposed method is proved by analysis and experimental results for both
test images and real world images.
Dataiku's Solution to SPHERE's Activity Recognition Challenge
Authors: Maxime Voisin, Leo Dreyfus-Schmidt, Pierre Gutierrez, Samuel Ronsin, Marc Beillevaire
Date: 2016-10-10 00:00:00
Tasks: Activity Recognition, BIG-bench Machine Learning, Feature Engineering

Our team won the second prize of the Safe Aging with SPHERE Challenge
organized by SPHERE, in conjunction with ECML-PKDD and Driven Data. The goal of
the competition was to recognize activities performed by humans, using sensor
data. This paper presents our solution. It is based on a rich pre-processing
and state of the art machine learning methods. From the raw train data, we
generate a synthetic train set with the same statistical characteristics as the
test set. We then perform feature engineering. The machine learning modeling
part is based on stacking weak learners through a grid searched XGBoost
algorithm. Finally, we use post-processing to smooth our predictions over time.
A New Theoretical and Technological System of Imprecise-Information Processing
Authors: Shiyou Lian
Date: 2016-10-10 00:00:00

Imprecise-information processing will play an indispensable role in
intelligent systems, especially in the anthropomorphic intelligent systems (as
intelligent robots). A new theoretical and technological system of
imprecise-information processing has been founded in Principles of
Imprecise-Information Processing: A New Theoretical and Technological System[1]
which is different from fuzzy technology. The system has clear hierarchy and
rigorous structure, which results from the formation principle of imprecise
information and has solid mathematical and logical bases, and which has many
advantages beyond fuzzy technology. The system provides a technological
platform for relevant applications and lays a theoretical foundation for
further research.
A Dynamic Window Neural Network for CCG Supertagging
Authors: Huijia Wu, Jiajun Zhang, Cheng-qing Zong
Date: 2016-10-10 00:00:00
Tasks: CCG Supertagging, Sentence, Word Embeddings

Combinatory Category Grammar (CCG) supertagging is a task to assign lexical
categories to each word in a sentence. Almost all previous methods use fixed
context window sizes as input features. However, it is obvious that different
tags usually rely on different context window sizes. These motivate us to build
a supertagger with a dynamic window approach, which can be treated as an
attention mechanism on the local contexts. Applying dropout on the dynamic
filters can be seen as drop on words directly, which is superior to the regular
dropout on word embeddings. We use this approach to demonstrate the
state-of-the-art CCG supertagging performance on the standard test set.
Emergence of linguistic laws in human voice
Authors: Ivan Gonzalez Torre, Bartolo Luque, Lucas Lacasa, Jordi Luque, Antoni Hernandez-Fernandez
Date: 2016-10-09 00:00:00

Linguistic laws constitute one of the quantitative cornerstones of modern
cognitive sciences and have been routinely investigated in written corpora, or
in the equivalent transcription of oral corpora. This means that inferences of
statistical patterns of language in acoustics are biased by the arbitrary,
language-dependent segmentation of the signal, and virtually precludes the
possibility of making comparative studies between human voice and other animal
communication systems. Here we bridge this gap by proposing a method that
allows to measure such patterns in acoustic signals of arbitrary origin,
without needs to have access to the language corpus underneath. The method has
been applied to six different human languages, recovering successfully some
well-known laws of human communication at timescales even below the phoneme and
finding yet another link between complexity and criticality in a biological
system. These methods further pave the way for new comparative studies in
animal communication or the analysis of signals of unknown code.
Investigating the effects Diversity Mechanisms have on Evolutionary Algorithms in Dynamic Environments
Authors: Matthew Hughes
Date: 2016-10-09 00:00:00
Tasks: Diversity, Evolutionary Algorithms

Evolutionary algorithms have been successfully applied to a variety of
optimisation problems in stationary environments. However, many real world
optimisation problems are set in dynamic environments where the success
criteria shifts regularly. Population diversity affects algorithmic
performance, particularly on multiobjective and dynamic problems. Diversity
mechanisms are methods of altering evolutionary algorithms in a way that
promotes the maintenance of population diversity. This project intends to
measure and compare the performance effect a variety of diversity mechanisms
have on an evolutionary algorithm when facing an assortment of dynamic
problems.
Egocentric Height Estimation
Authors: Jessica Finocchiaro, Aisha Urooj Khan, Ali Borji
Date: 2016-10-09 00:00:00
Tasks: Object Recognition, Object Tracking, Optical Flow Estimation

Egocentric, or first-person vision which became popular in recent years with
an emerge in wearable technology, is different than exocentric (third-person)
vision in some distinguishable ways, one of which being that the camera wearer
is generally not visible in the video frames. Recent work has been done on
action and object recognition in egocentric videos, as well as work on
biometric extraction from first-person videos. Height estimation can be a
useful feature for both soft-biometrics and object tracking. Here, we propose a
method of estimating the height of an egocentric camera without any calibration
or reference points. We used both traditional computer vision approaches and
deep learning in order to determine the visual cues that results in best height
estimation. Here, we introduce a framework inspired by two stream networks
comprising of two Convolutional Neural Networks, one based on spatial
information, and one based on information given by optical flow in a frame.
Given an egocentric video as an input to the framework, our model yields a
height estimate as an output. We also incorporate late fusion to learn a
combination of temporal and spatial cues. Comparing our model with other
methods we used as baselines, we achieve height estimates for videos with a
Mean Average Error of 14.04 cm over a range of 103 cm of data, and
classification accuracy for relative height (tall, medium or short) up to
93.75% where chance level is 33%.
Multi-Objective Deep Reinforcement Learning
Authors: Hossam Mossalam, Yannis M. Assael, Diederik M. Roijers, Shimon Whiteson
Date: 2016-10-09 00:00:00
Tasks: Deep Reinforcement Learning, Multi-Objective Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose Deep Optimistic Linear Support Learning (DOL) to solve
high-dimensional multi-objective decision problems where the relative
importances of the objectives are not known a priori. Using features from the
high-dimensional inputs, DOL computes the convex coverage set containing all
potential optimal solutions of the convex combinations of the objectives. To
our knowledge, this is the first time that deep reinforcement learning has
succeeded in learning multi-objective policies. In addition, we provide a
testbed with two experiments to be used as a benchmark for deep multi-objective
reinforcement learning.
Open-Ended Visual Question-Answering
Authors: Issey Masuda, Santiago Pascual de la Puente, Xavier Giro-i-Nieto
Date: 2016-10-09 00:00:00
Tasks: Question Answering, Sentence, Sentence Embedding, Sentence-Embedding, Visual Question Answering, Visual Question Answering (VQA)

This thesis report studies methods to solve Visual Question-Answering (VQA)
tasks with a Deep Learning framework. As a preliminary step, we explore Long
Short-Term Memory (LSTM) networks used in Natural Language Processing (NLP) to
tackle Question-Answering (text based). We then modify the previous model to
accept an image as an input in addition to the question. For this purpose, we
explore the VGG-16 and K-CNN convolutional neural networks to extract visual
features from the image. These are merged with the word embedding or with a
sentence embedding of the question to predict the answer. This work was
successfully submitted to the Visual Question Answering Challenge 2016, where
it achieved a 53,62% of accuracy in the test dataset. The developed software
has followed the best programming practices and Python code style, providing a
consistent baseline in Keras for different configurations.
Privacy-Friendly Mobility Analytics using Aggregate Location Data
Authors: Apostolos Pyrgelis, Emiliano De Cristofaro, Gordon Ross
Date: 2016-09-21 00:00:00
Tasks: Privacy Preserving, Time Series, Time Series Analysis

Location data can be extremely useful to study commuting patterns and
disruptions, as well as to predict real-time traffic volumes. At the same time,
however, the fine-grained collection of user locations raises serious privacy
concerns, as this can reveal sensitive information about the users, such as,
life style, political and religious inclinations, or even identities. In this
paper, we study the feasibility of crowd-sourced mobility analytics over
aggregate location information: users periodically report their location, using
a privacy-preserving aggregation protocol, so that the server can only recover
aggregates -- i.e., how many, but not which, users are in a region at a given
time. We experiment with real-world mobility datasets obtained from the
Transport For London authority and the San Francisco Cabs network, and present
a novel methodology based on time series modeling that is geared to forecast
traffic volumes in regions of interest and to detect mobility anomalies in
them. In the presence of anomalies, we also make enhanced traffic volume
predictions by feeding our model with additional information from correlated
regions. Finally, we present and evaluate a mobile app prototype, called
Mobility Data Donors (MDD), in terms of computation, communication, and energy
overhead, demonstrating the real-world deployability of our techniques.
Interpreting Neural Networks to Improve Politeness Comprehension
Authors: Malika Aubakirova, Mohit Bansal
Date: 2016-10-09 00:00:00
Proceeding: EMNLP 2016 11

We present an interpretable neural network approach to predicting and
understanding politeness in natural language requests. Our models are based on
simple convolutional neural networks directly on raw text, avoiding any manual
identification of complex sentiment or syntactic features, while performing
better than such feature-based models from previous work. More importantly, we
use the challenging task of politeness prediction as a testbed to next present
a much-needed understanding of what these successful networks are actually
learning. For this, we present several network visualizations based on
activation clusters, first derivative saliency, and embedding space
transformations, helping us automatically identify several subtle linguistics
markers of politeness theories. Further, this analysis reveals multiple novel,
high-scoring politeness strategies which, when added back as new features,
reduce the accuracy gap between the original featurized system and the neural
model, thus providing a clear quantitative interpretation of the success of
these neural networks.
Proposal for Automatic License and Number Plate Recognition System for Vehicle Identification
Authors: Hamed Saghaei
Date: 2016-10-09 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

In this paper, we propose an automatic and mechanized license and number
plate recognition (LNPR) system which can extract the license plate number of
the vehicles passing through a given location using image processing
algorithms. No additional devices such as GPS or radio frequency identification
(RFID) need to be installed for implementing the proposed system. Using special
cameras, the system takes pictures from each passing vehicle and forwards the
image to the computer for being processed by the LPR software. Plate
recognition software uses different algorithms such as localization,
orientation, normalization, segmentation and finally optical character
recognition (OCR). The resulting data is applied to compare with the records on
a database. Experimental results reveal that the presented system successfully
detects and recognizes the vehicle number plate on real images. This system can
also be used for security and traffic control.
Zero Shot Hashing
Authors: Shubham Pachori, Shanmuganathan Raman
Date: 2016-10-09 00:00:00
Tasks: Object Recognition, Zero-Shot Learning

This paper provides a framework to hash images containing instances of
unknown object classes. In many object recognition problems, we might have
access to huge amount of data. It may so happen that even this huge data
doesn't cover the objects belonging to classes that we see in our day to day
life. Zero shot learning exploits auxiliary information (also called as
signatures) in order to predict the labels corresponding to unknown classes. In
this work, we attempt to generate the hash codes for images belonging to unseen
classes, information of which is available only through the textual corpus. We
formulate this as an unsupervised hashing formulation as the exact labels are
not available for the instances of unseen classes. We show that the proposed
solution is able to generate hash codes which can predict labels corresponding
to unseen classes with appreciably good precision.
A new selection strategy for selective cluster ensemble based on Diversity and Independency
Authors: Muhammad Yousefnezhad, Ali Reihanian, Daoqiang Zhang, Behrouz Minaei-Bidgoli
Date: 2016-10-09 00:00:00
Tasks: Clustering, Diversity, software testing

This research introduces a new strategy in cluster ensemble selection by
using Independency and Diversity metrics. In recent years, Diversity and
Quality, which are two metrics in evaluation procedure, have been used for
selecting basic clustering results in the cluster ensemble selection. Although
quality can improve the final results in cluster ensemble, it cannot control
the procedures of generating basic results, which causes a gap in prediction of
the generated basic results' accuracy. Instead of quality, this paper
introduces Independency as a supplementary method to be used in conjunction
with Diversity. Therefore, this paper uses a heuristic metric, which is based
on the procedure of converting code to graph in Software Testing, in order to
calculate the Independency of two basic clustering algorithms. Moreover, a new
modeling language, which we called as "Clustering Algorithms Independency
Language" (CAIL), is introduced in order to generate graphs which depict
Independency of algorithms. Also, Uniformity, which is a new similarity metric,
has been introduced for evaluating the diversity of basic results. As a
credential, our experimental results on varied different standard data sets
show that the proposed framework improves the accuracy of final results
dramatically in comparison with other cluster ensemble methods.
Neural versus Phrase-Based Machine Translation Quality: a Case Study
Authors: Luisa Bentivogli, Arianna Bisazza, Mauro Cettolo, Marcello Federico
Date: 2016-08-16 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Machine Translation, NMT, Translation

Within the field of Statistical Machine Translation (SMT), the neural
approach (NMT) has recently emerged as the first technology able to challenge
the long-standing dominance of phrase-based approaches (PBMT). In particular,
at the IWSLT 2015 evaluation campaign, NMT outperformed well established
state-of-the-art PBMT systems on English-German, a language pair known to be
particularly hard because of morphology and syntactic differences. To
understand in what respects NMT provides better translation quality than PBMT,
we perform a detailed analysis of neural versus phrase-based SMT outputs,
leveraging high quality post-edits performed by professional translators on the
IWSLT data. For the first time, our analysis provides useful insights on what
linguistic phenomena are best modeled by neural models -- such as the
reordering of verbs -- while pointing out other aspects that remain to be
improved.
Online semi-parametric learning for inverse dynamics modeling
Authors: Diego Romeres, Mattia Zorzi, Raffaello Camoriano, Alessandro Chiuso
Date: 2016-03-17 00:00:00

This paper presents a semi-parametric algorithm for online learning of a
robot inverse dynamics model. It combines the strength of the parametric and
non-parametric modeling. The former exploits the rigid body dynamics equa-
tion, while the latter exploits a suitable kernel function. We provide an
extensive comparison with other methods from the literature using real data
from the iCub humanoid robot. In doing so we also compare two different
techniques, namely cross validation and marginal likelihood optimization, for
estimating the hyperparameters of the kernel function.