pwc-archive/papers-with-abstracts huggingface dataset:
Neural Comic Style Transfer: Case Study
Authors: Maciej Pęśko, Tomasz Trzciński
Date: 2018-09-05 00:00:00
Tasks: Style Transfer

The work by Gatys et al. [1] recently showed a neural style algorithm that
can produce an image in the style of another image. Some further works
introduced various improvements regarding generalization, quality and
efficiency, but each of them was mostly focused on styles such as paintings,
abstract images or photo-realistic style. In this paper, we present a
comparison of how state-of-the-art style transfer methods cope with
transferring various comic styles on different images. We select different
combinations of Adaptive Instance Normalization [11] and Universal Style
Transfer [16] models and confront them to find their advantages and
disadvantages in terms of qualitative and quantitative analysis. Finally, we
present the results of a survey conducted on over 100 people that aims at
validating the evaluation results in a real-life application of comic style
transfer.
Bridging machine learning and cryptography in defence against adversarial attacks
Authors: Olga Taran, Shideh Rezaeifar, Slava Voloshynovskiy
Date: 2018-09-05 00:00:00
Tasks: BIG-bench Machine Learning

In the last decade, deep learning algorithms have become very popular thanks
to the achieved performance in many machine learning and computer vision tasks.
However, most of the deep learning architectures are vulnerable to so called
adversarial examples. This questions the security of deep neural networks (DNN)
for many security- and trust-sensitive domains. The majority of the proposed
existing adversarial attacks are based on the differentiability of the DNN cost
function.Defence strategies are mostly based on machine learning and signal
processing principles that either try to detect-reject or filter out the
adversarial perturbations and completely neglect the classical cryptographic
component in the defence. In this work, we propose a new defence mechanism
based on the second Kerckhoffs's cryptographic principle which states that the
defence and classification algorithm are supposed to be known, but not the key.
To be compliant with the assumption that the attacker does not have access to
the secret key, we will primarily focus on a gray-box scenario and do not
address a white-box one. More particularly, we assume that the attacker does
not have direct access to the secret block, but (a) he completely knows the
system architecture, (b) he has access to the data used for training and
testing and (c) he can observe the output of the classifier for each given
input. We show empirically that our system is efficient against most famous
state-of-the-art attacks in black-box and gray-box scenarios.
Coverage-Based Designs Improve Sample Mining and Hyper-Parameter Optimization
Authors: Gowtham Muniraju, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Cihan Tepedelenlioglu, Andreas Spanias
Date: 2018-09-05 00:00:00
Tasks: Bayesian Optimization, Data Summarization

Sampling one or more effective solutions from large search spaces is a
recurring idea in machine learning, and sequential optimization has become a
popular solution. Typical examples include data summarization, sample mining
for predictive modeling and hyper-parameter optimization. Existing solutions
attempt to adaptively trade-off between global exploration and local
exploitation, wherein the initial exploratory sample is critical to their
success. While discrepancy-based samples have become the de facto approach for
exploration, results from computer graphics suggest that coverage-based
designs, e.g. Poisson disk sampling, can be a superior alternative. In order to
successfully adopt coverage-based sample designs to ML applications, which were
originally developed for 2-d image analysis, we propose fundamental advances by
constructing a parameterized family of designs with provably improved coverage
characteristics, and by developing algorithms for effective sample synthesis.
Using experiments in sample mining and hyper-parameter optimization for
supervised learning, we show that our approach consistently outperforms
existing exploratory sampling methods in both blind exploration, and sequential
search with Bayesian optimization.
HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems
Authors: Lucas Vinh Tran, Yi Tay, Shuai Zhang, Gao Cong, Xiao-Li Li
Date: 2018-09-05 00:00:00
Tasks: Collaborative Filtering, Metric Learning, Recommendation Systems, Representation Learning

This paper investigates the notion of learning user and item representations in non-Euclidean space. Specifically, we study the connection between metric learning in hyperbolic space and collaborative filtering by exploring Mobius gyrovector spaces where the formalism of the spaces could be utilized to generalize the most common Euclidean vector operations. Overall, this work aims to bridge the gap between Euclidean and hyperbolic geometry in recommender systems through metric learning approach. We propose HyperML (Hyperbolic Metric Learning), a conceptually simple but highly effective model for boosting the performance. Via a series of extensive experiments, we show that our proposed HyperML not only outperforms their Euclidean counterparts, but also achieves state-of-the-art performance on multiple benchmark datasets, demonstrating the effectiveness of personalized recommendation in hyperbolic geometry.
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing
Authors: Athindran Ramesh Kumar, Balaraman Ravindran, Anand Raghunathan
Date: 2018-09-05 00:00:00
Tasks: GPU, Object, object-detection, Object Detection, Object Tracking, Real-Time Object Detection, Video Object Detection, Video Summarization

Object detection in videos is an important task in computer vision for various applications such as object tracking, video summarization and video search. Although great progress has been made in improving the accuracy of object detection in recent years due to the rise of deep neural networks, the state-of-the-art algorithms are highly computationally intensive. In order to address this challenge, we make two important observations in the context of videos: (i) Objects often occupy only a small fraction of the area in each video frame, and (ii) There is a high likelihood of strong temporal correlation between consecutive frames. Based on these observations, we propose Pack and Detect (PaD), an approach to reduce the computational requirements of object detection in videos. In PaD, only selected video frames called anchor frames are processed at full size. In the frames that lie between anchor frames (inter-anchor frames), regions of interest (ROIs) are identified based on the detections in the previous frame. We propose an algorithm to pack the ROIs of each inter-anchor frame together into a reduced-size frame. The computational requirements of the detector are reduced due to the lower size of the input. In order to maintain the accuracy of object detection, the proposed algorithm expands the ROIs greedily to provide additional background around each object to the detector. PaD can use any underlying neural network architecture to process the full-size and reduced-size frames. Experiments using the ImageNet video object detection dataset indicate that PaD can potentially reduce the number of FLOPS required for a frame by $4\times$. This leads to an overall increase in throughput of $1.25\times$ on a 2.1 GHz Intel Xeon server with a NVIDIA Titan X GPU at the cost of $1.1\%$ drop in accuracy.
TVQA: Localized, Compositional Video Question Answering
Authors: Jie Lei, Licheng Yu, Mohit Bansal, Tamara L. Berg
Date: 2018-09-05 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Video Question Answering

Recent years have witnessed an increasing interest in image-based question-answering (QA) tasks. However, due to data limitations, there has been much less work on video-based QA. In this paper, we present TVQA, a large-scale video QA dataset based on 6 popular TV shows. TVQA consists of 152,545 QA pairs from 21,793 clips, spanning over 460 hours of video. Questions are designed to be compositional in nature, requiring systems to jointly localize relevant moments within a clip, comprehend subtitle-based dialogue, and recognize relevant visual concepts. We provide analyses of this new dataset as well as several baselines and a multi-stream end-to-end trainable neural network framework for the TVQA task. The dataset is publicly available at http://tvqa.cs.unc.edu.
Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction
Authors: Kazuma Hashimoto, Yoshimasa Tsuruoka
Date: 2018-09-05 00:00:00
Proceeding: NAACL 2019 6
Tasks: GPU, Image Captioning, Machine Translation, Prediction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence, Translation

A major obstacle in reinforcement learning-based sentence generation is the
large action space whose size is equal to the vocabulary size of the
target-side language. To improve the efficiency of reinforcement learning, we
present a novel approach for reducing the action space based on dynamic
vocabulary prediction. Our method first predicts a fixed-size small vocabulary
for each input to generate its target sentence. The input-specific vocabularies
are then used at supervised and reinforcement learning steps, and also at test
time. In our experiments on six machine translation and two image captioning
datasets, our method achieves faster reinforcement learning ($\sim$2.7x faster)
with less GPU memory ($\sim$2.3x less) than the full-vocabulary counterpart.
The reinforcement learning with our method consistently leads to significant
improvement of BLEU scores, and the scores are equal to or better than those of
baselines using the full vocabularies, with faster decoding time ($\sim$3x
faster) on CPUs.
Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network
Authors: Vivek Kumar Singh, Hatem A. Rashwan, Santiago Romani, Farhan Akram, Nidhi Pandey, Md. Mostafa Kamal Sarker, Adel Saleh, Meritexell Arenas, Miguel Arquez, Domenec Puig, Jordina Torrents-Barrena
Date: 2018-09-05 00:00:00
Tasks: General Classification, Generative Adversarial Network, Tumor Segmentation

Mammogram inspection in search of breast tumors is a tough assignment that
radiologists must carry out frequently. Therefore, image analysis methods are
needed for the detection and delineation of breast masses, which portray
crucial morphological information that will support reliable diagnosis. In this
paper, we proposed a conditional Generative Adversarial Network (cGAN) devised
to segment a breast mass within a region of interest (ROI) in a mammogram. The
generative network learns to recognize the breast mass area and to create the
binary mask that outlines the breast mass. In turn, the adversarial network
learns to distinguish between real (ground truth) and synthetic segmentations,
thus enforcing the generative network to create binary masks as realistic as
possible. The cGAN works well even when the number of training samples are
limited. Therefore, the proposed method outperforms several state-of-the-art
approaches. This hypothesis is corroborated by diverse experiments performed on
two datasets, the public INbreast and a private in-house dataset. The proposed
segmentation model provides a high Dice coefficient and Intersection over Union
(IoU) of 94% and 87%, respectively. In addition, a shape descriptor based on a
Convolutional Neural Network (CNN) is proposed to classify the generated masks
into four mass shapes: irregular, lobular, oval and round. The proposed shape
descriptor was trained on Digital Database for Screening Mammography (DDSM)
yielding an overall accuracy of 80%, which outperforms the current
state-of-the-art.
Hierarchical Selective Recruitment in Linear-Threshold Brain Networks, Part I: Single-Layer Dynamics and Selective Inhibition
Authors: Erfan Nozari, Jorge Cortés
Date: 2018-09-05 00:00:00

Goal-driven selective attention (GDSA) refers to the brain's function of prioritizing the activity of a task-relevant subset of its overall network to efficiently process relevant information while inhibiting the effects of distractions. Despite decades of research in neuroscience, a comprehensive understanding of GDSA is still lacking. We propose a novel framework using concepts and tools from control theory as well as insights and structures from neuroscience. Central to this framework is an information-processing hierarchy with two main components: selective inhibition of task-irrelevant activity and top-down recruitment of task-relevant activity. We analyze the internal dynamics of each layer of the hierarchy described as a network with linear-threshold dynamics and derive conditions on its structure to guarantee existence and uniqueness of equilibria, asymptotic stability, and boundedness of trajectories. We also provide mechanisms that enforce selective inhibition using the biologically-inspired schemes of feedforward and feedback inhibition. Despite their differences, both lead to the same conclusion: the intrinsic dynamical properties of the (not-inhibited) task-relevant subnetworks are the sole determiner of the dynamical properties that are achievable under selective inhibition.
A Differentially Private Wilcoxon Signed-Rank Test
Authors: Simon Couch, Zeki Kazan, Kaiyan Shi, Andrew Bray, Adam Groce
Date: 2018-09-05 00:00:00

Hypothesis tests are a crucial statistical tool for data mining and are the
workhorse of scientific research in many fields. Here we present a
differentially private analogue of the classic Wilcoxon signed-rank hypothesis
test, which is used when comparing sets of paired (e.g., before-and-after) data
values. We present not only a private estimate of the test statistic, but a
method to accurately compute a p-value and assess statistical significance. We
evaluate our test on both simulated and real data. Compared to the only
existing private test for this situation, that of Task and Clifton, we find
that our test requires less than half as much data to achieve the same
statistical power.
Latent Molecular Optimization for Targeted Therapeutic Design
Authors: Tristan Aumentado-Armstrong
Date: 2018-09-05 00:00:00
Tasks: Drug Discovery

We devise an approach for targeted molecular design, a problem of interest in
computational drug discovery: given a target protein site, we wish to generate
a chemical with both high binding affinity to the target and satisfactory
pharmacological properties. This problem is made difficult by the enormity and
discreteness of the space of potential therapeutics, as well as the
graph-structured nature of biomolecular surface sites. Using a dataset of
protein-ligand complexes, we surmount these issues by extracting a signature of
the target site with a graph convolutional network and by encoding the discrete
chemical into a continuous latent vector space. The latter embedding permits
gradient-based optimization in molecular space, which we perform using learned
differentiable models of binding affinity and other pharmacological properties.
We show that our approach is able to efficiently optimize these multiple
objectives and discover new molecules with potentially useful binding
properties, validated via docking methods.
Recurrent World Models Facilitate Policy Evolution
Authors: David Ha, Jürgen Schmidhuber
Date: 2018-09-04 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A generative recurrent neural network is quickly trained in an unsupervised
manner to model popular reinforcement learning environments through compressed
spatio-temporal representations. The world model's extracted features are fed
into compact and simple policies trained by evolution, achieving state of the
art results in various environments. We also train our agent entirely inside of
an environment generated by its own internal world model, and transfer this
policy back into the actual environment. Interactive version of paper at
https://worldmodels.github.io
Deep Joint Source-Channel Coding for Wireless Image Transmission
Authors: Eirina Bourtsoulatze, David Burth Kurka, Deniz Gunduz
Date: 2018-09-04 00:00:00
Tasks: Decoder

We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols. We parameterize the encoder and decoder functions by two convolutional neural networks (CNNs), which are trained jointly, and can be considered as an autoencoder with a non-trainable layer in the middle that represents the noisy communication channel. Our results show that the proposed deep JSCC scheme outperforms digital transmission concatenating JPEG or JPEG2000 compression with a capacity achieving channel code at low signal-to-noise ratio (SNR) and channel bandwidth values in the presence of additive white Gaussian noise (AWGN). More strikingly, deep JSCC does not suffer from the ``cliff effect'', and it provides a graceful performance degradation as the channel SNR varies with respect to the SNR value assumed during training. In the case of a slow Rayleigh fading channel, deep JSCC learns noise resilient coded representations and significantly outperforms separation-based digital communication at all SNR and channel bandwidth values.
HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition
Authors: Zirui Xu, Fuxun Yu, ChenChen Liu, Xiang Chen
Date: 2018-09-04 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Mobile Security, speech-recognition, Speech Recognition

Nowadays, machine learning based Automatic Speech Recognition (ASR) technique
has widely spread in smartphones, home devices, and public facilities. As
convenient as this technology can be, a considerable security issue also raises
-- the users' speech content might be exposed to malicious ASR monitoring and
cause severe privacy leakage. In this work, we propose HASP -- a
high-performance security enhancement approach to solve this security issue on
mobile devices. Leveraging ASR systems' vulnerability to the adversarial
examples, HASP is designed to cast human imperceptible adversarial noises to
real-time speech and effectively perturb malicious ASR monitoring by increasing
the Word Error Rate (WER). To enhance the practical performance on mobile
devices, HASP is also optimized for effective adaptation to the human speech
characteristics, environmental noises, and mobile computation scenarios. The
experiments show that HASP can achieve optimal real-time security enhancement:
it can lead an average WER of 84.55% for perturbing the malicious ASR
monitoring, and the data processing speed is 15x to 40x faster compared to the
state-of-the-art methods. Moreover, HASP can effectively perturb various ASR
systems, demonstrating a strong transferability.
Three-Stage Speaker Verification Architecture in Emotional Talking Environments
Authors: Ismail Shahin, Ali Bou Nassif
Date: 2018-09-03 00:00:00
Tasks: Speaker Verification

Speaker verification performance in neutral talking environment is usually
high, while it is sharply decreased in emotional talking environments. This
performance degradation in emotional environments is due to the problem of
mismatch between training in neutral environment while testing in emotional
environments. In this work, a three-stage speaker verification architecture has
been proposed to enhance speaker verification performance in emotional
environments. This architecture is comprised of three cascaded stages: gender
identification stage followed by an emotion identification stage followed by a
speaker verification stage. The proposed framework has been evaluated on two
distinct and independent emotional speech datasets: in-house dataset and
Emotional Prosody Speech and Transcripts dataset. Our results show that speaker
verification based on both gender information and emotion information is
superior to each of speaker verification based on gender information only,
emotion information only, and neither gender information nor emotion
information. The attained average speaker verification performance based on the
proposed framework is very alike to that attained in subjective assessment by
human listeners.
Challenges of Context and Time in Reinforcement Learning: Introducing Space Fortress as a Benchmark
Authors: Akshat Agarwal, Ryan Hope, Katia Sycara
Date: 2018-09-06 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL), Sensitivity, Space Fortress

Research in deep reinforcement learning (RL) has coalesced around improving
performance on benchmarks like the Arcade Learning Environment. However, these
benchmarks conspicuously miss important characteristics like abrupt
context-dependent shifts in strategy and temporal sensitivity that are often
present in real-world domains. As a result, RL research has not focused on
these challenges, resulting in algorithms which do not understand critical
changes in context, and have little notion of real world time. To tackle this
issue, this paper introduces the game of Space Fortress as a RL benchmark which
incorporates these characteristics. We show that existing state-of-the-art RL
algorithms are unable to learn to play the Space Fortress game. We then confirm
that this poor performance is due to the RL algorithms' context insensitivity
and reward sparsity. We also identify independent axes along which to vary
context and temporal sensitivity, allowing Space Fortress to be used as a
testbed for understanding both characteristics in combination and also in
isolation. We release Space Fortress as an open-source Gym environment.
Accelerating Deep Neural Networks with Spatial Bottleneck Modules
Authors: Junran Peng, Lingxi Xie, Zhao-Xiang Zhang, Tieniu Tan, Jingdong Wang
Date: 2018-09-07 00:00:00

This paper presents an efficient module named spatial bottleneck for
accelerating the convolutional layers in deep neural networks. The core idea is
to decompose convolution into two stages, which first reduce the spatial
resolution of the feature map, and then restore it to the desired size. This
operation decreases the sampling density in the spatial domain, which is
independent yet complementary to network acceleration approaches in the channel
domain. Using different sampling rates, we can tradeoff between recognition
accuracy and model complexity.
  As a basic building block, spatial bottleneck can be used to replace any
single convolutional layer, or the combination of two convolutional layers. We
empirically verify the effectiveness of spatial bottleneck by applying it to
the deep residual networks. Spatial bottleneck achieves 2x and 1.4x speedup on
the regular and channel-bottlenecked residual blocks, respectively, with the
accuracies retained in recognizing low-resolution images, and even improved in
recognizing high-resolution images.