pwc-archive/papers-with-abstracts huggingface dataset:
Dreaming neural networks: forgetting spurious memories and reinforcing pure ones
Authors: Alberto Fachechi, Elena Agliari, Adriano Barra
Date: 2018-10-29 00:00:00

The standard Hopfield model for associative neural networks accounts for
biological Hebbian learning and acts as the harmonic oscillator for pattern
recognition, however its maximal storage capacity is $\alpha \sim 0.14$, far
from the theoretical bound for symmetric networks, i.e. $\alpha =1$. Inspired
by sleeping and dreaming mechanisms in mammal brains, we propose an extension
of this model displaying the standard on-line (awake) learning mechanism (that
allows the storage of external information in terms of patterns) and an
off-line (sleep) unlearning$\&$consolidating mechanism (that allows
spurious-pattern removal and pure-pattern reinforcement): this obtained daily
prescription is able to saturate the theoretical bound $\alpha=1$, remaining
also extremely robust against thermal noise. Both neural and synaptic features
are analyzed both analytically and numerically. In particular, beyond obtaining
a phase diagram for neural dynamics, we focus on synaptic plasticity and we
give explicit prescriptions on the temporal evolution of the synaptic matrix.
We analytically prove that our algorithm makes the Hebbian kernel converge with
high probability to the projection matrix built over the pure stored patterns.
Furthermore, we obtain a sharp and explicit estimate for the "sleep rate" in
order to ensure such a convergence. Finally, we run extensive numerical
simulations (mainly Monte Carlo sampling) to check the approximations
underlying the analytical investigations (e.g., we developed the whole theory
at the so called replica-symmetric level, as standard in the
Amit-Gutfreund-Sompolinsky reference framework) and possible finite-size
effects, finding overall full agreement with the theory.
ReviewQA: a relational aspect-based opinion reading dataset
Authors: Quentin Grail, Julien Perez
Date: 2018-10-29 00:00:00
Tasks: Question Answering

Deep reading models for question-answering have demonstrated promising
performance over the last couple of years. However current systems tend to
learn how to cleverly extract a span of the source document, based on its
similarity with the question, instead of seeking for the appropriate answer.
Indeed, a reading machine should be able to detect relevant passages in a
document regarding a question, but more importantly, it should be able to
reason over the important pieces of the document in order to produce an answer
when it is required. To motivate this purpose, we present ReviewQA, a
question-answering dataset based on hotel reviews. The questions of this
dataset are linked to a set of relational understanding competencies that we
expect a model to master. Indeed, each question comes with an associated type
that characterizes the required competency. With this framework, it is possible
to benchmark the main families of models and to get an overview of what are the
strengths and the weaknesses of a given model on the set of tasks evaluated in
this dataset. Our corpus contains more than 500.000 questions in natural
language over 100.000 hotel reviews. Our setup is projective, the answer of a
question does not need to be extracted from a document, like in most of the
recent datasets, but selected among a set of candidates that contains all the
possible answers to the questions of the dataset. Finally, we present several
baselines over this dataset.
Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training
Authors: Feng Zheng, Cheng Deng, Xing Sun, Xinyang Jiang, Xiaowei Guo, Zongqiao Yu, Feiyue Huang, Rongrong Ji
Date: 2018-10-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: Person Re-Identification

Most existing Re-IDentification (Re-ID) methods are highly dependent on precise bounding boxes that enable images to be aligned with each other. However, due to the challenging practical scenarios, current detection models often produce inaccurate bounding boxes, which inevitably degenerate the performance of existing Re-ID algorithms. In this paper, we propose a novel coarse-to-fine pyramid model to relax the need of bounding boxes, which not only incorporates local and global information, but also integrates the gradual cues between them. The pyramid model is able to match at different scales and then search for the correct image of the same identity, even when the image pairs are not aligned. In addition, in order to learn discriminative identity representation, we explore a dynamic training scheme to seamlessly unify two losses and extract appropriate shared information between them. Experimental results clearly demonstrate that the proposed method achieves the state-of-the-art results on three datasets. Especially, our approach exceeds the current best method by 9.5% on the most challenging CUHK03 dataset.
Adversarial Attacks on Stochastic Bandits
Authors: Kwang-Sung Jun, Lihong Li, Yuzhe ma, Xiaojin Zhu
Date: 2018-10-29 00:00:00
Proceeding: NeurIPS 2018 12

We study adversarial attacks that manipulate the reward signals to control
the actions chosen by a stochastic multi-armed bandit algorithm. We propose the
first attack against two popular bandit algorithms: $\epsilon$-greedy and UCB,
\emph{without} knowledge of the mean rewards. The attacker is able to spend
only logarithmic effort, multiplied by a problem-specific parameter that
becomes smaller as the bandit problem gets easier to attack. The result means
the attacker can easily hijack the behavior of the bandit algorithm to promote
or obstruct certain actions, say, a particular medical treatment. As bandits
are seeing increasingly wide use in practice, our study exposes a significant
security threat.
End-to-end music source separation: is it possible in the waveform domain?
Authors: Francesc Lluís, Jordi Pons, Xavier Serra
Date: 2018-10-29 00:00:00
Tasks: Deep Learning, Music Source Separation

Most of the currently successful source separation techniques use the magnitude spectrogram as input, and are therefore by default omitting part of the signal: the phase. To avoid omitting potentially useful information, we study the viability of using end-to-end models for music source separation --- which take into account all the information available in the raw audio signal, including the phase. Although during the last decades end-to-end music source separation has been considered almost unattainable, our results confirm that waveform-based models can perform similarly (if not better) than a spectrogram-based deep learning model. Namely: a Wavenet-based model we propose and Wave-U-Net can outperform DeepConvSep, a recent spectrogram-based deep learning model.
DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications
Authors: Nathanaël Perraudin, Michaël Defferrard, Tomasz Kacprzak, Raphael Sgier
Date: 2018-10-29 00:00:00
Tasks: General Classification

Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning
toolbox and have led to many breakthroughs in Artificial Intelligence. These
networks have mostly been developed for regular Euclidean domains such as those
supporting images, audio, or video. Because of their success, CNN-based methods
are becoming increasingly popular in Cosmology. Cosmological data often comes
as spherical maps, which make the use of the traditional CNNs more complicated.
The commonly used pixelization scheme for spherical maps is the Hierarchical
Equal Area isoLatitude Pixelisation (HEALPix). We present a spherical CNN for
analysis of full and partial HEALPix maps, which we call DeepSphere. The
spherical CNN is constructed by representing the sphere as a graph. Graphs are
versatile data structures that can act as a discrete representation of a
continuous manifold. Using the graph-based representation, we define many of
the standard CNN operations, such as convolution and pooling. With filters
restricted to being radial, our convolutions are equivariant to rotation on the
sphere, and DeepSphere can be made invariant or equivariant to rotation. This
way, DeepSphere is a special case of a graph CNN, tailored to the HEALPix
sampling of the sphere. This approach is computationally more efficient than
using spherical harmonics to perform convolutions. We demonstrate the method on
a classification problem of weak lensing mass maps from two cosmological models
and compare the performance of the CNN with that of two baseline classifiers.
The results show that the performance of DeepSphere is always superior or equal
to both of these baselines. For high noise levels and for data covering only a
smaller fraction of the sphere, DeepSphere achieves typically 10% better
classification accuracy than those baselines. Finally, we show how learned
filters can be visualized to introspect the neural network.
Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning
Authors: Ilkay Oksuz, Bram Ruijsink, Esther Puyol-Anton, James Clough, Gastao Cruz, Aurelien Bustin, Claudia Prieto, Rene Botnar, Daniel Rueckert, Julia A. Schnabel, Andrew P. King
Date: 2018-10-29 00:00:00
Tasks: Data Augmentation, General Classification, Image Quality Assessment, imbalanced classification

Good quality of medical images is a prerequisite for the success of
subsequent image analysis pipelines. Quality assessment of medical images is
therefore an essential activity and for large population studies such as the UK
Biobank (UKBB), manual identification of artefacts such as those caused by
unanticipated motion is tedious and time-consuming. Therefore, there is an
urgent need for automatic image quality assessment techniques. In this paper,
we propose a method to automatically detect the presence of motion-related
artefacts in cardiac magnetic resonance (CMR) cine images. We compare two deep
learning architectures to classify poor quality CMR images: 1) 3D
spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent
Convolutional Network (LRCN). Though in real clinical setup motion artefacts
are common, high-quality imaging of UKBB, which comprises cross-sectional
population data of volunteers who do not necessarily have health problems
creates a highly imbalanced classification problem. Due to the high number of
good quality images compared to the relatively low number of images with motion
artefacts, we propose a novel data augmentation scheme based on synthetic
artefact creation in k-space. We also investigate a learning approach using a
predetermined curriculum based on synthetic artefact severity. We evaluate our
pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images.
The LRCN architecture outperformed the 3D-CNN architecture and was able to
detect 2D+time short axis images with motion artefacts in less than 1ms with
high recall. We compare our approach to a range of state-of-the-art quality
assessment methods. The novel data augmentation and curriculum learning
approaches both improved classification performance achieving overall area
under the ROC curve of 0.89.
Variational Calibration of Computer Models
Authors: Sébastien Marmin, Maurizio Filippone
Date: 2018-10-29 00:00:00
Tasks: Gaussian Processes, Variational Inference

Bayesian calibration of black-box computer models offers an established
framework to obtain a posterior distribution over model parameters. Traditional
Bayesian calibration involves the emulation of the computer model and an
additive model discrepancy term using Gaussian processes; inference is then
carried out using MCMC. These choices pose computational and statistical
challenges and limitations, which we overcome by proposing the use of
approximate Deep Gaussian processes and variational inference techniques. The
result is a practical and scalable framework for calibration, which obtains
competitive performance compared to the state-of-the-art.
Semi-unsupervised Learning of Human Activity using Deep Generative Models
Authors: Matthew Willetts, Aiden Doherty, Stephen Roberts, Chris Holmes
Date: 2018-10-29 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis, Transfer Learning, Zero-Shot Learning

We introduce 'semi-unsupervised learning', a problem regime related to
transfer learning and zero-shot learning where, in the training data, some
classes are sparsely labelled and others entirely unlabelled. Models able to
learn from training data of this type are potentially of great use as many
real-world datasets are like this. Here we demonstrate a new deep generative
model for classification in this regime. Our model, a Gaussian mixture deep
generative model, demonstrates superior semi-unsupervised classification
performance on MNIST to model M2 from Kingma and Welling (2014). We apply the
model to human accelerometer data, performing activity classification and
structure discovery on windows of time series data.
Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality
Authors: Lea Müller, Maha Shadaydeh, Martin Thümmel, Thomas Kessler, Dana Schneider, Joachim Denzler
Date: 2018-10-29 00:00:00
Tasks: Causal Inference, Relation, Time Series, Time Series Analysis

Human nonverbal emotional communication in dyadic dialogs is a process of
mutual influence and adaptation. Identifying the direction of influence, or
cause-effect relation between participants is a challenging task, due to two
main obstacles. First, distinct emotions might not be clearly visible. Second,
participants cause-effect relation is transient and variant over time. In this
paper, we address these difficulties by using facial expressions that can be
present even when strong distinct facial emotions are not visible. We also
propose to apply a relevant interval selection approach prior to causal
inference to identify those transient intervals where adaptation process
occurs. To identify the direction of influence, we apply the concept of Granger
causality to the time series of facial expressions on the set of relevant
intervals. We tested our approach on synthetic data and then applied it to
newly, experimentally obtained data. Here, we were able to show that a more
sensitive facial expression detection algorithm and a relevant interval
detection approach is most promising to reveal the cause-effect pattern for
dyadic communication in various instructed interaction conditions.
Contextual Speech Recognition with Difficult Negative Training Examples
Authors: Uri Alon, Golan Pundak, Tara N. Sainath
Date: 2018-10-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Improving the representation of contextual information is key to unlocking
the potential of end-to-end (E2E) automatic speech recognition (ASR). In this
work, we present a novel and simple approach for training an ASR context
mechanism with difficult negative examples. The main idea is to focus on proper
nouns (e.g., unique entities such as names of people and places) in the
reference transcript, and use phonetically similar phrases as negative
examples, encouraging the neural model to learn more discriminative
representations. We apply our approach to an end-to-end contextual ASR model
that jointly learns to transcribe and select the correct context items, and
show that our proposed method gives up to $53.1\%$ relative improvement in word
error rate (WER) across several benchmarks.
Median activation functions for graph neural networks
Authors: Luana Ruiz, Fernando Gama, Antonio G. Marques, Alejandro Ribeiro
Date: 2018-10-29 00:00:00

Graph neural networks (GNNs) have been shown to replicate convolutional
neural networks' (CNNs) superior performance in many problems involving graphs.
By replacing regular convolutions with linear shift-invariant graph filters
(LSI-GFs), GNNs take into account the (irregular) structure of the graph and
provide meaningful representations of network data. However, LSI-GFs fail to
encode local nonlinear graph signal behavior, and so do regular activation
functions, which are nonlinear but pointwise. To address this issue, we propose
median activation functions with support on graph neighborhoods instead of
individual nodes. A GNN architecture with a trainable multirresolution version
of this activation function is then tested on synthetic and real-word datasets,
where we show that median activation functions can improve GNN capacity with
marginal increase in complexity.
Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade
Authors: Tommaso Cavallari, Stuart Golodetz, Nicholas A. Lord, Julien Valentin, Victor A. Prisacariu, Luigi Di Stefano, Philip H. S. Torr
Date: 2018-10-29 00:00:00
Tasks: Camera Pose Estimation, Pose Estimation

Camera pose estimation is an important problem in computer vision. Common techniques either match the current image against keyframes with known poses, directly regress the pose, or establish correspondences between keypoints in the image and points in the scene to estimate the pose. In recent years, regression forests have become a popular alternative to establish such correspondences. They achieve accurate results, but have traditionally needed to be trained offline on the target scene, preventing relocalisation in new environments. Recently, we showed how to circumvent this limitation by adapting a pre-trained forest to a new scene on the fly. The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time. In this paper, we present an extension of this work that achieves significantly better relocalisation performance whilst running fully in real time. To achieve this, we make several changes to the original approach: (i) instead of accepting the camera pose hypothesis without question, we make it possible to score the final few hypotheses using a geometric approach and select the most promising; (ii) we chain several instantiations of our relocaliser together in a cascade, allowing us to try faster but less accurate relocalisation first, only falling back to slower, more accurate relocalisation as necessary; and (iii) we tune the parameters of our cascade to achieve effective overall performance. These changes allow us to significantly improve upon the performance our original state-of-the-art method was able to achieve on the well-known 7-Scenes and Stanford 4 Scenes benchmarks. As additional contributions, we present a way of visualising the internal behaviour of our forests and show how to entirely circumvent the need to pre-train a forest on a generic scene.
Model-Based Active Exploration
Authors: Pranav Shyam, Wojciech Jaśkowski, Faustino Gomez
Date: 2018-10-29 00:00:00
Tasks: Efficient Exploration, model, Reinforcement Learning, Reinforcement Learning (RL)

Efficient exploration is an unsolved problem in Reinforcement Learning which is usually addressed by reactively rewarding the agent for fortuitously encountering novel situations. This paper introduces an efficient active exploration algorithm, Model-Based Active eXploration (MAX), which uses an ensemble of forward models to plan to observe novel events. This is carried out by optimizing agent behaviour with respect to a measure of novelty derived from the Bayesian perspective of exploration, which is estimated using the disagreement between the futures predicted by the ensemble members. We show empirically that in semi-random discrete environments where directed exploration is critical to make progress, MAX is at least an order of magnitude more efficient than strong baselines. MAX scales to high-dimensional continuous environments where it builds task-agnostic models that can be used for any downstream task.
Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models
Authors: Faicel Chamroukhi, Bao-Tuyen Huynh
Date: 2018-10-29 00:00:00
Tasks: Clustering, feature selection, Mixture-of-Experts, parameter estimation, regression

Mixture of Experts (MoE) are successful models for modeling heterogeneous
data in many statistical learning problems including regression, clustering and
classification. Generally fitted by maximum likelihood estimation via the
well-known EM algorithm, their application to high-dimensional problems is
still therefore challenging. We consider the problem of fitting and feature
selection in MoE models, and propose a regularized maximum likelihood
estimation approach that encourages sparse solutions for heterogeneous
regression data models with potentially high-dimensional predictors. Unlike
state-of-the art regularized MLE for MoE, the proposed modelings do not require
an approximate of the penalty function. We develop two hybrid EM algorithms: an
Expectation-Majorization-Maximization (EM/MM) algorithm, and an EM algorithm
with coordinate ascent algorithm. The proposed algorithms allow to
automatically obtaining sparse solutions without thresholding, and avoid matrix
inversion by allowing univariate parameter updates. An experimental study shows
the good performance of the algorithms in terms of recovering the actual sparse
solutions, parameter estimation, and clustering of heterogeneous regression
data.
Recurrent Transformer Networks for Semantic Correspondence
Authors: Seungryong Kim, Stephen Lin, Sangryul Jeon, Dongbo Min, Kwanghoon Sohn
Date: 2018-10-29 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: General Classification, Semantic correspondence

We present recurrent transformer networks (RTNs) for obtaining dense
correspondences between semantically similar images. Our networks accomplish
this through an iterative process of estimating spatial transformations between
the input images and using these transformations to generate aligned
convolutional activations. By directly estimating the transformations between
an image pair, rather than employing spatial transformer networks to
independently normalize each individual image, we show that greater accuracy
can be achieved. This process is conducted in a recursive manner to refine both
the transformation estimates and the feature representations. In addition, a
technique is presented for weakly-supervised training of RTNs that is based on
a proposed classification loss. With RTNs, state-of-the-art performance is
attained on several benchmarks for semantic correspondence.
Low-complexity Recurrent Neural Network-based Polar Decoder with Weight Quantization Mechanism
Authors: Chieh-Fang Teng, Chen-Hsi Wu, Kuan-Shiuan Ho, An-Yeu Wu
Date: 2018-10-29 00:00:00
Tasks: Decoder, Quantization

Polar codes have drawn much attention and been adopted in 5G New Radio (NR)
due to their capacity-achieving performance. Recently, as the emerging deep
learning (DL) technique has breakthrough achievements in many fields, neural
network decoder was proposed to obtain faster convergence and better
performance than belief propagation (BP) decoding. However, neural networks are
memory-intensive and hinder the deployment of DL in communication systems. In
this work, a low-complexity recurrent neural network (RNN) polar decoder with
codebook-based weight quantization is proposed. Our test results show that we
can effectively reduce the memory overhead by 98% and alleviate computational
complexity with slight performance loss.