pwc-archive/papers-with-abstracts huggingface dataset:
ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?
Authors: Mostapha Benhenda
Date: 2017-08-28 00:00:00
Tasks: Diversity, Drug Discovery, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Generating molecules with desired chemical properties is important for drug
discovery. The use of generative neural networks is promising for this task.
However, from visual inspection, it often appears that generated samples lack
diversity. In this paper, we quantify this internal chemical diversity, and we
raise the following challenge: can a nontrivial AI model reproduce natural
chemical diversity for desired molecules? To illustrate this question, we
consider two generative models: a Reinforcement Learning model and the recently
introduced ORGAN. Both fail at this challenge. We hope this challenge will
stimulate research in this direction.
Sketching the order of events
Authors: Terry Lyons, Harald Oberhauser
Date: 2017-08-31 00:00:00

We introduce features for massive data streams. These stream features can be
thought of as "ordered moments" and generalize stream sketches from "moments of
order one" to "ordered moments of arbitrary order". In analogy to classic
moments, they have theoretical guarantees such as universality that are
important for learning algorithms.
Human and Machine Judgements for Russian Semantic Relatedness
Authors: Alexander Panchenko, Dmitry Ustalov, Nikolay Arefyev, Denis Paperno, Natalia Konstantinova, Natalia Loukachevitch, Chris Biemann
Date: 2017-08-31 00:00:00

Semantic relatedness of terms represents similarity of meaning by a numerical
score. On the one hand, humans easily make judgments about semantic
relatedness. On the other hand, this kind of information is useful in language
processing systems. While semantic relatedness has been extensively studied for
English using numerous language resources, such as associative norms, human
judgments, and datasets generated from lexical databases, no evaluation
resources of this kind have been available for Russian to date. Our
contribution addresses this problem. We present five language resources of
different scale and purpose for Russian semantic relatedness, each being a list
of triples (word_i, word_j, relatedness_ij). Four of them are designed for
evaluation of systems for computing semantic relatedness, complementing each
other in terms of the semantic relation type they represent. These benchmarks
were used to organize a shared task on Russian semantic relatedness, which
attracted 19 teams. We use one of the best approaches identified in this
competition to generate the fifth high-coverage resource, the first open
distributional thesaurus of Russian. Multiple evaluations of this thesaurus,
including a large-scale crowdsourcing study involving native speakers, indicate
its high accuracy.
Boosting with Lexicographic Programming: Addressing Class Imbalance without Cost Tuning
Authors: Shounak Datta, Sayak Nag, Swagatam Das
Date: 2017-08-31 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification, imbalanced classification

A large amount of research effort has been dedicated to adapting boosting for
imbalanced classification. However, boosting methods are yet to be
satisfactorily immune to class imbalance, especially for multi-class problems.
This is because most of the existing solutions for handling class imbalance
rely on expensive cost set tuning for determining the proper level of
compensation. We show that the assignment of weights to the component
classifiers of a boosted ensemble can be thought of as a game of Tug of War
between the classes in the margin space. We then demonstrate how this insight
can be used to attain a good compromise between the rare and abundant classes
without having to resort to cost set tuning, which has long been the norm for
imbalanced classification. The solution is based on a lexicographic linear
programming framework which requires two stages. Initially, class-specific
component weight combinations are found so as to minimize a hinge loss
individually for each of the classes. Subsequently, the final component weights
are assigned so that the maximum deviation from the class-specific minimum loss
values (obtained in the previous stage) is minimized. Hence, the proposal is
not only restricted to two-class situations, but is also readily applicable to
multi-class problems. Additionally,we also derive the dual formulation
corresponding to the proposed framework. Experiments conducted on artificial
and real-world imbalanced datasets as well as on challenging applications such
as hyperspectral image classification and ImageNet classification establish the
efficacy of the proposal.
Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks
Authors: Patricia Binder, Michael Muma, Abdelhak M. Zoubir
Date: 2017-08-31 00:00:00
Tasks: Clustering

Distributed signal processing for wireless sensor networks enables that
different devices cooperate to solve different signal processing tasks. A
crucial first step is to answer the question: who observes what? Recently,
several distributed algorithms have been proposed, which frame the
signal/object labelling problem in terms of cluster analysis after extracting
source-specific features, however, the number of clusters is assumed to be
known. We propose a new method called Gravitational Clustering (GC) to
adaptively estimate the time-varying number of clusters based on a set of
feature vectors. The key idea is to exploit the physical principle of
gravitational force between mass units: streaming-in feature vectors are
considered as mass units of fixed position in the feature space, around which
mobile mass units are injected at each time instant. The cluster enumeration
exploits the fact that the highest attraction on the mobile mass units is
exerted by regions with a high density of feature vectors, i.e., gravitational
clusters. By sharing estimates among neighboring nodes via a
diffusion-adaptation scheme, cooperative and distributed cluster enumeration is
achieved. Numerical experiments concerning robustness against outliers,
convergence and computational complexity are conducted. The application in a
distributed cooperative multi-view camera network illustrates the applicability
to real-world problems.
Gate Activation Signal Analysis for Gated Recurrent Neural Networks and Its Correlation with Phoneme Boundaries
Authors: Yu-Hsuan Wang, Cheng-Tao Chung, Hung-Yi Lee
Date: 2017-03-22 00:00:00

In this paper we analyze the gate activation signals inside the gated
recurrent neural networks, and find the temporal structure of such signals is
highly correlated with the phoneme boundaries. This correlation is further
verified by a set of experiments for phoneme segmentation, in which better
results compared to standard approaches were obtained.
Modelling and computation using NCoRM mixtures for density regression
Authors: Jim Griffin, Fabrizio Leisen
Date: 2016-08-02 00:00:00
Tasks: regression

Normalized compound random measures are flexible nonparametric priors for
related distributions. We consider building general nonparametric regression
models using normalized compound random measure mixture models. Posterior
inference is made using a novel pseudo-marginal Metropolis-Hastings sampler for
normalized compound random measure mixture models. The algorithm makes use of a
new general approach to the unbiased estimation of Laplace functionals of
compound random measures (which includes completely random measures as a
special case). The approach is illustrated on problems of density regression.
Abnormal Event Detection in Videos using Generative Adversarial Nets
Authors: Mahdyar Ravanbakhsh, Moin Nabi, Enver Sangineto, Lucio Marcenaro, Carlo Regazzoni, Nicu Sebe
Date: 2017-08-31 00:00:00
Tasks: Abnormal Event Detection In Video, Anomaly Detection, Event Detection, Optical Flow Estimation, Semi-supervised Anomaly Detection

In this paper we address the abnormality detection problem in crowded scenes.
We propose to use Generative Adversarial Nets (GANs), which are trained using
normal frames and corresponding optical-flow images in order to learn an
internal representation of the scene normality. Since our GANs are trained with
only normal data, they are not able to generate abnormal events. At testing
time the real data are compared with both the appearance and the motion
representations reconstructed by our GANs and abnormal areas are detected by
computing local differences. Experimental results on challenging abnormality
detection datasets show the superiority of the proposed method compared to the
state of the art in both frame-level and pixel-level abnormality detection
tasks.
Unknown sparsity in compressed sensing: Denoising and inference
Authors: Miles E. Lopes
Date: 2015-07-25 00:00:00
Tasks: compressed sensing, Denoising

The theory of Compressed Sensing (CS) asserts that an unknown signal
$x\in\mathbb{R}^p$ can be accurately recovered from an underdetermined set of
$n$ linear measurements with $n\ll p$, provided that $x$ is sufficiently
sparse. However, in applications, the degree of sparsity $\|x\|_0$ is typically
unknown, and the problem of directly estimating $\|x\|_0$ has been a
longstanding gap between theory and practice. A closely related issue is that
$\|x\|_0$ is a highly idealized measure of sparsity, and for real signals with
entries not equal to 0, the value $\|x\|_0=p$ is not a useful description of
compressibility. In our previous conference paper [Lop13] that examined these
problems, we considered an alternative measure of "soft" sparsity,
$\|x\|_1^2/\|x\|_2^2$, and designed a procedure to estimate
$\|x\|_1^2/\|x\|_2^2$ that does not rely on sparsity assumptions.
  The present work offers a new deconvolution-based method for estimating
unknown sparsity, which has wider applicability and sharper theoretical
guarantees. In particular, we introduce a family of entropy-based sparsity
measures $s_q(x):=\big(\frac{\|x\|_q}{\|x\|_1}\big)^{\frac{q}{1-q}}$
parameterized by $q\in[0,\infty]$. This family interpolates between
$\|x\|_0=s_0(x)$ and $\|x\|_1^2/\|x\|_2^2=s_2(x)$ as $q$ ranges over $[0,2]$.
For any $q\in (0,2]\setminus\{1\}$, we propose an estimator $\hat{s}_q(x)$
whose relative error converges at the dimension-free rate of $1/\sqrt{n}$, even
when $p/n\to\infty$. Our main results also describe the limiting distribution
of $\hat{s}_q(x)$, as well as some connections to Basis Pursuit Denosing, the
Lasso, deterministic measurement matrices, and inference problems in CS.
Neural Class-Specific Regression for face verification
Authors: Guanqun Cao, Alexandros Iosifidis, Moncef Gabbouj
Date: 2017-08-31 00:00:00
Tasks: Face Verification, regression

Face verification is a problem approached in the literature mainly using
nonlinear class-specific subspace learning techniques. While it has been shown
that kernel-based Class-Specific Discriminant Analysis is able to provide
excellent performance in small- and medium-scale face verification problems,
its application in today's large-scale problems is difficult due to its
training space and computational requirements. In this paper, generalizing our
previous work on kernel-based class-specific discriminant analysis, we show
that class-specific subspace learning can be cast as a regression problem. This
allows us to derive linear, (reduced) kernel and neural network-based
class-specific discriminant analysis methods using efficient batch and/or
iterative training schemes, suited for large-scale learning problems. We test
the performance of these methods in two datasets describing medium- and
large-scale face verification problems.
Automatic Semantic Style Transfer using Deep Convolutional Neural Networks and Soft Masks
Authors: Huihuang Zhao, Paul L. Rosin, Yu-Kun Lai
Date: 2017-08-31 00:00:00
Tasks: Image Generation, Style Transfer

This paper presents an automatic image synthesis method to transfer the style
of an example image to a content image. When standard neural style transfer
approaches are used, the textures and colours in different semantic regions of
the style image are often applied inappropriately to the content image,
ignoring its semantic layout, and ruining the transfer result. In order to
reduce or avoid such effects, we propose a novel method based on automatically
segmenting the objects and extracting their soft semantic masks from the style
and content images, in order to preserve the structure of the content image
while having the style transferred. Each soft mask of the style image
represents a specific part of the style image, corresponding to the soft mask
of the content image with the same semantics. Both the soft masks and source
images are provided as multichannel input to an augmented deep CNN framework
for style transfer which incorporates a generative Markov random field (MRF)
model. Results on various images show that our method outperforms the most
recent techniques.
ALCN: Meta-Learning for Contrast Normalization Applied to Robust 3D Pose Estimation
Authors: Mahdi Rad, Peter M. Roth, Vincent Lepetit
Date: 2017-08-31 00:00:00
Tasks: 3D Pose Estimation, Meta-Learning, Pose Estimation

To be robust to illumination changes when detecting objects in images, the
current trend is to train a Deep Network with training images captured under
many different lighting conditions. Unfortunately, creating such a training set
is very cumbersome, or sometimes even impossible, for some applications such as
3D pose estimation of specific objects, which is the application we focus on in
this paper. We therefore propose a novel illumination normalization method that
lets us learn to detect objects and estimate their 3D pose under challenging
illumination conditions from very few training samples. Our key insight is that
normalization parameters should adapt to the input image. In particular, we
realized this via a Convolutional Neural Network trained to predict the
parameters of a generalization of the Difference-of-Gaussians method. We show
that our method significantly outperforms standard normalization methods and
demonstrate it on two challenging 3D detection and pose estimation problems.
Generalized Multi-view Embedding for Visual Recognition and Cross-modal Retrieval
Authors: Guanqun Cao, Alexandros Iosifidis, Ke Chen, Moncef Gabbouj
Date: 2016-05-31 00:00:00
Tasks: Cross-Modal Retrieval, Image Retrieval, Object Recognition, Retrieval

In this paper, the problem of multi-view embedding from different visual cues
and modalities is considered. We propose a unified solution for subspace
learning methods using the Rayleigh quotient, which is extensible for multiple
views, supervised learning, and non-linear embeddings. Numerous methods
including Canonical Correlation Analysis, Partial Least Sqaure regression and
Linear Discriminant Analysis are studied using specific intrinsic and penalty
graphs within the same framework. Non-linear extensions based on kernels and
(deep) neural networks are derived, achieving better performance than the
linear ones. Moreover, a novel Multi-view Modular Discriminant Analysis (MvMDA)
is proposed by taking the view difference into consideration. We demonstrate
the effectiveness of the proposed multi-view embedding methods on visual object
recognition and cross-modal image retrieval, and obtain superior results in
both applications compared to related methods.
Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation
Authors: Greg Durrett, Jonathan K. Kummerfeld, Taylor Berg-Kirkpatrick, Rebecca S. Portnoff, Sadia Afroz, Damon McCoy, Kirill Levchenko, Vern Paxson
Date: 2017-08-31 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Domain Adaptation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), slot-filling, Slot Filling

One weakness of machine-learned NLP models is that they typically perform
poorly on out-of-domain data. In this work, we study the task of identifying
products being bought and sold in online cybercrime forums, which exhibits
particularly challenging cross-domain effects. We formulate a task that
represents a hybrid of slot-filling information extraction and named entity
recognition and annotate data from four different forums. Each of these forums
constitutes its own "fine-grained domain" in that the forums cover different
market sectors with different properties, even though all forums are in the
broad domain of cybercrime. We characterize these domain differences in the
context of a learning-based system: supervised models see decreased accuracy
when applied to new forums, and standard techniques for semi-supervised
learning and domain adaptation have limited effectiveness on this data, which
suggests the need to improve these techniques. We release a dataset of 1,938
annotated posts from across the four forums.
Acoustic Feature Learning via Deep Variational Canonical Correlation Analysis
Authors: Qingming Tang, Weiran Wang, Karen Livescu
Date: 2017-08-11 00:00:00
Tasks: Representation Learning

We study the problem of acoustic feature learning in the setting where we
have access to another (non-acoustic) modality for feature learning but not at
test time. We use deep variational canonical correlation analysis (VCCA), a
recently proposed deep generative method for multi-view representation
learning. We also extend VCCA with improved latent variable priors and with
adversarial learning. Compared to other techniques for multi-view feature
learning, VCCA's advantages include an intuitive latent variable interpretation
and a variational lower bound objective that can be trained end-to-end
efficiently. We compare VCCA and its extensions with previous feature learning
methods on the University of Wisconsin X-ray Microbeam Database, and show that
VCCA-based feature learning improves over previous methods for
speaker-independent phonetic recognition.
Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition
Authors: Gee-Sern, Hsu, Hung-Cheng Shie, Cheng-Hua Hsieh
Date: 2017-08-31 00:00:00
Tasks: 3D Reconstruction, Face Detection, Face Recognition

Two approaches are proposed for cross-pose face recognition, one is based on
the 3D reconstruction of facial components and the other is based on the deep
Convolutional Neural Network (CNN). Unlike most 3D approaches that consider
holistic faces, the proposed approach considers 3D facial components. It
segments a 2D gallery face into components, reconstructs the 3D surface for
each component, and recognizes a probe face by component features. The
segmentation is based on the landmarks located by a hierarchical algorithm that
combines the Faster R-CNN for face detection and the Reduced Tree Structured
Model for landmark localization. The core part of the CNN-based approach is a
revised VGG network. We study the performances with different settings on the
training set, including the synthesized data from 3D reconstruction, the
real-life data from an in-the-wild database, and both types of data combined.
We investigate the performances of the network when it is employed as a
classifier or designed as a feature extractor. The two recognition approaches
and the fast landmark localization are evaluated in extensive experiments, and
compared to stateof-the-art methods to demonstrate their efficacy.
Improved ArtGAN for Conditional Synthesis of Natural Image and Artwork
Authors: Wei Ren Tan, Chee Seng Chan, Hernan Aguirre, Kiyoshi Tanaka
Date: 2017-08-31 00:00:00
Tasks: Conditional Image Generation, Generative Adversarial Network, Image Generation

This paper proposes a series of new approaches to improve Generative
Adversarial Network (GAN) for conditional image synthesis and we name the
proposed model as ArtGAN. One of the key innovation of ArtGAN is that, the
gradient of the loss function w.r.t. the label (randomly assigned to each
generated image) is back-propagated from the categorical discriminator to the
generator. With the feedback from the label information, the generator is able
to learn more efficiently and generate image with better quality. Inspired by
recent works, an autoencoder is incorporated into the categorical discriminator
for additional complementary information. Last but not least, we introduce a
novel strategy to improve the image quality. In the experiments, we evaluate
ArtGAN on CIFAR-10 and STL-10 via ablation studies. The empirical results
showed that our proposed model outperforms the state-of-the-art results on
CIFAR-10 in terms of Inception score. Qualitatively, we demonstrate that ArtGAN
is able to generate plausible-looking images on Oxford-102 and CUB-200, as well
as able to draw realistic artworks based on style, artist, and genre. The
source code and models are available at: https://github.com/cs-chan/ArtGAN
Action Classification and Highlighting in Videos
Authors: Atousa Torabi, Leonid Sigal
Date: 2017-08-31 00:00:00
Tasks: Action Classification, Activity Recognition, Classification, Decoder, General Classification, Human Activity Recognition, Machine Translation, Translation

Inspired by recent advances in neural machine translation, that jointly align
and translate using encoder-decoder networks equipped with attention, we
propose an attentionbased LSTM model for human activity recognition. Our model
jointly learns to classify actions and highlight frames associated with the
action, by attending to salient visual information through a jointly learned
soft-attention networks. We explore attention informed by various forms of
visual semantic features, including those encoding actions, objects and scenes.
We qualitatively show that soft-attention can learn to effectively attend to
important objects and scene information correlated with specific human actions.
Further, we show that, quantitatively, our attention-based LSTM outperforms the
vanilla LSTM and CNN models used by stateof-the-art methods. On a large-scale
youtube video dataset, ActivityNet, our model outperforms competing methods in
action classification.
Leveraging Deep Neural Network Activation Entropy to cope with Unseen Data in Speech Recognition
Authors: Vikramjit Mitra, Horacio Franco
Date: 2017-08-31 00:00:00
Tasks: BIG-bench Machine Learning, speech-recognition, Speech Recognition

Unseen data conditions can inflict serious performance degradation on systems
relying on supervised machine learning algorithms. Because data can often be
unseen, and because traditional machine learning algorithms are trained in a
supervised manner, unsupervised adaptation techniques must be used to adapt the
model to the unseen data conditions. However, unsupervised adaptation is often
challenging, as one must generate some hypothesis given a model and then use
that hypothesis to bootstrap the model to the unseen data conditions.
Unfortunately, reliability of such hypotheses is often poor, given the mismatch
between the training and testing datasets. In such cases, a model hypothesis
confidence measure enables performing data selection for the model adaptation.
Underlying this approach is the fact that for unseen data conditions, data
variability is introduced to the model, which the model propagates to its
output decision, impacting decision reliability. In a fully connected network,
this data variability is propagated as distortions from one layer to the next.
This work aims to estimate the propagation of such distortion in the form of
network activation entropy, which is measured over a short- time running window
on the activation from each neuron of a given hidden layer, and these
measurements are then used to compute summary entropy. This work demonstrates
that such an entropy measure can help to select data for unsupervised model
adaptation, resulting in performance gains in speech recognition tasks. Results
from standard benchmark speech recognition tasks show that the proposed
approach can alleviate the performance degradation experienced under unseen
data conditions by iteratively adapting the model to the unseen datas acoustic
condition.