pwc-archive/papers-with-abstracts huggingface dataset:
Early Methods for Detecting Adversarial Images
Authors: Dan Hendrycks, Kevin Gimpel
Date: 2016-08-01 00:00:00
Tasks: BIG-bench Machine Learning

Many machine learning classifiers are vulnerable to adversarial
perturbations. An adversarial perturbation modifies an input to change a
classifier's prediction without causing the input to seem substantially
different to human perception. We deploy three methods to detect adversarial
images. Adversaries trying to bypass our detectors must make the adversarial
image less pathological or they will fail trying. Our best detection method
reveals that adversarial images place abnormal emphasis on the lower-ranked
principal components from PCA. Other detectors and a colorful saliency map are
in an appendix.
Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics
Authors: Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton
Date: 2016-06-15 00:00:00

While significant progress has been made separately on analytics systems for
scalable stochastic gradient descent (SGD) and private SGD, none of the major
scalable analytics frameworks have incorporated differentially private SGD.
There are two inter-related issues for this disconnect between research and
practice: (1) low model accuracy due to added noise to guarantee privacy, and
(2) high development and runtime overhead of the private algorithms. This paper
takes a first step to remedy this disconnect and proposes a private SGD
algorithm to address \emph{both} issues in an integrated manner. In contrast to
the white-box approach adopted by previous work, we revisit and use the
classical technique of {\em output perturbation} to devise a novel "bolt-on"
approach to private SGD. While our approach trivially addresses (2), it makes
(1) even more challenging. We address this challenge by providing a novel
analysis of the $L_2$-sensitivity of SGD, which allows, under the same privacy
guarantees, better convergence of SGD when only a constant number of passes can
be made over the data. We integrate our algorithm, as well as other
state-of-the-art differentially private SGD, into Bismarck, a popular scalable
SGD-based analytics system on top of an RDBMS. Extensive experiments show that
our algorithm can be easily integrated, incurs virtually no overhead, scales
well, and most importantly, yields substantially better (up to 4X) test
accuracy than the state-of-the-art algorithms on many real datasets.
Recurrent and Contextual Models for Visual Question Answering
Authors: Abhijit Sharang, Eric Lau
Date: 2017-03-23 00:00:00
Tasks: Diversity, Multiple-choice, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We propose a series of recurrent and contextual neural network models for
multiple choice visual question answering on the Visual7W dataset. Motivated by
divergent trends in model complexities in the literature, we explore the
balance between model expressiveness and simplicity by studying incrementally
more complex architectures. We start with LSTM-encoding of input questions and
answers; build on this with context generation by LSTM-encodings of neural
image and question representations and attention over images; and evaluate the
diversity and predictive power of our models and the ensemble thereof. All
models are evaluated against a simple baseline inspired by the current
state-of-the-art, consisting of involving simple concatenation of bag-of-words
and CNN representations for the text and images, respectively. Generally, we
observe marked variation in image-reasoning performance between our models not
obvious from their overall performance, as well as evidence of dataset bias.
Our standalone models achieve accuracies up to $64.6\%$, while the ensemble of
all models achieves the best accuracy of $66.67\%$, within $0.5\%$ of the
current state-of-the-art for Visual7W.
Quality Resilient Deep Neural Networks
Authors: Samuel Dodge, Lina Karam
Date: 2017-03-23 00:00:00
Tasks: Mixture-of-Experts

We study deep neural networks for classification of images with quality
distortions. We first show that networks fine-tuned on distorted data greatly
outperform the original networks when tested on distorted data. However,
fine-tuned networks perform poorly on quality distortions that they have not
been trained for. We propose a mixture of experts ensemble method that is
robust to different types of distortions. The "experts" in our model are
trained on a particular type of distortion. The output of the model is a
weighted sum of the expert models, where the weights are determined by a
separate gating network. The gating network is trained to predict optimal
weights for a particular distortion type and level. During testing, the network
is blind to the distortion level and type, yet can still assign appropriate
weights to the expert models. We additionally investigate weight sharing
methods for the mixture model and show that improved performance can be
achieved with a large reduction in the number of unique network parameters.
A Bag-of-Words Equivalent Recurrent Neural Network for Action Recognition
Authors: Alexander Richard, Juergen Gall
Date: 2017-03-23 00:00:00
Tasks: Action Recognition, General Classification, Quantization, Temporal Action Localization

The traditional bag-of-words approach has found a wide range of applications
in computer vision. The standard pipeline consists of a generation of a visual
vocabulary, a quantization of the features into histograms of visual words, and
a classification step for which usually a support vector machine in combination
with a non-linear kernel is used. Given large amounts of data, however, the
model suffers from a lack of discriminative power. This applies particularly
for action recognition, where the vast amount of video features needs to be
subsampled for unsupervised visual vocabulary generation. Moreover, the kernel
computation can be very expensive on large datasets. In this work, we propose a
recurrent neural network that is equivalent to the traditional bag-of-words
approach but enables for the application of discriminative training. The model
further allows to incorporate the kernel computation into the neural network
directly, solving the complexity issue and allowing to represent the complete
classification system within a single network. We evaluate our method on four
recent action recognition benchmarks and show that the conventional model as
well as sparse coding methods are outperformed.
Multimodal Compact Bilinear Pooling for Multimodal Neural Machine Translation
Authors: Jean-Benoit Delbrouck, Stephane Dupont
Date: 2017-03-23 00:00:00
Tasks: Decoder, Machine Translation, Question Answering, Sentence, Translation, Visual Question Answering, Visual Question Answering (VQA)

In state-of-the-art Neural Machine Translation, an attention mechanism is
used during decoding to enhance the translation. At every step, the decoder
uses this mechanism to focus on different parts of the source sentence to
gather the most useful information before outputting its target word. Recently,
the effectiveness of the attention mechanism has also been explored for
multimodal tasks, where it becomes possible to focus both on sentence parts and
image regions. Approaches to pool two modalities usually include element-wise
product, sum or concatenation. In this paper, we evaluate the more advanced
Multimodal Compact Bilinear pooling method, which takes the outer product of
two vectors to combine the attention features for the two modalities. This has
been previously investigated for visual question answering. We try out this
approach for multimodal image caption translation and show improvements
compared to basic combination methods.
Recurrent Memory Addressing for describing videos
Authors: Arnav Kumar Jain, Abhinav Agarwalla, Kumar Krishna Agrawal, Pabitra Mitra
Date: 2016-11-20 00:00:00
Tasks: Video Captioning

In this paper, we introduce Key-Value Memory Networks to a multimodal setting
and a novel key-addressing mechanism to deal with sequence-to-sequence models.
The proposed model naturally decomposes the problem of video captioning into
vision and language segments, dealing with them as key-value pairs. More
specifically, we learn a semantic embedding (v) corresponding to each frame (k)
in the video, thereby creating (k, v) memory slots. We propose to find the next
step attention weights conditioned on the previous attention distributions for
the key-value memory slots in the memory addressing schema. Exploiting this
flexibility of the framework, we additionally capture spatial dependencies
while mapping from the visual to semantic embedding. Experiments done on the
Youtube2Text dataset demonstrate usefulness of recurrent key-addressing, while
achieving competitive scores on BLEU@4, METEOR metrics against state-of-the-art
models.
Why we have switched from building full-fledged taxonomies to simply detecting hypernymy relations
Authors: Jose Camacho-Collados
Date: 2017-03-12 00:00:00

The study of taxonomies and hypernymy relations has been extensive on the
Natural Language Processing (NLP) literature. However, the evaluation of
taxonomy learning approaches has been traditionally troublesome, as it mainly
relies on ad-hoc experiments which are hardly reproducible and manually
expensive. Partly because of this, current research has been lately focusing on
the hypernymy detection task. In this paper we reflect on this trend, analyzing
issues related to current evaluation procedures. Finally, we propose three
potential avenues for future work so that is-a relations and resources based on
them play a more important role in downstream NLP applications.
Sequential Recurrent Neural Networks for Language Modeling
Authors: Youssef Oualil, Clayton Greenberg, Mittul Singh, Dietrich Klakow
Date: 2017-03-23 00:00:00
Tasks: Language Modeling, Language Modelling, Text Compression

Feedforward Neural Network (FNN)-based language models estimate the
probability of the next word based on the history of the last N words, whereas
Recurrent Neural Networks (RNN) perform the same task based only on the last
word and some context information that cycles in the network. This paper
presents a novel approach, which bridges the gap between these two categories
of networks. In particular, we propose an architecture which takes advantage of
the explicit, sequential enumeration of the word history in FNN structure while
enhancing each word representation at the projection layer through recurrent
context information that evolves in the network. The context integration is
performed using an additional word-dependent weight matrix that is also learned
during the training. Extensive experiments conducted on the Penn Treebank (PTB)
and the Large Text Compression Benchmark (LTCB) corpus showed a significant
reduction of the perplexity when compared to state-of-the-art feedforward as
well as recurrent neural network architectures.
Dynamic Bernoulli Embeddings for Language Evolution
Authors: Maja Rudolph, David Blei
Date: 2017-03-23 00:00:00
Tasks: Word Embeddings

Word embeddings are a powerful approach for unsupervised analysis of
language. Recently, Rudolph et al. (2016) developed exponential family
embeddings, which cast word embeddings in a probabilistic framework. Here, we
develop dynamic embeddings, building on exponential family embeddings to
capture how the meanings of words change over time. We use dynamic embeddings
to analyze three large collections of historical texts: the U.S. Senate
speeches from 1858 to 2009, the history of computer science ACM abstracts from
1951 to 2014, and machine learning papers on the Arxiv from 2007 to 2015. We
find dynamic embeddings provide better fits than classical embeddings and
capture interesting patterns about how language changes.
Resolving the Complexity of Some Fundamental Problems in Computational Social Choice
Authors: Palash Dey
Date: 2017-03-23 00:00:00

This thesis is in the area called computational social choice which is an
intersection area of algorithms and social choice theory.
Generative Adversarial Residual Pairwise Networks for One Shot Learning
Authors: Akshay Mehrotra, Ambedkar Dukkipati
Date: 2017-03-23 00:00:00
Tasks: General Classification, One-Shot Learning

Deep neural networks achieve unprecedented performance levels over many tasks
and scale well with large quantities of data, but performance in the low-data
regime and tasks like one shot learning still lags behind. While recent work
suggests many hypotheses from better optimization to more complicated network
structures, in this work we hypothesize that having a learnable and more
expressive similarity objective is an essential missing component. Towards
overcoming that, we propose a network design inspired by deep residual networks
that allows the efficient computation of this more expressive pairwise
similarity objective. Further, we argue that regularization is key in learning
with small amounts of data, and propose an additional generator network based
on the Generative Adversarial Networks where the discriminator is our residual
pairwise network. This provides a strong regularizer by leveraging the
generated data samples. The proposed model can generate plausible variations of
exemplars over unseen classes and outperforms strong discriminative baselines
for few shot classification tasks. Notably, our residual pairwise network
design outperforms previous state-of-theart on the challenging mini-Imagenet
dataset for one shot learning by getting over 55% accuracy for the 5-way
classification task over unseen classes.
Distribution of Gaussian Process Arc Lengths
Authors: Justin D. Bewsher, Alessandra Tosi, Michael A. Osborne, Stephen J. Roberts
Date: 2017-03-23 00:00:00
Tasks: ARC

We present the first treatment of the arc length of the Gaussian Process (GP)
with more than a single output dimension. GPs are commonly used for tasks such
as trajectory modelling, where path length is a crucial quantity of interest.
Previously, only paths in one dimension have been considered, with no
theoretical consideration of higher dimensional problems. We fill the gap in
the existing literature by deriving the moments of the arc length for a
stationary GP with multiple output dimensions. A new method is used to derive
the mean of a one-dimensional GP over a finite interval, by considering the
distribution of the arc length integrand. This technique is used to derive an
approximate distribution over the arc length of a vector valued GP in
$\mathbb{R}^n$ by moment matching the distribution. Numerical simulations
confirm our theoretical derivations.
Validity of Clusters Produced By kernel-$k$-means With Kernel-Trick
Authors: Mieczysław A. Kłopotek
Date: 2017-01-19 00:00:00
Tasks: Clustering

This paper corrects the proof of the Theorem 2 from the Gower's paper
\cite[page 5]{Gower:1982} as well as corrects the Theorem 7 from Gower's paper
\cite{Gower:1986}. The first correction is needed in order to establish the
existence of the kernel function used commonly in the kernel trick e.g. for
$k$-means clustering algorithm, on the grounds of distance matrix. The
correction encompasses the missing if-part proof and dropping unnecessary
conditions. The second correction deals with transformation of the kernel
matrix into a one embeddable in Euclidean space.
A network of deep neural networks for distant speech recognition
Authors: Mirco Ravanelli, Philemon Brakel, Maurizio Omologo, Yoshua Bengio
Date: 2017-03-23 00:00:00
Tasks: Distant Speech Recognition, Speech Enhancement, speech-recognition, Speech Recognition

Despite the remarkable progress recently made in distant speech recognition,
state-of-the-art technology still suffers from a lack of robustness, especially
when adverse acoustic conditions characterized by non-stationary noises and
reverberation are met. A prominent limitation of current systems lies in the
lack of matching and communication between the various technologies involved in
the distant speech recognition process. The speech enhancement and speech
recognition modules are, for instance, often trained independently. Moreover,
the speech enhancement normally helps the speech recognizer, but the output of
the latter is not commonly used, in turn, to improve the speech enhancement. To
address both concerns, we propose a novel architecture based on a network of
deep neural networks, where all the components are jointly trained and better
cooperate with each other thanks to a full communication scheme between them.
Experiments, conducted using different datasets, tasks and acoustic conditions,
revealed that the proposed framework can overtake other competitive solutions,
including recent joint training approaches.
Nonlinear Spectral Image Fusion
Authors: Martin Benning, Michael Möller, Raz Z. Nossek, Martin Burger, Daniel Cremers, Guy Gilboa, Carola-Bibiane Schönlieb
Date: 2017-03-23 00:00:00
Tasks: Image Manipulation

In this paper we demonstrate that the framework of nonlinear spectral
decompositions based on total variation (TV) regularization is very well suited
for image fusion as well as more general image manipulation tasks. The
well-localized and edge-preserving spectral TV decomposition allows to select
frequencies of a certain image to transfer particular features, such as
wrinkles in a face, from one image to another. We illustrate the effectiveness
of the proposed approach in several numerical experiments, including a
comparison to the competing techniques of Poisson image editing, linear
osmosis, wavelet fusion and Laplacian pyramid fusion. We conclude that the
proposed spectral TV image decomposition framework is a valuable tool for semi-
and fully-automatic image editing and fusion.
Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data
Authors: Salman H. Khan, Munawar Hayat, Mohammed Bennamoun, Ferdous Sohel, Roberto Togneri
Date: 2015-08-14 00:00:00
Tasks: General Classification, image-classification, Image Classification, object-detection, Object Detection

Class imbalance is a common problem in the case of real-world object
detection and classification tasks. Data of some classes is abundant making
them an over-represented majority, and data of other classes is scarce, making
them an under-represented minority. This imbalance makes it challenging for a
classifier to appropriately learn the discriminating boundaries of the majority
and minority classes. In this work, we propose a cost sensitive deep neural
network which can automatically learn robust feature representations for both
the majority and minority classes. During training, our learning procedure
jointly optimizes the class dependent costs and the neural network parameters.
The proposed approach is applicable to both binary and multi-class problems
without any modification. Moreover, as opposed to data level approaches, we do
not alter the original data distribution which results in a lower computational
cost during the training process. We report the results of our experiments on
six major image classification datasets and show that the proposed approach
significantly outperforms the baseline algorithms. Comparisons with popular
data sampling techniques and cost sensitive classifiers demonstrate the
superior performance of our proposed method.
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders
Authors: Fengfu Li, Hong Qiao, Bo Zhang, Xuanyang Xi
Date: 2017-03-23 00:00:00
Tasks: Clustering, Image Clustering

Traditional image clustering methods take a two-step approach, feature
learning and clustering, sequentially. However, recent research results
demonstrated that combining the separated phases in a unified framework and
training them jointly can achieve a better performance. In this paper, we first
introduce fully convolutional auto-encoders for image feature learning and then
propose a unified clustering framework to learn image representations and
cluster centers jointly based on a fully convolutional auto-encoder and soft
$k$-means scores. At initial stages of the learning procedure, the
representations extracted from the auto-encoder may not be very discriminative
for latter clustering. We address this issue by adopting a boosted
discriminative distribution, where high score assignments are highlighted and
low score ones are de-emphasized. With the gradually boosted discrimination,
clustering assignment scores are discriminated and cluster purities are
enlarged. Experiments on several vision benchmark datasets show that our
methods can achieve a state-of-the-art performance.
Using Graphs of Classifiers to Impose Declarative Constraints on Semi-supervised Learning
Authors: Lidong Bing, William W. Cohen, Bhuwan Dhingra
Date: 2017-03-05 00:00:00
Tasks: Bayesian Optimization, Classification, General Classification, Relation, Relation Extraction, text-classification, Text Classification

We propose a general approach to modeling semi-supervised learning (SSL)
algorithms. Specifically, we present a declarative language for modeling both
traditional supervised classification tasks and many SSL heuristics, including
both well-known heuristics such as co-training and novel domain-specific
heuristics. In addition to representing individual SSL heuristics, we show that
multiple heuristics can be automatically combined using Bayesian optimization
methods. We experiment with two classes of tasks, link-based text
classification and relation extraction. We show modest improvements on
well-studied link-based classification benchmarks, and state-of-the-art results
on relation-extraction tasks for two realistic domains.
"Influence Sketching": Finding Influential Samples In Large-Scale Regressions
Authors: Mike Wojnowicz, Ben Cruz, Xuan Zhao, Brian Wallace, Matt Wolff, Jay Luan, Caleb Crable
Date: 2016-11-17 00:00:00
Tasks: Adversarial Attack, Malware Detection

There is an especially strong need in modern large-scale data analysis to
prioritize samples for manual inspection. For example, the inspection could
target important mislabeled samples or key vulnerabilities exploitable by an
adversarial attack. In order to solve the "needle in the haystack" problem of
which samples to inspect, we develop a new scalable version of Cook's distance,
a classical statistical technique for identifying samples which unusually
strongly impact the fit of a regression model (and its downstream predictions).
In order to scale this technique up to very large and high-dimensional
datasets, we introduce a new algorithm which we call "influence sketching."
Influence sketching embeds random projections within the influence computation;
in particular, the influence score is calculated using the randomly projected
pseudo-dataset from the post-convergence Generalized Linear Model (GLM). We
validate that influence sketching can reliably and successfully discover
influential samples by applying the technique to a malware detection dataset of
over 2 million executable files, each represented with almost 100,000 features.
For example, we find that randomly deleting approximately 10% of training
samples reduces predictive accuracy only slightly from 99.47% to 99.45%,
whereas deleting the same number of samples with high influence sketch scores
reduces predictive accuracy all the way down to 90.24%. Moreover, we find that
influential samples are especially likely to be mislabeled. In the case study,
we manually inspect the most influential samples, and find that influence
sketching pointed us to new, previously unidentified pieces of malware.