pwc-archive/papers-with-abstracts huggingface dataset:
VQA with no questions-answers training
Authors: Ben-Zion Vatashsky, Shimon Ullman
Date: 2018-11-20 00:00:00
Proceeding: CVPR 2020 6
Tasks: Visual Question Answering (VQA)

Methods for teaching machines to answer visual questions have made significant progress in recent years, but current methods still lack important human capabilities, including integrating new visual classes and concepts in a modular manner, providing explanations for the answers and handling new domains without explicit examples. We propose a novel method that consists of two main parts: generating a question graph representation, and an answering procedure, guided by the abstract structure of the question graph to invoke an extendable set of visual estimators. Training is performed for the language part and the visual part on their own, but unlike existing schemes, the method does not require any training using images with associated questions and answers. This approach is able to handle novel domains (extended question types and new object classes, properties and relations) as long as corresponding visual estimators are available. In addition, it can provide explanations to its answers and suggest alternatives when questions are not grounded in the image. We demonstrate that this approach achieves both high performance and domain extensibility without any questions-answers training.
Stable Opponent Shaping in Differentiable Games
Authors: Alistair Letcher, Jakob Foerster, David Balduzzi, Tim Rocktäschel, Shimon Whiteson
Date: 2018-11-20 00:00:00
Proceeding: ICLR 2019 5

A growing number of learning methods are actually differentiable games whose players optimise multiple, interdependent objectives in parallel -- from GANs and intrinsic curiosity to multi-agent RL. Opponent shaping is a powerful approach to improve learning dynamics in these games, accounting for player influence on others' updates. Learning with Opponent-Learning Awareness (LOLA) is a recent algorithm that exploits this response and leads to cooperation in settings like the Iterated Prisoner's Dilemma. Although experimentally successful, we show that LOLA agents can exhibit 'arrogant' behaviour directly at odds with convergence. In fact, remarkably few algorithms have theoretical guarantees applying across all (n-player, non-convex) games. In this paper we present Stable Opponent Shaping (SOS), a new method that interpolates between LOLA and a stable variant named LookAhead. We prove that LookAhead converges locally to equilibria and avoids strict saddles in all differentiable games. SOS inherits these essential guarantees, while also shaping the learning of opponents and consistently either matching or outperforming LOLA experimentally.
Double Refinement Network for Efficient Indoor Monocular Depth Estimation
Authors: Nikita Durasov, Mikhail Romanov, Valeriya Bubnova, Pavel Bogomolov, Anton Konushin
Date: 2018-11-20 00:00:00
Tasks: Depth Estimation, Indoor Monocular Depth Estimation, Monocular Depth Estimation

Monocular depth estimation is the task of obtaining a measure of distance for
each pixel using a single image. It is an important problem in computer vision
and is usually solved using neural networks. Though recent works in this area
have shown significant improvement in accuracy, the state-of-the-art methods
tend to require massive amounts of memory and time to process an image. The
main purpose of this work is to improve the performance of the latest solutions
with no decrease in accuracy. To this end, we introduce the Double Refinement
Network architecture. The proposed method achieves state-of-the-art results on
the standard benchmark RGB-D dataset NYU Depth v2, while its frames per second
rate is significantly higher (up to 18 times speedup per image at batch size 1)
and the RAM usage per image is lower.
Fading of collective attention shapes the evolution of linguistic variants
Authors: Diego E Shalom, Mariano Sigman, Gabriel Mindlin, Marcos A Trevisan
Date: 2018-11-20 00:00:00
Tasks: Form, Time Series, Time Series Analysis

Language change involves the competition between alternative linguistic forms
(1). The spontaneous evolution of these forms typically results in monotonic
growths or decays (2, 3) like in winner-take-all attractor behaviors. In the
case of the Spanish past subjunctive, the spontaneous evolution of its two
competing forms (ended in -ra and -se) was perturbed by the appearance of the
Royal Spanish Academy in 1713, which enforced the spelling of both forms as
perfectly interchangeable variants (4), at a moment in which the -ra form was
dominant (5). Time series extracted from a massive corpus of books (6) reveal
that this regulation in fact produced a transient renewed interest for the old
form -se which, once faded, left the -ra again as the dominant form up to the
present day. We show that time series are successfully explained by a
two-dimensional linear model that integrates an imitative and a novelty
component. The model reveals that the temporal scale over which collective
attention fades is in inverse proportion to the verb frequency. The integration
of the two basic mechanisms of imitation and attention to novelty allows to
understand diverse competing objects, with lifetimes that range from hours for
memes and news (7, 8) to decades for verbs, suggesting the existence of a
general mechanism underlying cultural evolution.
Intermediate Level Adversarial Attack for Enhanced Transferability
Authors: Qian Huang, Zeqi Gu, Isay Katsman, Horace He, Pian Pawakapan, Zhiqiu Lin, Serge Belongie, Ser-Nam Lim
Date: 2018-11-20 00:00:00
Tasks: Adversarial Attack

Neural networks are vulnerable to adversarial examples, malicious inputs
crafted to fool trained models. Adversarial examples often exhibit black-box
transfer, meaning that adversarial examples for one model can fool another
model. However, adversarial examples may be overfit to exploit the particular
architecture and feature representation of a source model, resulting in
sub-optimal black-box transfer attacks to other target models. This leads us to
introduce the Intermediate Level Attack (ILA), which attempts to fine-tune an
existing adversarial example for greater black-box transferability by
increasing its perturbation on a pre-specified layer of the source model. We
show that our method can effectively achieve this goal and that we can decide a
nearly-optimal layer of the source model to perturb without any knowledge of
the target models.
Artificial Color Constancy via GoogLeNet with Angular Loss Function
Authors: Oleksii Sidorov
Date: 2018-11-20 00:00:00
Tasks: Color Constancy, Object Recognition, Scene Understanding, Transfer Learning

Color Constancy is the ability of the human visual system to perceive colors unchanged independently of the illumination. Giving a machine this feature will be beneficial in many fields where chromatic information is used. Particularly, it significantly improves scene understanding and object recognition. In this paper, we propose transfer learning-based algorithm, which has two main features: accuracy higher than many state-of-the-art algorithms and simplicity of implementation. Despite the fact that GoogLeNet was used in the experiments, given approach may be applied to any CNN. Additionally, we discuss design of a new loss function oriented specifically to this problem, and propose a few the most suitable options.
Multi-scale aggregation of phase information for reducing computational cost of CNN based DOA estimation
Authors: Soumitro Chakrabarty, Emanuël. A. P. Habets
Date: 2018-11-20 00:00:00

In a recent work on direction-of-arrival (DOA) estimation of multiple
speakers with convolutional neural networks (CNNs), the phase component of
short-time Fourier transform (STFT) coefficients of the microphone signal is
given as input and small filters are used to learn the phase relations between
neighboring microphones. Due to this chosen filter size, $M-1$ convolution
layers are required to achieve the best performance for a microphone array with
M microphones. For arrays with large number of microphones, this requirement
leads to a high computational cost making the method practically infeasible. In
this work, we propose to use systematic dilations of the convolution filters in
each of the convolution layers of the previously proposed CNN for expansion of
the receptive field of the filters to reduce the computational cost of the
method. Different strategies for expansion of the receptive field of the
filters for a specific microphone array are explored. With experimental
analysis of the different strategies, it is shown that an aggressive expansion
strategy results in a considerable reduction in computational cost while a
relatively gradual expansion of the receptive field exhibits the best DOA
estimation performance along with reduction in the computational cost.
Learning Better Features for Face Detection with Feature Fusion and Segmentation Supervision
Authors: Wanxin Tian, Zixuan Wang, Haifeng Shen, Weihong Deng, Yiping Meng, Binghui Chen, Xiubao Zhang, Yuan Zhao, Xiehe Huang
Date: 2018-11-20 00:00:00
Tasks: Face Detection, Segmentation, Semantic Segmentation

The performance of face detectors has been largely improved with the
development of convolutional neural network. However, it remains challenging
for face detectors to detect tiny, occluded or blurry faces. Besides, most face
detectors can't locate face's position precisely and can't achieve high
Intersection-over-Union (IoU) scores. We assume that problems inside are
inadequate use of supervision information and imbalance between semantics and
details at all level feature maps in CNN even with Feature Pyramid Networks
(FPN). In this paper, we present a novel single-shot face detection network,
named DF$^2$S$^2$ (Detection with Feature Fusion and Segmentation Supervision),
which introduces a more effective feature fusion pyramid and a more efficient
segmentation branch on ResNet-50 to handle mentioned problems. Specifically,
inspired by FPN and SENet, we apply semantic information from higher-level
feature maps as contextual cues to augment low-level feature maps via a spatial
and channel-wise attention style, preventing details from being covered by too
much semantics and making semantics and details complement each other. We
further propose a semantic segmentation branch to best utilize detection
supervision information meanwhile applying attention mechanism in a
self-supervised manner. The segmentation branch is supervised by weak
segmentation ground-truth (no extra annotation is required) in a hierarchical
manner, deprecated in the inference time so it wouldn't compromise the
inference speed. We evaluate our model on WIDER FACE dataset and achieved
state-of-art results.
Reinforcement Learning and Inverse Reinforcement Learning with System 1 and System 2
Authors: Alexander Peysakhovich
Date: 2018-11-19 00:00:00
Tasks: Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Inferring a person's goal from their behavior is an important problem in
applications of AI (e.g. automated assistants, recommender systems). The
workhorse model for this task is the rational actor model - this amounts to
assuming that people have stable reward functions, discount the future
exponentially, and construct optimal plans. Under the rational actor assumption
techniques such as inverse reinforcement learning (IRL) can be used to infer a
person's goals from their actions. A competing model is the dual-system model.
Here decisions are the result of an interplay between a fast, automatic,
heuristic-based system 1 and a slower, deliberate, calculating system 2. We
generalize the dual system framework to the case of Markov decision problems
and show how to compute optimal plans for dual-system agents. We show that
dual-system agents exhibit behaviors that are incompatible with rational actor
assumption. We show that naive applications of rational-actor IRL to the
behavior of dual-system agents can generate wrong inference about the agents'
goals and suggest interventions that actually reduce the agent's overall
utility. Finally, we adapt a simple IRL algorithm to correctly infer the goals
of dual system decision-makers. This allows us to make interventions that help,
rather than hinder, the dual-system agent's ability to reach their true goals.
Can Synthetic Faces Undo the Damage of Dataset Bias to Face Recognition and Facial Landmark Detection?
Authors: Adam Kortylewski, Bernhard Egger, Andreas Morel-Forster, Andreas Schneider, Thomas Gerig, Clemens Blumer, Corius Reyneke, Thomas Vetter
Date: 2018-11-19 00:00:00
Tasks: Data Augmentation, Face Model, Face Recognition, Facial Landmark Detection, Transfer Learning

It is well known that deep learning approaches to face recognition and facial landmark detection suffer from biases in modern training datasets. In this work, we propose to use synthetic face images to reduce the negative effects of dataset biases on these tasks. Using a 3D morphable face model, we generate large amounts of synthetic face images with full control over facial shape and color, pose, illumination, and background. With a series of experiments, we extensively test the effects of priming deep nets by pre-training them with synthetic faces. We observe the following positive effects for face recognition and facial landmark detection tasks: 1) Priming with synthetic face images improves the performance consistently across all benchmarks because it reduces the negative effects of biases in the training data. 2) Traditional approaches for reducing the damage of dataset bias, such as data augmentation and transfer learning, are less effective than training with synthetic faces. 3) Using synthetic data, we can reduce the size of real-world datasets by 75% for face recognition and by 50% for facial landmark detection while maintaining performance. Thus, offering a means to focus the data collection process on less but higher quality data.
Model change detection with application to machine learning
Authors: Yuheng Bu, Jiaxun Lu, Venugopal V. Veeravalli
Date: 2018-11-19 00:00:00
Tasks: BIG-bench Machine Learning, Change Detection, model, regression

Model change detection is studied, in which there are two sets of samples
that are independently and identically distributed (i.i.d.) according to a
pre-change probabilistic model with parameter $\theta$, and a post-change model
with parameter $\theta'$, respectively. The goal is to detect whether the
change in the model is significant, i.e., whether the difference between the
pre-change parameter and the post-change parameter $\|\theta-\theta'\|_2$ is
larger than a pre-determined threshold $\rho$. The problem is considered in a
Neyman-Pearson setting, where the goal is to maximize the probability of
detection under a false alarm constraint. Since the generalized likelihood
ratio test (GLRT) is difficult to compute in this problem, we construct an
empirical difference test (EDT), which approximates the GLRT and has low
computational complexity. Moreover, we provide an approximation method to set
the threshold of the EDT to meet the false alarm constraint. Experiments with
linear regression and logistic regression are conducted to validate the
proposed algorithms.
A Variational Prosody Model for the decomposition and synthesis of speech prosody
Authors: Branislav Gerazov, Gérard Bailly, Omar Mohammed, Yi Xu, Philip N. Garner
Date: 2018-06-22 00:00:00
Tasks: Speech Synthesis

The quest for comprehensive generative models of intonation that link
linguistic and paralinguistic functions to prosodic forms has been a
longstanding challenge of speech communication research. More traditional
intonation models have given way to the overwhelming performance of artificial
intelligence (AI) techniques for training model-free, end-to-end mappings using
millions of tunable parameters. The shift towards machine learning models has
nonetheless posed the reverse problem - a compelling need to discover
knowledge, to explain, visualise and interpret. Our work bridges between a
comprehensive generative model of intonation and state-of-the-art AI
techniques. We build upon the modelling paradigm of the Superposition of
Functional Contours model and propose a Variational Prosody Model (VPM) that
uses a network of deep variational contour generators to capture the
context-sensitive variation of the constituent elementary prosodic cliches. We
show that the VPM can give insight into the intrinsic variability of these
prosodic prototypes through learning a meaningful prosodic latent space
representation structure. We also show that the VPM brings improved modelling
performance especially when such variability is prominent. In a speech
synthesis scenario we believe the model can be used to generate a dynamic and
natural prosody contour largely devoid of averaging effects.
Speech Enhancement Based on Reducing the Detail Portion of Speech Spectrograms in Modulation Domain via Discrete Wavelet Transform
Authors: Shih-kuang Lee, Syu-Siang Wang, Yu Tsao, Jeih-weih Hung
Date: 2018-11-08 00:00:00
Tasks: Speech Enhancement

In this paper, we propose a novel speech enhancement (SE) method by
exploiting the discrete wavelet transform (DWT). This new method reduces the
amount of fast time-varying portion, viz. the DWT-wise detail component, in the
spectrogram of speech signals so as to highlight the speech-dominant component
and achieves better speech quality. A particularity of this new method is that
it is completely unsupervised and requires no prior information about the clean
speech and noise in the processed utterance. The presented DWT-based SE method
with various scaling factors for the detail part is evaluated with a subset of
Aurora-2 database, and the PESQ metric is used to indicate the quality of
processed speech signals. The preliminary results show that the processed
speech signals reveal a higher PESQ score in comparison with the original
counterparts. Furthermore, we show that this method can still enhance the
signal by totally discarding the detail part (setting the respective scaling
factor to zero), revealing that the spectrogram can be down-sampled and thus
compressed without the cost of lowered quality. In addition, we integrate this
new method with conventional speech enhancement algorithms, including spectral
subtraction, Wiener filtering, and spectral MMSE estimation, and show that the
resulting integration behaves better than the respective component method. As a
result, this new method is quite effective in improving the speech quality and
well additive to the other SE methods.
A new ANEW: Evaluation of a word list for sentiment analysis in microblogs
Authors: Finn Årup Nielsen
Date: 2011-03-15 00:00:00
Tasks: Sentiment Analysis

Sentiment analysis of microblogs such as Twitter has recently gained a fair
amount of attention. One of the simplest sentiment analysis approaches compares
the words of a posting against a labeled word list, where each word has been
scored for valence, -- a 'sentiment lexicon' or 'affective word lists'. There
exist several affective word lists, e.g., ANEW (Affective Norms for English
Words) developed before the advent of microblogging and sentiment analysis. I
wanted to examine how well ANEW and other word lists performs for the detection
of sentiment strength in microblog posts in comparison with a new word list
specifically constructed for microblogs. I used manually labeled postings from
Twitter scored for sentiment. Using a simple word matching I show that the new
word list may perform better than ANEW, though not as good as the more
elaborate approach found in SentiStrength.
Model-Based Reinforcement Learning for Sepsis Treatment
Authors: Aniruddh Raghu, Matthieu Komorowski, Sumeetpal Singh
Date: 2018-11-23 00:00:00
Tasks: model, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Sepsis is a dangerous condition that is a leading cause of patient mortality.
Treating sepsis is highly challenging, because individual patients respond very
differently to medical interventions and there is no universally agreed-upon
treatment for sepsis. In this work, we explore the use of continuous
state-space model-based reinforcement learning (RL) to discover high-quality
treatment policies for sepsis patients. Our quantitative evaluation reveals
that by blending the treatment strategy discovered with RL with what clinicians
follow, we can obtain improved policies, potentially allowing for better
medical treatment for sepsis.
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses
Authors: Jérôme Rony, Luiz G. Hafemann, Luiz S. Oliveira, Ismail Ben Ayed, Robert Sabourin, Eric Granger
Date: 2018-11-23 00:00:00

Research on adversarial examples in computer vision tasks has shown that
small, often imperceptible changes to an image can induce misclassification,
which has security implications for a wide range of image processing systems.
Considering $L_2$ norm distortions, the Carlini and Wagner attack is presently
the most effective white-box attack in the literature. However, this method is
slow since it performs a line-search for one of the optimization terms, and
often requires thousands of iterations. In this paper, an efficient approach is
proposed to generate gradient-based attacks that induce misclassifications with
low $L_2$ norm, by decoupling the direction and the norm of the adversarial
perturbation that is added to the image. Experiments conducted on the MNIST,
CIFAR-10 and ImageNet datasets indicate that our attack achieves comparable
results to the state-of-the-art (in terms of $L_2$ norm) with considerably
fewer iterations (as few as 100 iterations), which opens the possibility of
using these attacks for adversarial training. Models trained with our attack
achieve state-of-the-art robustness against white-box gradient-based $L_2$
attacks on the MNIST and CIFAR-10 datasets, outperforming the Madry defense
when the attacks are limited to a maximum norm.
Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules
Authors: Boris Knyazev, Xiao Lin, Mohamed R. Amer, Graham W. Taylor
Date: 2018-11-23 00:00:00
Tasks: Classification, General Classification, Graph Classification, Node Classification

Spectral Graph Convolutional Networks (GCNs) are a generalization of
convolutional networks to learning on graph-structured data. Applications of
spectral GCNs have been successful, but limited to a few problems where the
graph is fixed, such as shape correspondence and node classification. In this
work, we address this limitation by revisiting a particular family of spectral
graph networks, Chebyshev GCNs, showing its efficacy in solving graph
classification tasks with a variable graph structure and size. Chebyshev GCNs
restrict graphs to have at most one edge between any pair of nodes. To this
end, we propose a novel multigraph network that learns from multi-relational
graphs. We model learned edges with abstract meaning and experiment with
different ways to fuse the representations extracted from annotated and learned
edges, achieving competitive results on a variety of chemical classification
benchmarks.