pwc-archive/papers-with-abstracts huggingface dataset:
ISEC: Iterative over-Segmentation via Edge Clustering
Authors: Marcelo Santos, Luciano Oliveira
Date: 2018-02-16 00:00:00
Tasks: Clustering, Segmentation, Superpixels, Video Segmentation, Video Semantic Segmentation

Several image pattern recognition tasks rely on superpixel generation as a
fundamental step. Image analysis based on superpixels facilitates
domain-specific applications, also speeding up the overall processing time of
the task. Recent superpixel methods have been designed to fit boundary
adherence, usually regulating the size and shape of each superpixel in order to
mitigate the occurrence of undersegmentation failures. Superpixel regularity
and compactness sometimes imposes an excessive number of segments in the image,
which ultimately decreases the efficiency of the final segmentation, specially
in video segmentation. We propose here a novel method to generate superpixels,
called iterative over-segmentation via edge clustering (ISEC), which addresses
the over-segmentation problem from a different perspective in contrast to
recent state-of-the-art approaches. ISEC iteratively clusters edges extracted
from the image objects, providing adaptive superpixels in size, shape and
quantity, while preserving suitable adherence to the real object boundaries.
All this is achieved at a very low computational cost. Experiments show that
ISEC stands out from existing methods, meeting a favorable balance between
segmentation stability and accurate representation of motion discontinuities,
which are features specially suitable to video segmentation.
High efficiency compression for object detection
Authors: Hyomin Choi, Ivan V. Bajic
Date: 2017-10-30 00:00:00
Tasks: Object, object-detection, Object Detection, Video Compression, Vocal Bursts Intensity Prediction

Image and video compression has traditionally been tailored to human vision.
However, modern applications such as visual analytics and surveillance rely on
computers seeing and analyzing the images before (or instead of) humans. For
these applications, it is important to adjust compression to computer vision.
In this paper we present a bit allocation and rate control strategy that is
tailored to object detection. Using the initial convolutional layers of a
state-of-the-art object detector, we create an importance map that can guide
bit allocation to areas that are important for object detection. The proposed
method enables bit rate savings of 7% or more compared to default HEVC, at the
equivalent object detection rate.
DESlib: A Dynamic ensemble selection library in Python
Authors: Rafael M. O. Cruz, Luiz G. Hafemann, Robert Sabourin, George D. C. Cavalcanti
Date: 2018-02-14 00:00:00

DESlib is an open-source python library providing the implementation of
several dynamic selection techniques. The library is divided into three
modules: (i) \emph{dcs}, containing the implementation of dynamic classifier
selection methods (DCS); (ii) \emph{des}, containing the implementation of
dynamic ensemble selection methods (DES); (iii) \emph{static}, with the
implementation of static ensemble techniques. The library is fully documented
(documentation available online on Read the Docs), has a high test coverage
(codecov.io) and is part of the scikit-learn-contrib supported projects.
Documentation, code and examples can be found on its GitHub page:
https://github.com/scikit-learn-contrib/DESlib.
Variational Autoencoders for Collaborative Filtering
Authors: Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara
Date: 2018-02-16 00:00:00
Tasks: Bayesian Inference, Collaborative Filtering, Language Modeling, Language Modelling, parameter estimation, Recommendation Systems

We extend variational autoencoders (VAEs) to collaborative filtering for
implicit feedback. This non-linear probabilistic model enables us to go beyond
the limited modeling capacity of linear factor models which still largely
dominate collaborative filtering research.We introduce a generative model with
multinomial likelihood and use Bayesian inference for parameter estimation.
Despite widespread use in language modeling and economics, the multinomial
likelihood receives less attention in the recommender systems literature. We
introduce a different regularization parameter for the learning objective,
which proves to be crucial for achieving competitive performance. Remarkably,
there is an efficient way to tune the parameter using annealing. The resulting
model and learning algorithm has information-theoretic connections to maximum
entropy discrimination and the information bottleneck principle. Empirically,
we show that the proposed approach significantly outperforms several
state-of-the-art baselines, including two recently-proposed neural network
approaches, on several real-world datasets. We also provide extended
experiments comparing the multinomial likelihood with other commonly used
likelihood functions in the latent factor collaborative filtering literature
and show favorable results. Finally, we identify the pros and cons of employing
a principled Bayesian inference approach and characterize settings where it
provides the most significant improvements.
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
Authors: Jonathan Shen, Ruoming Pang, Ron J. Weiss, Mike Schuster, Navdeep Jaitly, Zongheng Yang, Zhifeng Chen, Yu Zhang, Yuxuan Wang, RJ Skerry-Ryan, Rif A. Saurous, Yannis Agiomyrgiannakis, Yonghui Wu
Date: 2017-12-16 00:00:00
Tasks: Speech Synthesis

This paper describes Tacotron 2, a neural network architecture for speech
synthesis directly from text. The system is composed of a recurrent
sequence-to-sequence feature prediction network that maps character embeddings
to mel-scale spectrograms, followed by a modified WaveNet model acting as a
vocoder to synthesize timedomain waveforms from those spectrograms. Our model
achieves a mean opinion score (MOS) of $4.53$ comparable to a MOS of $4.58$ for
professionally recorded speech. To validate our design choices, we present
ablation studies of key components of our system and evaluate the impact of
using mel spectrograms as the input to WaveNet instead of linguistic, duration,
and $F_0$ features. We further demonstrate that using a compact acoustic
intermediate representation enables significant simplification of the WaveNet
architecture.
Second-Order Optimization for Non-Convex Machine Learning: An Empirical Study
Authors: Peng Xu, Farbod Roosta-Khorasani, Michael W. Mahoney
Date: 2017-08-25 00:00:00
Tasks: ARC, BIG-bench Machine Learning, Second-order methods

While first-order optimization methods such as stochastic gradient descent
(SGD) are popular in machine learning (ML), they come with well-known
deficiencies, including relatively-slow convergence, sensitivity to the
settings of hyper-parameters such as learning rate, stagnation at high training
errors, and difficulty in escaping flat regions and saddle points. These issues
are particularly acute in highly non-convex settings such as those arising in
neural networks. Motivated by this, there has been recent interest in
second-order methods that aim to alleviate these shortcomings by capturing
curvature information. In this paper, we report detailed empirical evaluations
of a class of Newton-type methods, namely sub-sampled variants of trust region
(TR) and adaptive regularization with cubics (ARC) algorithms, for non-convex
ML problems. In doing so, we demonstrate that these methods not only can be
computationally competitive with hand-tuned SGD with momentum, obtaining
comparable or better generalization performance, but also they are highly
robust to hyper-parameter settings. Further, in contrast to SGD with momentum,
we show that the manner in which these Newton-type methods employ curvature
information allows them to seamlessly escape flat regions and saddle points.
Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information
Authors: Peng Xu, Fred Roosta, Michael W. Mahoney
Date: 2017-08-23 00:00:00
Tasks: Vocal Bursts Type Prediction

We consider variants of trust-region and cubic regularization methods for non-convex optimization, in which the Hessian matrix is approximated. Under mild conditions on the inexact Hessian, and using approximate solution of the corresponding sub-problems, we provide iteration complexity to achieve $ \epsilon $-approximate second-order optimality which have shown to be tight. Our Hessian approximation conditions constitute a major relaxation over the existing ones in the literature. Consequently, we are able to show that such mild conditions allow for the construction of the approximate Hessian through various random sampling methods. In this light, we consider the canonical problem of finite-sum minimization, provide appropriate uniform and non-uniform sub-sampling strategies to construct such Hessian approximations, and obtain optimal iteration complexity for the corresponding sub-sampled trust-region and cubic regularization methods.
Conditional Accelerated Lazy Stochastic Gradient Descent
Authors: Guanghui Lan, Sebastian Pokutta, Yi Zhou, Daniel Zink
Date: 2017-03-16 00:00:00
Proceeding: ICML 2017 8

In this work we introduce a conditional accelerated lazy stochastic gradient
descent algorithm with optimal number of calls to a stochastic first-order
oracle and convergence rate $O\left(\frac{1}{\varepsilon^2}\right)$ improving
over the projection-free, Online Frank-Wolfe based stochastic gradient descent
of Hazan and Kale [2012] with convergence rate
$O\left(\frac{1}{\varepsilon^4}\right)$.
Detecting Anomalous Faces with 'No Peeking' Autoencoders
Authors: Anand Bhattad, Jason Rock, David Forsyth
Date: 2018-02-15 00:00:00

Detecting anomalous faces has important applications. For example, a system
might tell when a train driver is incapacitated by a medical event, and assist
in adopting a safe recovery strategy. These applications are demanding, because
they require accurate detection of rare anomalies that may be seen only at
runtime. Such a setting causes supervised methods to perform poorly. We
describe a method for detecting an anomalous face image that meets these
requirements. We construct a feature vector that reliably has large entries for
anomalous images, then use various simple unsupervised methods to score the
image based on the feature. Obvious constructions (autoencoder codes;
autoencoder residuals) are defeated by a 'peeking' behavior in autoencoders.
Our feature construction removes rectangular patches from the image, predicts
the likely content of the patch conditioned on the rest of the image using a
specially trained autoencoder, then compares the result to the image. High
scores suggest that the patch was difficult for an autoencoder to predict, and
so is likely anomalous. We demonstrate that our method can identify real
anomalous face images in pools of typical images, taken from celeb-A, that is
much larger than usual in state-of-the-art experiments. A control experiment
based on our method with another set of normal celebrity images - a 'typical
set', but nonceleb-A are not identified as anomalous; confirms this is not due
to special properties of celeb-A.
Masked Conditional Neural Networks for Automatic Sound Events Recognition
Authors: Fady Medhat, David Chesmore, John Robinson
Date: 2018-02-15 00:00:00

Deep neural network architectures designed for application domains other than
sound, especially image recognition, may not optimally harness the
time-frequency representation when adapted to the sound recognition problem. In
this work, we explore the ConditionaL Neural Network (CLNN) and the Masked
ConditionaL Neural Network (MCLNN) for multi-dimensional temporal signal
recognition. The CLNN considers the inter-frame relationship, and the MCLNN
enforces a systematic sparseness over the network's links to enable learning in
frequency bands rather than bins allowing the network to be frequency shift
invariant mimicking a filterbank. The mask also allows considering several
combinations of features concurrently, which is usually handcrafted through
exhaustive manual search. We applied the MCLNN to the environmental sound
recognition problem using the ESC-10 and ESC-50 datasets. MCLNN achieved
competitive performance, using 12% of the parameters and without augmentation,
compared to state-of-the-art Convolutional Neural Networks.
Truth Validation with Evidence
Authors: Papis Wongchaisuwat, Diego Klabjan
Date: 2018-02-15 00:00:00
Tasks: valid

In the modern era, abundant information is easily accessible from various
sources, however only a few of these sources are reliable as they mostly
contain unverified contents. We develop a system to validate the truthfulness
of a given statement together with underlying evidence. The proposed system
provides supporting evidence when the statement is tagged as false. Our work
relies on an inference method on a knowledge graph (KG) to identify the
truthfulness of statements. In order to extract the evidence of falseness, the
proposed algorithm takes into account combined knowledge from KG and
ontologies. The system shows very good results as it provides valid and concise
evidence. The quality of KG plays a role in the performance of the inference
method which explicitly affects the performance of our evidence-extracting
algorithm.
Quantum Variational Autoencoder
Authors: Amir Khoshaman, Walter Vinci, Brandon Denis, Evgeny Andriyash, Hossein Sadeghi, Mohammad H. Amin
Date: 2018-02-15 00:00:00

Variational autoencoders (VAEs) are powerful generative models with the
salient ability to perform inference. Here, we introduce a quantum variational
autoencoder (QVAE): a VAE whose latent generative process is implemented as a
quantum Boltzmann machine (QBM). We show that our model can be trained
end-to-end by maximizing a well-defined loss-function: a 'quantum' lower-bound
to a variational approximation of the log-likelihood. We use quantum Monte
Carlo (QMC) simulations to train and evaluate the performance of QVAEs. To
achieve the best performance, we first create a VAE platform with discrete
latent space generated by a restricted Boltzmann machine (RBM). Our model
achieves state-of-the-art performance on the MNIST dataset when compared
against similar approaches that only involve discrete variables in the
generative process. We consider QVAEs with a smaller number of latent units to
be able to perform QMC simulations, which are computationally expensive. We
show that QVAEs can be trained effectively in regimes where quantum effects are
relevant despite training via the quantum bound. Our findings open the way to
the use of quantum computers to train QVAEs to achieve competitive performance
for generative models. Placing a QBM in the latent space of a VAE leverages the
full potential of current and next-generation quantum computers as sampling
devices.
Learning to Count Objects in Natural Images for Visual Question Answering
Authors: Yan Zhang, Jonathon Hare, Adam Prügel-Bennett
Date: 2018-02-15 00:00:00
Proceeding: ICLR 2018 1
Tasks: Visual Question Answering, Visual Question Answering (VQA)

Visual Question Answering (VQA) models have struggled with counting objects
in natural images so far. We identify a fundamental problem due to soft
attention in these models as a cause. To circumvent this problem, we propose a
neural network component that allows robust counting from object proposals.
Experiments on a toy task show the effectiveness of this component and we
obtain state-of-the-art accuracy on the number category of the VQA v2 dataset
without negatively affecting other categories, even outperforming ensemble
models with our single model. On a difficult balanced pair metric, the
component gives a substantial improvement in counting over a strong baseline by
6.6%.
Self-Regulating Artificial General Intelligence
Authors: Joshua S. Gans
Date: 2017-11-12 00:00:00

Here we examine the paperclip apocalypse concern for artificial general
intelligence (or AGI) whereby a superintelligent AI with a simple goal (ie.,
producing paperclips) accumulates power so that all resources are devoted
towards that simple goal and are unavailable for any other use. We provide
conditions under which a paper apocalypse can arise but also show that, under
certain architectures for recursive self-improvement of AIs, that a paperclip
AI may refrain from allowing power capabilities to be developed. The reason is
that such developments pose the same control problem for the AI as they do for
humans (over AIs) and hence, threaten to deprive it of resources for its
primary goal.
Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks
Authors: Christian Stab, Tristan Miller, Iryna Gurevych
Date: 2018-02-15 00:00:00
Tasks: Argument Mining

Argument mining is a core technology for automating argument search in large
document collections. Despite its usefulness for this task, most current
approaches to argument mining are designed for use only with specific text
types and fall short when applied to heterogeneous texts. In this paper, we
propose a new sentential annotation scheme that is reliably applicable by crowd
workers to arbitrary Web texts. We source annotations for over 25,000 instances
covering eight controversial topics. The results of cross-topic experiments
show that our attention-based neural network generalizes best to unseen topics
and outperforms vanilla BiLSTM models by 6% in accuracy and 11% in F-score.
Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers
Authors: Ishai Rosenberg, Asaf Shabtai, Lior Rokach, Yuval Elovici
Date: 2017-07-19 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we present a black-box attack against API call based machine
learning malware classifiers, focusing on generating adversarial sequences
combining API calls and static features (e.g., printable strings) that will be
misclassified by the classifier without affecting the malware functionality. We
show that this attack is effective against many classifiers due to the
transferability principle between RNN variants, feed forward DNNs, and
traditional machine learning classifiers such as SVM. We also implement GADGET,
a software framework to convert any malware binary to a binary undetected by
malware classifiers, using the proposed attack, without access to the malware
source code.
Graph-Guided Banding of the Covariance Matrix
Authors: Jacob Bien
Date: 2016-06-01 00:00:00

Regularization has become a primary tool for developing reliable estimators
of the covariance matrix in high-dimensional settings. To curb the curse of
dimensionality, numerous methods assume that the population covariance (or
inverse covariance) matrix is sparse, while making no particular structural
assumptions on the desired pattern of sparsity. A highly-related, yet
complementary, literature studies the specific setting in which the measured
variables have a known ordering, in which case a banded population matrix is
often assumed. While the banded approach is conceptually and computationally
easier than asking for "patternless sparsity," it is only applicable in very
specific situations (such as when data are measured over time or
one-dimensional space). This work proposes a generalization of the notion of
bandedness that greatly expands the range of problems in which banded
estimators apply.
  We develop convex regularizers occupying the broad middle ground between the
former approach of "patternless sparsity" and the latter reliance on having a
known ordering. Our framework defines bandedness with respect to a known graph
on the measured variables. Such a graph is available in diverse situations, and
we provide a theoretical, computational, and applied treatment of two new
estimators. An R package, called ggb, implements these new methods.
Mixed Precision Training
Authors: Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, Hao Wu
Date: 2017-10-10 00:00:00
Proceeding: ICLR 2018 1

Deep neural networks have enabled progress in a wide variety of applications.
Growing the size of the neural network typically results in improved accuracy.
As model sizes grow, the memory and compute requirements for training these
models also increases. We introduce a technique to train deep neural networks
using half precision floating point numbers. In our technique, weights,
activations and gradients are stored in IEEE half-precision format.
Half-precision floating numbers have limited numerical range compared to
single-precision numbers. We propose two techniques to handle this loss of
information. Firstly, we recommend maintaining a single-precision copy of the
weights that accumulates the gradients after each optimizer step. This
single-precision copy is rounded to half-precision format during training.
Secondly, we propose scaling the loss appropriately to handle the loss of
information with half-precision gradients. We demonstrate that this approach
works for a wide variety of models including convolution neural networks,
recurrent neural networks and generative adversarial networks. This technique
works for large scale models with more than 100 million parameters trained on
large datasets. Using this approach, we can reduce the memory consumption of
deep learning models by nearly 2x. In future processors, we can also expect a
significant computation speedup using half-precision hardware units.
JU_KS@SAIL_CodeMixed-2017: Sentiment Analysis for Indian Code Mixed Social Media Texts
Authors: Kamal Sarkar
Date: 2018-02-15 00:00:00
Tasks: Position, Sentiment Analysis

This paper reports about our work in the NLP Tool Contest @ICON-2017, shared
task on Sentiment Analysis for Indian Languages (SAIL) (code mixed). To
implement our system, we have used a machine learning algo-rithm called
Multinomial Na\"ive Bayes trained using n-gram and SentiWordnet features. We
have also used a small SentiWordnet for English and a small SentiWordnet for
Bengali. But we have not used any SentiWordnet for Hindi language. We have
tested our system on Hindi-English and Bengali-English code mixed social media
data sets released for the contest. The performance of our system is very close
to the best system participated in the contest. For both Bengali-English and
Hindi-English runs, our system was ranked at the 3rd position out of all
submitted runs and awarded the 3rd prize in the contest.
Fair Clustering Through Fairlets
Authors: Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii
Date: 2018-02-15 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Clustering

We study the question of fair clustering under the {\em disparate impact}
doctrine, where each protected class must have approximately equal
representation in every cluster. We formulate the fair clustering problem under
both the $k$-center and the $k$-median objectives, and show that even with two
protected classes the problem is challenging, as the optimum solution can
violate common conventions---for instance a point may no longer be assigned to
its nearest cluster center! En route we introduce the concept of fairlets,
which are minimal sets that satisfy fair representation while approximately
preserving the clustering objective. We show that any fair clustering problem
can be decomposed into first finding good fairlets, and then using existing
machinery for traditional clustering algorithms. While finding good fairlets
can be NP-hard, we proceed to obtain efficient approximation algorithms based
on minimum cost flow. We empirically quantify the value of fair clustering on
real-world datasets with sensitive attributes.