pwc-archive/papers-with-abstracts huggingface dataset:
Higher Order of Motion Magnification for Vessel Localisation in Surgical Video
Authors: Mirek Janatka, Ashwin Sridhar, John Kelly, Danail Stoyanov
Date: 2018-06-13 00:00:00
Tasks: Motion Magnification, SSIM

Locating vessels during surgery is critical for avoiding inadvertent damage,
yet vasculature can be difficult to identify. Video motion magnification can
potentially highlight vessels by exaggerating subtle motion embedded within the
video to become perceivable to the surgeon. In this paper, we explore a
physiological model of artery distension to extend motion magnification to
incorporate higher orders of motion, leveraging the difference in acceleration
over time (jerk) in pulsatile motion to highlight the vascular pulse wave. Our
method is compared to first and second order motion based Eulerian video
magnification algorithms. Using data from a surgical video retrieved during a
robotic prostatectomy, we show that our method can accentuate
cardio-physiological features and produce a more succinct and clearer video for
motion magnification, with more similarities in areas without motion to the
source video at large magnifications. We validate the approach with a Structure
Similarity (SSIM) and Peak Signal to Noise Ratio (PSNR) assessment of three
videos at an increasing working distance, using three different levels of
optical magnification. Spatio-temporal cross sections are presented to show the
effectiveness of our proposal and video samples are provided to demonstrates
qualitatively our results.
Towards Semantically Enhanced Data Understanding
Authors: Markus Schröder, Christian Jilek, Jörn Hees, Andreas Dengel
Date: 2018-06-13 00:00:00

In the field of machine learning, data understanding is the practice of
getting initial insights in unknown datasets. Such knowledge-intensive tasks
require a lot of documentation, which is necessary for data scientists to grasp
the meaning of the data. Usually, documentation is separate from the data in
various external documents, diagrams, spreadsheets and tools which causes
considerable look up overhead. Moreover, other supporting applications are not
able to consume and utilize such unstructured data. That is why we propose a
methodology that uses a single semantic model that interlinks data with its
documentation. Hence, data scientists are able to directly look up the
connected information about the data by simply following links. Equally, they
can browse the documentation which always refers to the data. Furthermore, the
model can be used by other approaches providing additional support, like
searching, comparing, integrating or visualizing data. To showcase our approach
we also demonstrate an early prototype.
Diachronic word embeddings and semantic shifts: a survey
Authors: Andrey Kutuzov, Lilja Øvrelid, Terrence Szymanski, Erik Velldal
Date: 2018-06-09 00:00:00
Proceeding: COLING 2018 8
Tasks: Diachronic Word Embeddings, Survey, Word Embeddings

Recent years have witnessed a surge of publications aimed at tracing temporal
changes in lexical semantics using distributional methods, particularly
prediction-based word embedding models. However, this vein of research lacks
the cohesion, common terminology and shared practices of more established areas
of natural language processing. In this paper, we survey the current state of
academic research related to diachronic word embeddings and semantic shifts
detection. We start with discussing the notion of semantic shifts, and then
continue with an overview of the existing methods for tracing such time-related
shifts with word embedding models. We propose several axes along which these
methods can be compared, and outline the main challenges before this emerging
subfield of NLP, as well as prospects and possible applications.
fMRI Semantic Category Decoding using Linguistic Encoding of Word Embeddings
Authors: Subba Reddy Oota, Naresh Manwani, Bapi Raju S
Date: 2018-06-13 00:00:00
Tasks: Word Embeddings

The dispute of how the human brain represents conceptual knowledge has been
argued in many scientific fields. Brain imaging studies have shown that the
spatial patterns of neural activation in the brain are correlated with thinking
about different semantic categories of words (for example, tools, animals, and
buildings) or when viewing the related pictures. In this paper, we present a
computational model that learns to predict the neural activation captured in
functional magnetic resonance imaging (fMRI) data of test words. Unlike the
models with hand-crafted features that have been used in the literature, in
this paper we propose a novel approach wherein decoding models are built with
features extracted from popular linguistic encodings of Word2Vec, GloVe,
Meta-Embeddings in conjunction with the empirical fMRI data associated with
viewing several dozen concrete nouns. We compared these models with several
other models that use word features extracted from FastText, Randomly-generated
features, Mitchell's 25 features [1]. The experimental results show that the
predicted fMRI images using Meta-Embeddings meet the state-of-the-art
performance. Although models with features from GloVe and Word2Vec predict fMRI
images similar to the state-of-the-art model, model with features from
Meta-Embeddings predicts significantly better. The proposed scheme that uses
popular linguistic encoding offers a simple and easy approach for semantic
decoding from fMRI experiments.
Convolutional Sparse Coding for High Dynamic Range Imaging
Authors: Ana Serrano, Felix Heide, Diego Gutierrez, Gordon Wetzstein, Belen Masia
Date: 2018-06-13 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Current HDR acquisition techniques are based on either (i) fusing
multibracketed, low dynamic range (LDR) images, (ii) modifying existing
hardware and capturing different exposures simultaneously with multiple
sensors, or (iii) reconstructing a single image with spatially-varying pixel
exposures. In this paper, we propose a novel algorithm to recover high-quality
HDRI images from a single, coded exposure. The proposed reconstruction method
builds on recently-introduced ideas of convolutional sparse coding (CSC); this
paper demonstrates how to make CSC practical for HDR imaging. We demonstrate
that the proposed algorithm achieves higher-quality reconstructions than
alternative methods, we evaluate optical coding schemes, analyze algorithmic
parameters, and build a prototype coded HDR camera that demonstrates the
utility of convolutional sparse HDRI coding with a custom hardware platform.
Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning
Authors: Luca Franceschi, Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo, Paolo Frasconi
Date: 2018-06-13 00:00:00
Tasks: Hyperparameter Optimization, Meta-Learning

In (Franceschi et al., 2018) we proposed a unified mathematical framework,
grounded on bilevel programming, that encompasses gradient-based hyperparameter
optimization and meta-learning. We formulated an approximate version of the
problem where the inner objective is solved iteratively, and gave sufficient
conditions ensuring convergence to the exact problem. In this work we show how
to optimize learning rates, automatically weight the loss of single examples
and learn hyper-representations with Far-HO, a software package based on the
popular deep learning framework TensorFlow that allows to seamlessly tackle
both HO and ML problems.
Convolutional sparse coding for capturing high speed video content
Authors: Ana Serrano, Elena Garces, Diego Gutierrez, Belen Masia
Date: 2018-06-13 00:00:00
Tasks: Compressive Sensing, Vocal Bursts Intensity Prediction

Video capture is limited by the trade-off between spatial and temporal
resolution: when capturing videos of high temporal resolution, the spatial
resolution decreases due to bandwidth limitations in the capture system.
Achieving both high spatial and temporal resolution is only possible with
highly specialized and very expensive hardware, and even then the same basic
trade-off remains. The recent introduction of compressive sensing and sparse
reconstruction techniques allows for the capture of single-shot high-speed
video, by coding the temporal information in a single frame, and then
reconstructing the full video sequence from this single coded image and a
trained dictionary of image patches. In this paper, we first analyze this
approach, and find insights that help improve the quality of the reconstructed
videos. We then introduce a novel technique, based on convolutional sparse
coding (CSC), and show how it outperforms the state-of-the-art, patch-based
approach in terms of flexibility and efficiency, due to the convolutional
nature of its filter banks. The key idea for CSC high-speed video acquisition
is extending the basic formulation by imposing an additional constraint in the
temporal dimension, which enforces sparsity of the first-order derivatives over
time.
Reservoir Computing Hardware with Cellular Automata
Authors: Alejandro Morán, Christiam F. Frasser, Josep L. Rosselló
Date: 2018-06-13 00:00:00

Elementary cellular automata (ECA) is a widely studied one-dimensional processing methodology where the successive iteration of the automaton may lead to the recreation of a rich pattern dynamic. Recently, cellular automata have been proposed as a feasible way to implement Reservoir Computing (RC) systems in which the automata rule is fixed and the training is performed using a linear regression. In this work we perform an exhaustive study of the performance of the different ECA rules when applied to pattern recognition of time-independent input signals using a RC scheme. Once the different ECA rules have been tested, the most accurate one (rule 90) is selected to implement a digital circuit. Rule 90 is easily reproduced using a reduced set of XOR gates and shift-registers, thus representing a high-performance alternative for RC hardware implementation in terms of processing time, circuit area, power dissipation and system accuracy. The model (both in software and its hardware implementation) has been tested using a pattern recognition task of handwritten numbers (the MNIST database) for which we obtained competitive results in terms of accuracy, speed and power dissipation. The proposed model can be considered to be a low-cost method to implement fast pattern recognition digital circuits.
An image representation based convolutional network for DNA classification
Authors: Bojian Yin, Marleen Balvert, Davide Zambrano, Alexander Schönhuth, Sander Bohte
Date: 2018-06-13 00:00:00
Proceeding: ICLR 2018 1
Tasks: Classification, General Classification

The folding structure of the DNA molecule combined with helper molecules,
also referred to as the chromatin, is highly relevant for the functional
properties of DNA. The chromatin structure is largely determined by the
underlying primary DNA sequence, though the interaction is not yet fully
understood. In this paper we develop a convolutional neural network that takes
an image-representation of primary DNA sequence as its input, and predicts key
determinants of chromatin structure. The method is developed such that it is
capable of detecting interactions between distal elements in the DNA sequence,
which are known to be highly relevant. Our experiments show that the method
outperforms several existing methods both in terms of prediction accuracy and
training time.
Probabilistic Feature Selection and Classification Vector Machine
Authors: Bingbing Jiang, Chang Li, Maarten de Rijke, Xin Yao, Huanhuan Chen
Date: 2016-09-18 00:00:00
Tasks: Classification, feature selection, General Classification

Sparse Bayesian learning is a state-of-the-art supervised learning algorithm
that can choose a subset of relevant samples from the input data and make
reliable probabilistic predictions. However, in the presence of
high-dimensional data with irrelevant features, traditional sparse Bayesian
classifiers suffer from performance degradation and low efficiency by failing
to eliminate irrelevant features. To tackle this problem, we propose a novel
sparse Bayesian embedded feature selection method that adopts truncated
Gaussian distributions as both sample and feature priors. The proposed method,
called probabilistic feature selection and classification vector machine
(PFCVMLP ), is able to simultaneously select relevant features and samples for
classification tasks. In order to derive the analytical solutions, Laplace
approximation is applied to compute approximate posteriors and marginal
likelihoods. Finally, parameters and hyperparameters are optimized by the
type-II maximum likelihood method. Experiments on three datasets validate the
performance of PFCVMLP along two dimensions: classification performance and
effectiveness for feature selection. Finally, we analyze the generalization
performance and derive a generalization error bound for PFCVMLP . By tightening
the bound, the importance of feature selection is demonstrated.
The IQ of Artificial Intelligence
Authors: Dimiter Dobrev
Date: 2018-06-13 00:00:00

All it takes to identify the computer programs which are Artificial
Intelligence is to give them a test and award AI to those that pass the test.
Let us say that the scores they earn at the test will be called IQ. We cannot
pinpoint a minimum IQ threshold that a program has to cover in order to be AI,
however, we will choose a certain value. Thus, our definition for AI will be
any program the IQ of which is above the chosen value. While this idea has
already been implemented in [3], here we will revisit this construct in order
to introduce certain improvements.
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
Authors: Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, Massimilano Pontil
Date: 2018-06-13 00:00:00
Proceeding: ICML 2018 7
Tasks: Few-Shot Learning, Hyperparameter Optimization, Meta-Learning

We introduce a framework based on bilevel programming that unifies
gradient-based hyperparameter optimization and meta-learning. We show that an
approximate version of the bilevel problem can be solved by taking into
explicit account the optimization dynamics for the inner objective. Depending
on the specific setting, the outer variables take either the meaning of
hyperparameters in a supervised learning problem or parameters of a
meta-learner. We provide sufficient conditions under which solutions of the
approximate problem converge to those of the exact problem. We instantiate our
approach for meta-learning in the case of deep learning where representation
layers are treated as hyperparameters shared across a set of training episodes.
In experiments, we confirm our theoretical findings, present encouraging
results for few-shot learning and contrast the bilevel approach against
classical approaches for learning-to-learn.
A Machine-Learning Item Recommendation System for Video Games
Authors: Paul Bertens, Anna Guitart, Pei Pei Chen, África Periáñez
Date: 2018-06-13 00:00:00
Tasks: BIG-bench Machine Learning

Video-game players generate huge amounts of data, as everything they do
within a game is recorded. In particular, among all the stored actions and
behaviors, there is information on the in-game purchases of virtual products.
Such information is of critical importance in modern free-to-play titles, where
gamers can select or buy a profusion of items during the game in order to
progress and fully enjoy their experience.
  To try to maximize these kind of purchases, one can use a recommendation
system so as to present players with items that might be interesting for them.
Such systems can better achieve their goal by employing machine learning
algorithms that are able to predict the rating of an item or product by a
particular user. In this paper we evaluate and compare two of these algorithms,
an ensemble-based model (extremely randomized trees) and a deep neural network,
both of which are promising candidates for operational video-game recommender
engines.
  Item recommenders can help developers improve the game. But, more
importantly, it should be possible to integrate them into the game, so that
users automatically get personalized recommendations while playing. The
presented models are not only able to meet this challenge, providing accurate
predictions of the items that a particular player will find attractive, but
also sufficiently fast and robust to be used in operational settings.
Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models
Authors: Jiuxiang Gu, Jianfei Cai, Shafiq Joty, Li Niu, Gang Wang
Date: 2017-11-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: Cross-Modal Retrieval, Image-text Retrieval, Retrieval, Text Retrieval

Textual-visual cross-modal retrieval has been a hot research topic in both
computer vision and natural language processing communities. Learning
appropriate representations for multi-modal data is crucial for the cross-modal
retrieval performance. Unlike existing image-text retrieval approaches that
embed image-text pairs as single feature vectors in a common representational
space, we propose to incorporate generative processes into the cross-modal
feature embedding, through which we are able to learn not only the global
abstract features but also the local grounded features. Extensive experiments
show that our framework can well match images and sentences with complex
content, and achieve the state-of-the-art cross-modal retrieval results on
MSCOCO dataset.
Learning Longer-term Dependencies in RNNs with Auxiliary Losses
Authors: Trieu H. Trinh, Andrew M. Dai, Minh-Thang Luong, Quoc V. Le
Date: 2018-03-01 00:00:00
Proceeding: ICML 2018
Tasks: Document Classification, General Classification, image-classification, Image Classification

Despite recent advances in training recurrent neural networks (RNNs),
capturing long-term dependencies in sequences remains a fundamental challenge.
Most approaches use backpropagation through time (BPTT), which is difficult to
scale to very long sequences. This paper proposes a simple method that improves
the ability to capture long term dependencies in RNNs by adding an unsupervised
auxiliary loss to the original objective. This auxiliary loss forces RNNs to
either reconstruct previous events or predict next events in a sequence, making
truncated backpropagation feasible for long sequences and also improving full
BPTT. We evaluate our method on a variety of settings, including pixel-by-pixel
image classification with sequence lengths up to 16\,000, and a real document
classification benchmark. Our results highlight good performance and resource
efficiency of this approach over competitive baselines, including other
recurrent models and a comparable sized Transformer. Further analyses reveal
beneficial effects of the auxiliary loss on optimization and regularization, as
well as extreme cases where there is little to no backpropagation.
A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams
Authors: Duong Nguyen, Rodolphe Vadaine, Guillaume Hajduch, René Garello, Ronan Fablet
Date: 2018-06-06 00:00:00
Tasks: Anomaly Detection, Deep Learning

In a world of global trading, maritime safety, security and efficiency are
crucial issues. We propose a multi-task deep learning framework for vessel
monitoring using Automatic Identification System (AIS) data streams. We combine
recurrent neural networks with latent variable modeling and an embedding of AIS
messages to a new representation space to jointly address key issues to be
dealt with when considering AIS data streams: massive amount of streaming data,
noisy data and irregular timesampling. We demonstrate the relevance of the
proposed deep learning framework on real AIS datasets for a three-task setting,
namely trajectory reconstruction, anomaly detection and vessel type
identification.
Spurious Local Minima of Deep ReLU Neural Networks in the Neural Tangent Kernel Regime
Authors: Tohru Nitta
Date: 2018-06-13 00:00:00

In this paper, we theoretically prove that the deep ReLU neural networks do not lie in spurious local minima in the loss landscape under the Neural Tangent Kernel (NTK) regime, that is, in the gradient descent training dynamics of the deep ReLU neural networks whose parameters are initialized by a normal distribution in the limit as the widths of the hidden layers tend to infinity.
Visual Speech Enhancement
Authors: Aviv Gabbay, Asaph Shamir, Shmuel Peleg
Date: 2017-11-23 00:00:00
Tasks: Lipreading, Speech Enhancement

When video is shot in noisy environment, the voice of a speaker seen in the
video can be enhanced using the visible mouth movements, reducing background
noise. While most existing methods use audio-only inputs, improved performance
is obtained with our visual speech enhancement, based on an audio-visual neural
network. We include in the training data videos to which we added the voice of
the target speaker as background noise. Since the audio input is not sufficient
to separate the voice of a speaker from his own voice, the trained model better
exploits the visual input and generalizes well to different noise types. The
proposed model outperforms prior audio visual methods on two public lipreading
datasets. It is also the first to be demonstrated on a dataset not designed for
lipreading, such as the weekly addresses of Barack Obama.
Unsupervised Adaptation with Interpretable Disentangled Representations for Distant Conversational Speech Recognition
Authors: Wei-Ning Hsu, Hao Tang, James Glass
Date: 2018-06-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

The current trend in automatic speech recognition is to leverage large
amounts of labeled data to train supervised neural network models.
Unfortunately, obtaining data for a wide range of domains to train robust
models can be costly. However, it is relatively inexpensive to collect large
amounts of unlabeled data from domains that we want the models to generalize
to. In this paper, we propose a novel unsupervised adaptation method that
learns to synthesize labeled data for the target domain from unlabeled
in-domain data and labeled out-of-domain data. We first learn without
supervision an interpretable latent representation of speech that encodes
linguistic and nuisance factors (e.g., speaker and channel) using different
latent variables. To transform a labeled out-of-domain utterance without
altering its transcript, we transform the latent nuisance variables while
maintaining the linguistic variables. To demonstrate our approach, we focus on
a channel mismatch setting, where the domain of interest is distant
conversational speech, and labels are only available for close-talking speech.
Our proposed method is evaluated on the AMI dataset, outperforming all
baselines and bridging the gap between unadapted and in-domain models by over
77% without using any parallel data.
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms
Authors: Yao Liu, Emma Brunskill
Date: 2018-05-23 00:00:00
Tasks: Efficient Exploration, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Efficient exploration is one of the key challenges for reinforcement learning
(RL) algorithms. Most traditional sample efficiency bounds require strategic
exploration. Recently many deep RL algorithms with simple heuristic exploration
strategies that have few formal guarantees, achieve surprising success in many
domains. These results pose an important question about understanding these
exploration strategies such as $e$-greedy, as well as understanding what
characterize the difficulty of exploration in MDPs. In this work we propose
problem specific sample complexity bounds of $Q$ learning with random walk
exploration that rely on several structural properties. We also link our
theoretical results to some empirical benchmark domains, to illustrate if our
bound gives polynomial sample complexity in these domains and how that is
related with the empirical performance.