pwc-archive/papers-with-abstracts huggingface dataset:
Grounding Spatio-Semantic Referring Expressions for Human-Robot Interaction
Authors: Mohit Shridhar, David Hsu
Date: 2017-07-18 00:00:00
Tasks: Object

The human language is one of the most natural interfaces for humans to
interact with robots. This paper presents a robot system that retrieves
everyday objects with unconstrained natural language descriptions. A core issue
for the system is semantic and spatial grounding, which is to infer objects and
their spatial relationships from images and natural language expressions. We
introduce a two-stage neural-network grounding pipeline that maps natural
language referring expressions directly to objects in the images. The first
stage uses visual descriptions in the referring expressions to generate a
candidate set of relevant objects. The second stage examines all pairwise
relationships between the candidates and predicts the most likely referred
object according to the spatial descriptions in the referring expressions. A
key feature of our system is that by leveraging a large dataset of images
labeled with text descriptions, it allows unrestricted object types and natural
language referring expressions. Preliminary results indicate that our system
outperforms a near state-of-the-art object comprehension system on standard
benchmark datasets. We also present a robot system that follows voice commands
to pick and place previously unseen objects.
Discovering topics in text datasets by visualizing relevant words
Authors: Franziska Horn, Leila Arras, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek
Date: 2017-07-18 00:00:00
Tasks: Clustering

When dealing with large collections of documents, it is imperative to quickly
get an overview of the texts' contents. In this paper we show how this can be
achieved by using a clustering algorithm to identify topics in the dataset and
then selecting and visualizing relevant words, which distinguish a group of
documents from the rest of the texts, to summarize the contents of the
documents belonging to each topic. We demonstrate our approach by discovering
trending topics in a collection of New York Times article snippets.
An Iterative BP-CNN Architecture for Channel Decoding
Authors: Fei Liang, Cong Shen, Feng Wu
Date: 2017-07-18 00:00:00
Tasks: Decoder, Noise Estimation

Inspired by recent advances in deep learning, we propose a novel iterative
BP-CNN architecture for channel decoding under correlated noise. This
architecture concatenates a trained convolutional neural network (CNN) with a
standard belief-propagation (BP) decoder. The standard BP decoder is used to
estimate the coded bits, followed by a CNN to remove the estimation errors of
the BP decoder and obtain a more accurate estimation of the channel noise.
Iterating between BP and CNN will gradually improve the decoding SNR and hence
result in better decoding performance. To train a well-behaved CNN model, we
define a new loss function which involves not only the accuracy of the noise
estimation but also the normality test for the estimation errors, i.e., to
measure how likely the estimation errors follow a Gaussian distribution. The
introduction of the normality test to the CNN training shapes the residual
noise distribution and further reduces the BER of the iterative decoding,
compared to using the standard quadratic loss function. We carry out extensive
experiments to analyze and verify the proposed framework. The iterative BP-CNN
decoder has better BER performance with lower complexity, is suitable for
parallel implementation, does not rely on any specific channel model or
encoding method, and is robust against training mismatches. All of these
features make it a good candidate for decoding modern channel codes.
Learning Fashion Compatibility with Bidirectional LSTMs
Authors: Xintong Han, Zuxuan Wu, Yu-Gang Jiang, Larry S. Davis
Date: 2017-07-18 00:00:00
Tasks: Attribute

The ubiquity of online fashion shopping demands effective recommendation
services for customers. In this paper, we study two types of fashion
recommendation: (i) suggesting an item that matches existing components in a
set to form a stylish outfit (a collection of fashion items), and (ii)
generating an outfit with multimodal (images/text) specifications from a user.
To this end, we propose to jointly learn a visual-semantic embedding and the
compatibility relationships among fashion items in an end-to-end fashion. More
specifically, we consider a fashion outfit to be a sequence (usually from top
to bottom and then accessories) and each item in the outfit as a time step.
Given the fashion items in an outfit, we train a bidirectional LSTM (Bi-LSTM)
model to sequentially predict the next item conditioned on previous ones to
learn their compatibility relationships. Further, we learn a visual-semantic
space by regressing image features to their semantic representations aiming to
inject attribute and category information as a regularization for training the
LSTM. The trained network can not only perform the aforementioned
recommendations effectively but also predict the compatibility of a given
outfit. We conduct extensive experiments on our newly collected Polyvore
dataset, and the results provide strong qualitative and quantitative evidence
that our framework outperforms alternative methods.
Hashed Binary Search Sampling for Convolutional Network Training with Large Overhead Image Patches
Authors: Dalton Lunga, Lexie Yang, Budhendra Bhaduri
Date: 2017-07-18 00:00:00
Tasks: BIG-bench Machine Learning

Very large overhead imagery associated with ground truth maps has the
potential to generate billions of training image patches for machine learning
algorithms. However, random sampling selection criteria often leads to
redundant and noisy-image patches for model training. With minimal research
efforts behind this challenge, the current status spells missed opportunities
to develop supervised learning algorithms that generalize over wide
geographical scenes. In addition, much of the computational cycles for large
scale machine learning are poorly spent crunching through noisy and redundant
image patches. We demonstrate a potential framework to address these challenges
specifically, while evaluating a human settlement detection task. A novel
binary search tree sampling scheme is fused with a kernel based hashing
procedure that maps image patches into hash-buckets using binary codes
generated from image content. The framework exploits inherent redundancy within
billions of image patches to promote mostly high variance preserving samples
for accelerating algorithmic training and increasing model generalization.
Exploiting Convolutional Representations for Multiscale Human Settlement Detection
Authors: Dalton Lunga, Dilip Patlolla, Lexie Yang, Jeanette Weaver, Budhendra Bhadhuri
Date: 2017-07-18 00:00:00
Tasks: Transfer Learning

We test this premise and explore representation spaces from a single deep
convolutional network and their visualization to argue for a novel unified
feature extraction framework. The objective is to utilize and re-purpose
trained feature extractors without the need for network retraining on three
remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and
semantic based image visualization. By leveraging the same convolutional
feature extractors and viewing them as visual information extractors that
encode different image representation spaces, we demonstrate a preliminary
inductive transfer learning potential on multiscale experiments that
incorporate edge-level details up to semantic-level information.
Empirical evaluation of a Q-Learning Algorithm for Model-free Autonomous Soaring
Authors: Erwan Lecarpentier, Sebastian Rapp, Marc Melo, Emmanuel Rachelson
Date: 2017-07-18 00:00:00
Tasks: Q-Learning, Reinforcement Learning

Autonomous unpowered flight is a challenge for control and guidance systems:
all the energy the aircraft might use during flight has to be harvested
directly from the atmosphere. We investigate the design of an algorithm that
optimizes the closed-loop control of a glider's bank and sideslip angles, while
flying in the lower convective layer of the atmosphere in order to increase its
mission endurance. Using a Reinforcement Learning approach, we demonstrate the
possibility for real-time adaptation of the glider's behaviour to the
time-varying and noisy conditions associated with thermal soaring flight. Our
approach is online, data-based and model-free, hence avoids the pitfalls of
aerological and aircraft modelling and allow us to deal with uncertainties and
non-stationarity. Additionally, we put a particular emphasis on keeping low
computational requirements in order to make on-board execution feasible. This
article presents the stochastic, time-dependent aerological model used for
simulation, together with a standard aircraft model. Then we introduce an
adaptation of a Q-learning algorithm and demonstrate its ability to control the
aircraft and improve its endurance by exploiting updrafts in non-stationary
scenarios.
Learning Powers of Poisson Binomial Distributions
Authors: Dimitris Fotakis, Vasilis Kontonis, Piotr Krysta, Paul Spirakis
Date: 2017-07-18 00:00:00

We introduce the problem of simultaneously learning all powers of a Poisson
Binomial Distribution (PBD). A PBD of order $n$ is the distribution of a sum of
$n$ mutually independent Bernoulli random variables $X_i$, where
$\mathbb{E}[X_i] = p_i$. The $k$'th power of this distribution, for $k$ in a
range $[m]$, is the distribution of $P_k = \sum_{i=1}^n X_i^{(k)}$, where each
Bernoulli random variable $X_i^{(k)}$ has $\mathbb{E}[X_i^{(k)}] = (p_i)^k$.
The learning algorithm can query any power $P_k$ several times and succeeds in
learning all powers in the range, if with probability at least $1- \delta$:
given any $k \in [m]$, it returns a probability distribution $Q_k$ with total
variation distance from $P_k$ at most $\epsilon$. We provide almost matching
lower and upper bounds on query complexity for this problem. We first show a
lower bound on the query complexity on PBD powers instances with many distinct
parameters $p_i$ which are separated, and we almost match this lower bound by
examining the query complexity of simultaneously learning all the powers of a
special class of PBD's resembling the PBD's of our lower bound. We study the
fundamental setting of a Binomial distribution, and provide an optimal
algorithm which uses $O(1/\epsilon^2)$ samples. Diakonikolas, Kane and Stewart
[COLT'16] showed a lower bound of $\Omega(2^{1/\epsilon})$ samples to learn the
$p_i$'s within error $\epsilon$. The question whether sampling from powers of
PBDs can reduce this sampling complexity, has a negative answer since we show
that the exponential number of samples is inevitable. Having sampling access to
the powers of a PBD we then give a nearly optimal algorithm that learns its
$p_i$'s. To prove our two last lower bounds we extend the classical minimax
risk definition from statistics to estimating functions of sequences of
distributions.
You said that?
Authors: Joon Son Chung, Amir Jamaludin, Andrew Zisserman
Date: 2017-05-08 00:00:00
Tasks: Decoder, Unconstrained Lip-synchronization

We present a method for generating a video of a talking face. The method
takes as inputs: (i) still images of the target face, and (ii) an audio speech
segment; and outputs a video of the target face lip synched with the audio. The
method runs in real time and is applicable to faces and audio not seen at
training time.
  To achieve this we propose an encoder-decoder CNN model that uses a joint
embedding of the face and audio to generate synthesised talking face video
frames. The model is trained on tens of hours of unlabelled videos.
  We also show results of re-dubbing videos using speech from a different
person.
Spherical Paragraph Model
Authors: Ruqing Zhang, Jiafeng Guo, Yanyan Lan, Jun Xu, Xue-Qi Cheng
Date: 2017-07-18 00:00:00
Tasks: model, Representation Learning, Sentiment Analysis

Representing texts as fixed-length vectors is central to many language
processing tasks. Most traditional methods build text representations based on
the simple Bag-of-Words (BoW) representation, which loses the rich semantic
relations between words. Recent advances in natural language processing have
shown that semantically meaningful representations of words can be efficiently
acquired by distributed models, making it possible to build text
representations based on a better foundation called the Bag-of-Word-Embedding
(BoWE) representation. However, existing text representation methods using BoWE
often lack sound probabilistic foundations or cannot well capture the semantic
relatedness encoded in word vectors. To address these problems, we introduce
the Spherical Paragraph Model (SPM), a probabilistic generative model based on
BoWE, for text representation. SPM has good probabilistic interpretability and
can fully leverage the rich semantics of words, the word co-occurrence
information as well as the corpus-wide information to help the representation
learning of texts. Experimental results on topical classification and sentiment
analysis demonstrate that SPM can achieve new state-of-the-art performances on
several benchmark datasets.
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
Authors: Anup Das, Paruthi Pradhapan, Willemijn Groenendaal, Prathyusha Adiraju, Raj Thilak Rajan, Francky Catthoor, Siebren Schaafsma, Jeffrey L. Krichmar, Nikil Dutt, Chris Van Hoof
Date: 2017-07-18 00:00:00
Tasks: Clustering, GPU, Heart rate estimation

Heart-rate estimation is a fundamental feature of modern wearable devices. In
this paper we propose a machine intelligent approach for heart-rate estimation
from electrocardiogram (ECG) data collected using wearable devices. The novelty
of our approach lies in (1) encoding spatio-temporal properties of ECG signals
directly into spike train and using this to excite recurrently connected
spiking neurons in a Liquid State Machine computation model; (2) a novel
learning algorithm; and (3) an intelligently designed unsupervised readout
based on Fuzzy c-Means clustering of spike responses from a subset of neurons
(Liquid states), selected using particle swarm optimization. Our approach
differs from existing works by learning directly from ECG signals (allowing
personalization), without requiring costly data annotations. Additionally, our
approach can be easily implemented on state-of-the-art spiking-based
neuromorphic systems, offering high accuracy, yet significantly low energy
footprint, leading to an extended battery life of wearable devices. We
validated our approach with CARLsim, a GPU accelerated spiking neural network
simulator modeling Izhikevich spiking neurons with Spike Timing Dependent
Plasticity (STDP) and homeostatic scaling. A range of subjects are considered
from in-house clinical trials and public ECG databases. Results show high
accuracy and low energy footprint in heart-rate estimation across subjects with
and without cardiac irregularities, signifying the strong potential of this
approach to be integrated in future wearable devices.
Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data
Authors: Wenjing Liao, Mauro Maggioni
Date: 2016-11-03 00:00:00
Tasks: Dictionary Learning

We consider the problem of efficiently approximating and encoding
high-dimensional data sampled from a probability distribution $\rho$ in
$\mathbb{R}^D$, that is nearly supported on a $d$-dimensional set $\mathcal{M}$
- for example supported on a $d$-dimensional Riemannian manifold. Geometric
Multi-Resolution Analysis (GMRA) provides a robust and computationally
efficient procedure to construct low-dimensional geometric approximations of
$\mathcal{M}$ at varying resolutions. We introduce a thresholding algorithm on
the geometric wavelet coefficients, leading to what we call adaptive GMRA
approximations. We show that these data-driven, empirical approximations
perform well, when the threshold is chosen as a suitable universal function of
the number of samples $n$, on a wide variety of measures $\rho$, that are
allowed to exhibit different regularity at different scales and locations,
thereby efficiently encoding data from more complex measures than those
supported on manifolds. These approximations yield a data-driven dictionary,
together with a fast transform mapping data to coefficients, and an inverse of
such a map. The algorithms for both the dictionary construction and the
transforms have complexity $C n \log n$ with the constant linear in $D$ and
exponential in $d$. Our work therefore establishes adaptive GMRA as a fast
dictionary learning algorithm with approximation guarantees. We include several
numerical experiments on both synthetic and real data, confirming our
theoretical results and demonstrating the effectiveness of adaptive GMRA.
Graph learning under sparsity priors
Authors: Hermina Petric Maretic, Dorina Thanou, Pascal Frossard
Date: 2017-07-18 00:00:00
Tasks: Graph Learning

Graph signals offer a very generic and natural representation for data that
lives on networks or irregular structures. The actual data structure is however
often unknown a priori but can sometimes be estimated from the knowledge of the
application domain. If this is not possible, the data structure has to be
inferred from the mere signal observations. This is exactly the problem that we
address in this paper, under the assumption that the graph signals can be
represented as a sparse linear combination of a few atoms of a structured graph
dictionary. The dictionary is constructed on polynomials of the graph
Laplacian, which can sparsely represent a general class of graph signals
composed of localized patterns on the graph. We formulate a graph learning
problem, whose solution provides an ideal fit between the signal observations
and the sparse graph signal model. As the problem is non-convex, we propose to
solve it by alternating between a signal sparse coding and a graph update step.
We provide experimental results that outline the good graph recovery
performance of our method, which generally compares favourably to other recent
network inference algorithms.
Multi-label Classification using Labels as Hidden Nodes
Authors: Jesse Read, Jaakko Hollmén
Date: 2015-03-31 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Competitive methods for multi-label classification typically invest in
learning labels together. To do so in a beneficial way, analysis of label
dependence is often seen as a fundamental step, separate and prior to
constructing a classifier. Some methods invest up to hundreds of times more
computational effort in building dependency models, than training the final
classifier itself. We extend some recent discussion in the literature and
provide a deeper analysis, namely, developing the view that label dependence is
often introduced by an inadequate base classifier, rather than being inherent
to the data or underlying concept; showing how even an exhaustive analysis of
label dependence may not lead to an optimal classification structure. Viewing
labels as additional features (a transformation of the input), we create
neural-network inspired novel methods that remove the emphasis of a prior
dependency structure. Our methods have an important advantage particular to
multi-label data: they leverage labels to create effective units in middle
layers, rather than learning these units from scratch in an unsupervised
fashion with gradient-based methods. Results are promising. The methods we
propose perform competitively, and also have very important qualities of
scalability.
Predicting Target Language CCG Supertags Improves Neural Machine Translation
Authors: Maria Nadejde, Siva Reddy, Rico Sennrich, Tomasz Dwojak, Marcin Junczys-Dowmunt, Philipp Koehn, Alexandra Birch
Date: 2017-02-03 00:00:00
Proceeding: WS 2017 9
Tasks: Decoder, Machine Translation, NMT, Prepositional Phrase Attachment, Translation

Neural machine translation (NMT) models are able to partially learn syntactic
information from sequential lexical information. Still, some complex syntactic
phenomena such as prepositional phrase attachment are poorly modeled. This work
aims to answer two questions: 1) Does explicitly modeling target language
syntax help NMT? 2) Is tight integration of words and syntax better than
multitask training? We introduce syntactic information in the form of CCG
supertags in the decoder, by interleaving the target supertags with the word
sequence. Our results on WMT data show that explicitly modeling target-syntax
improves machine translation quality for German->English, a high-resource pair,
and for Romanian->English, a low-resource pair and also several syntactic
phenomena including prepositional phrase attachment. Furthermore, a tight
coupling of words and syntax improves translation quality more than multitask
training. By combining target-syntax with adding source-side dependency labels
in the embedding layer, we obtain a total improvement of 0.9 BLEU for
German->English and 1.2 BLEU for Romanian->English.
Domain Adaptation for Resume Classification Using Convolutional Neural Networks
Authors: Luiza Sayfullina, Eric Malmi, Yiping Liao, Alex Jung
Date: 2017-07-18 00:00:00
Tasks: Classification, Domain Adaptation, General Classification

We propose a novel method for classifying resume data of job applicants into
27 different job categories using convolutional neural networks. Since resume
data is costly and hard to obtain due to its sensitive nature, we use domain
adaptation. In particular, we train a classifier on a large number of freely
available job description snippets and then use it to classify resume data. We
empirically verify a reasonable classification performance of our approach
despite having only a small amount of labeled resume data available.
Fast Feature Fool: A data independent approach to universal adversarial perturbations
Authors: Konda Reddy Mopuri, Utsav Garg, R. Venkatesh Babu
Date: 2017-07-18 00:00:00
Tasks: Object Recognition

State-of-the-art object recognition Convolutional Neural Networks (CNNs) are
shown to be fooled by image agnostic perturbations, called universal
adversarial perturbations. It is also observed that these perturbations
generalize across multiple networks trained on the same target data. However,
these algorithms require training data on which the CNNs were trained and
compute adversarial perturbations via complex optimization. The fooling
performance of these approaches is directly proportional to the amount of
available training data. This makes them unsuitable for practical attacks since
its unreasonable for an attacker to have access to the training data. In this
paper, for the first time, we propose a novel data independent approach to
generate image agnostic perturbations for a range of CNNs trained for object
recognition. We further show that these perturbations are transferable across
multiple network architectures trained either on same or different data. In the
absence of data, our method generates universal adversarial perturbations
efficiently via fooling the features learned at multiple layers thereby causing
CNNs to misclassify. Experiments demonstrate impressive fooling rates and
surprising transferability for the proposed universal perturbations generated
without any training data.