pwc-archive/papers-with-abstracts huggingface dataset:
Find your Way by Observing the Sun and Other Semantic Cues
Authors: Wei-Chiu Ma, Shenlong Wang, Marcus A. Brubaker, Sanja Fidler, Raquel Urtasun
Date: 2016-06-23 00:00:00

In this paper we present a robust, efficient and affordable approach to
self-localization which does not require neither GPS nor knowledge about the
appearance of the world. Towards this goal, we utilize freely available
cartographic maps and derive a probabilistic model that exploits semantic cues
in the form of sun direction, presence of an intersection, road type, speed
limit as well as the ego-car trajectory in order to produce very reliable
localization results. Our experimental evaluation shows that our approach can
localize much faster (in terms of driving time) with less computation and more
robustly than competing approaches, which ignore semantic information.
Multiplierless 16-point DCT Approximation for Low-complexity Image and Video Coding
Authors: T. L. T. Silveira, R. S. Oliveira, F. M. Bayer, R. J. Cintra, A. Madanayake
Date: 2016-06-23 00:00:00
Tasks: Image Compression, SSIM

An orthogonal 16-point approximate discrete cosine transform (DCT) is
introduced. The proposed transform requires neither multiplications nor
bit-shifting operations. A fast algorithm based on matrix factorization is
introduced, requiring only 44 additions---the lowest arithmetic cost in
literature. To assess the introduced transform, computational complexity,
similarity with the exact DCT, and coding performance measures are computed.
Classical and state-of-the-art 16-point low-complexity transforms were used in
a comparative analysis. In the context of image compression, the proposed
approximation was evaluated via PSNR and SSIM measurements, attaining the best
cost-benefit ratio among the competitors. For video encoding, the proposed
approximation was embedded into a HEVC reference software for direct comparison
with the original HEVC standard. Physically realized and tested using FPGA
hardware, the proposed transform showed 35% and 37% improvements of area-time
and area-time-squared VLSI metrics when compared to the best competing
transform in the literature.
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
Authors: Christos Louizos, Max Welling
Date: 2016-03-15 00:00:00
Tasks: Deep Learning, Gaussian Processes

We introduce a variational Bayesian neural network where the parameters are
governed via a probability distribution on random matrices. Specifically, we
employ a matrix variate Gaussian \cite{gupta1999matrix} parameter posterior
distribution where we explicitly model the covariance among the input and
output dimensions of each layer. Furthermore, with approximate covariance
matrices we can achieve a more efficient way to represent those correlations
that is also cheaper than fully factorized parameter posteriors. We further
show that with the "local reprarametrization trick"
\cite{kingma2015variational} on this posterior distribution we arrive at a
Gaussian Process \cite{rasmussen2006gaussian} interpretation of the hidden
units in each layer and we, similarly with \cite{gal2015dropout}, provide
connections with deep Gaussian processes. We continue in taking advantage of
this duality and incorporate "pseudo-data" \cite{snelson2005sparse} in our
model, which in turn allows for more efficient sampling while maintaining the
properties of the original model. The validity of the proposed approach is
verified through extensive experiments.
Precise neural network computation with imprecise analog devices
Authors: Jonathan Binas, Daniel Neil, Giacomo Indiveri, Shih-Chii Liu, Michael Pfeiffer
Date: 2016-06-23 00:00:00

The operations used for neural network computation map favorably onto simple analog circuits, which outshine their digital counterparts in terms of compactness and efficiency. Nevertheless, such implementations have been largely supplanted by digital designs, partly because of device mismatch effects due to material and fabrication imperfections. We propose a framework that exploits the power of deep learning to compensate for this mismatch by incorporating the measured device variations as constraints in the neural network training process. This eliminates the need for mismatch minimization strategies and allows circuit complexity and power-consumption to be reduced to a minimum. Our results, based on large-scale simulations as well as a prototype VLSI chip implementation indicate a processing efficiency comparable to current state-of-art digital implementations. This method is suitable for future technology based on nanodevices with large variability, such as memristive arrays.
Fast Multi-Layer Laplacian Enhancement
Authors: Hossein Talebi, Peyman Milanfar
Date: 2016-06-23 00:00:00
Tasks: Denoising, image smoothing

A novel, fast and practical way of enhancing images is introduced in this
paper. Our approach builds on Laplacian operators of well-known edge-aware
kernels, such as bilateral and nonlocal means, and extends these filter's
capabilities to perform more effective and fast image smoothing, sharpening and
tone manipulation. We propose an approximation of the Laplacian, which does not
require normalization of the kernel weights. Multiple Laplacians of the
affinity weights endow our method with progressive detail decomposition of the
input image from fine to coarse scale. These image components are blended by a
structure mask, which avoids noise/artifact magnification or detail loss in the
output image. Contributions of the proposed method to existing image editing
tools are: (1) Low computational and memory requirements, making it appropriate
for mobile device implementations (e.g. as a finish step in a camera pipeline),
(2) A range of filtering applications from detail enhancement to denoising with
only a few control parameters, enabling the user to apply a combination of
various (and even opposite) filtering effects.
Robust Learning of Fixed-Structure Bayesian Networks
Authors: Yu Cheng, Ilias Diakonikolas, Daniel Kane, Alistair Stewart
Date: 2016-06-23 00:00:00
Proceeding: NeurIPS 2018 12

We investigate the problem of learning Bayesian networks in a robust model
where an $\epsilon$-fraction of the samples are adversarially corrupted. In
this work, we study the fully observable discrete case where the structure of
the network is given. Even in this basic setting, previous learning algorithms
either run in exponential time or lose dimension-dependent factors in their
error guarantees. We provide the first computationally efficient robust
learning algorithm for this problem with dimension-independent error
guarantees. Our algorithm has near-optimal sample complexity, runs in
polynomial time, and achieves error that scales nearly-linearly with the
fraction of adversarially corrupted samples. Finally, we show on both synthetic
and semi-synthetic data that our algorithm performs well in practice.
Parallel SGD: When does averaging help?
Authors: Jian Zhang, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré
Date: 2016-06-23 00:00:00

Consider a number of workers running SGD independently on the same pool of
data and averaging the models every once in a while -- a common but not well
understood practice. We study model averaging as a variance-reducing mechanism
and describe two ways in which the frequency of averaging affects convergence.
For convex objectives, we show the benefit of frequent averaging depends on the
gradient variance envelope. For non-convex objectives, we illustrate that this
benefit depends on the presence of multiple globally optimal points. We
complement our findings with multicore experiments on both synthetic and real
data.
Dynamical optical flow of saliency maps for predicting visual attention
Authors: Aniello Raffaele Patrone, Christian Valuch, Ulrich Ansorge, Otmar Scherzer
Date: 2016-06-23 00:00:00
Tasks: Optical Flow Estimation

Saliency maps are used to understand human attention and visual fixation.
However, while very well established for static images, there is no general
agreement on how to compute a saliency map of dynamic scenes. In this paper we
propose a mathematically rigorous approach to this prob- lem, including static
saliency maps of each video frame for the calculation of the optical flow.
Taking into account static saliency maps for calculating the optical flow
allows for overcoming the aperture problem. Our ap- proach is able to explain
human fixation behavior in situations which pose challenges to standard
approaches, such as when a fixated object disappears behind an occlusion and
reappears after several frames. In addition, we quantitatively compare our
model against alternative solutions using a large eye tracking data set.
Together, our results suggest that assessing optical flow information across a
series of saliency maps gives a highly accurate and useful account of human
overt attention in dynamic scenes.
Unsupervised preprocessing for Tactile Data
Authors: Maximilian Karl, Justin Bayer, Patrick van der Smagt
Date: 2016-06-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Tactile information is important for gripping, stable grasp, and in-hand
manipulation, yet the complexity of tactile data prevents widespread use of
such sensors. We make use of an unsupervised learning algorithm that transforms
the complex tactile data into a compact, latent representation without the need
to record ground truth reference data. These compact representations can either
be used directly in a reinforcement learning based controller or can be used to
calibrate the tactile sensor to physical quantities with only a few datapoints.
We show the quality of our latent representation by predicting important
features and with a simple control task.
Theoretical Robopsychology: Samu Has Learned Turing Machines
Authors: Norbert Bátfai
Date: 2016-06-08 00:00:00
Tasks: BIG-bench Machine Learning

From the point of view of a programmer, the robopsychology is a synonym for
the activity is done by developers to implement their machine learning
applications. This robopsychological approach raises some fundamental
theoretical questions of machine learning. Our discussion of these questions is
constrained to Turing machines. Alan Turing had given an algorithm (aka the
Turing Machine) to describe algorithms. If it has been applied to describe
itself then this brings us to Turing's notion of the universal machine. In the
present paper, we investigate algorithms to write algorithms. From a pedagogy
point of view, this way of writing programs can be considered as a combination
of learning by listening and learning by doing due to it is based on applying
agent technology and machine learning. As the main result we introduce the
problem of learning and then we show that it cannot easily be handled in
reality therefore it is reasonable to use machine learning algorithm for
learning Turing machines.
The AMU-UEDIN Submission to the WMT16 News Translation Task: Attention-based NMT Models as Feature Functions in Phrase-based SMT
Authors: Marcin Junczys-Dowmunt, Tomasz Dwojak, Rico Sennrich
Date: 2016-05-16 00:00:00
Proceeding: WS 2016 8
Tasks: GPU, Machine Translation, NMT, Translation

This paper describes the AMU-UEDIN submissions to the WMT 2016 shared task on
news translation. We explore methods of decode-time integration of
attention-based neural translation models with phrase-based statistical machine
translation. Efficient batch-algorithms for GPU-querying are proposed and
implemented. For English-Russian, our system stays behind the state-of-the-art
pure neural models in terms of BLEU. Among restricted systems, manual
evaluation places it in the first cluster tied with the pure neural model. For
the Russian-English task, our submission achieves the top BLEU result,
outperforming the best pure neural system by 1.1 BLEU points and our own
phrase-based baseline by 1.6 BLEU. After manual evaluation, this system is the
best restricted system in its own cluster. In follow-up experiments we improve
results by additional 0.8 BLEU.
Log-linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post-Editing
Authors: Marcin Junczys-Dowmunt, Roman Grundkiewicz
Date: 2016-05-16 00:00:00
Proceeding: WS 2016 8
Tasks: Automatic Post-Editing, Machine Translation, Translation

This paper describes the submission of the AMU (Adam Mickiewicz University)
team to the Automatic Post-Editing (APE) task of WMT 2016. We explore the
application of neural translation models to the APE problem and achieve good
results by treating different models as components in a log-linear model,
allowing for multiple inputs (the MT-output and the source) that are decoded to
the same target language (post-edited translations). A simple string-matching
penalty integrated within the log-linear model is used to control for higher
faithfulness with regard to the raw machine translation output. To overcome the
problem of too little training data, we generate large amounts of artificial
data. Our submission improves over the uncorrected baseline on the unseen test
set by -3.2\% TER and +5.5\% BLEU and outperforms any other system submitted to
the shared-task by a large margin.
Explaining Predictions of Non-Linear Classifiers in NLP
Authors: Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek
Date: 2016-06-23 00:00:00
Proceeding: WS 2016 8
Tasks: General Classification, image-classification, Image Classification

Layer-wise relevance propagation (LRP) is a recently proposed technique for
explaining predictions of complex non-linear classifiers in terms of input
variables. In this paper, we apply LRP for the first time to natural language
processing (NLP). More precisely, we use it to explain the predictions of a
convolutional neural network (CNN) trained on a topic categorization task. Our
analysis highlights which words are relevant for a specific prediction of the
CNN. We compare our technique to standard sensitivity analysis, both
qualitatively and quantitatively, using a "word deleting" perturbation
experiment, a PCA analysis, and various visualizations. All experiments
validate the suitability of LRP for explaining the CNN predictions, which is
also in line with results reported in recent image classification studies.
Improving performance of recurrent neural network with relu nonlinearity
Authors: Sachin S. Talathi, Aniket Vartak
Date: 2015-11-12 00:00:00
Tasks: Action Recognition, Temporal Action Localization

In recent years significant progress has been made in successfully training
recurrent neural networks (RNNs) on sequence learning problems involving long
range temporal dependencies. The progress has been made on three fronts: (a)
Algorithmic improvements involving sophisticated optimization techniques, (b)
network design involving complex hidden layer nodes and specialized recurrent
layer connections and (c) weight initialization methods. In this paper, we
focus on recently proposed weight initialization with identity matrix for the
recurrent weights in a RNN. This initialization is specifically proposed for
hidden nodes with Rectified Linear Unit (ReLU) non linearity. We offer a simple
dynamical systems perspective on weight initialization process, which allows us
to propose a modified weight initialization strategy. We show that this
initialization technique leads to successfully training RNNs composed of ReLUs.
We demonstrate that our proposal produces comparable or better solution for
three toy problems involving long range temporal structure: the addition
problem, the multiplication problem and the MNIST classification problem using
sequence of pixels. In addition, we present results for a benchmark action
recognition problem.
Proceedings Fifteenth Conference on Theoretical Aspects of Rationality and Knowledge
Authors: R. Ramanujam
Date: 2016-06-23 00:00:00
Tasks: Distributed Computing, Philosophy

The 15th Conference on Theoretical Aspects of Rationality and Knowledge
(TARK) took place in Carnegie Mellon University, Pittsburgh, USA from June 4 to
6, 2015.
  The mission of the TARK conferences is to bring together researchers from a
wide variety of fields, including Artificial Intelligence, Cryptography,
Distributed Computing, Economics and Game Theory, Linguistics, Philosophy, and
Psychology, in order to further our understanding of interdisciplinary issues
involving reasoning about rationality and knowledge.
  These proceedings consist of a subset of the papers / abstracts presented at
the TARK conference.
Non-convex regularization in remote sensing
Authors: Devis Tuia, Remi Flamary, Michel Barlaud
Date: 2016-06-23 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

In this paper, we study the effect of different regularizers and their
implications in high dimensional image classification and sparse linear
unmixing. Although kernelization or sparse methods are globally accepted
solutions for processing data in high dimensions, we present here a study on
the impact of the form of regularization used and its parametrization. We
consider regularization via traditional squared (2) and sparsity-promoting (1)
norms, as well as more unconventional nonconvex regularizers (p and Log Sum
Penalty). We compare their properties and advantages on several classification
and linear unmixing tasks and provide advices on the choice of the best
regularizer for the problem at hand. Finally, we also provide a fully
functional toolbox for the community.
Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions
Authors: Fabio Carrara, Andrea Esuli, Tiziano Fagni, Fabrizio Falchi, Alejandro Moreo Fernández
Date: 2016-06-23 00:00:00
Tasks: Cross-Modal Information Retrieval, Cross-Modal Retrieval, Descriptive, Image Retrieval, Translation

In this paper we tackle the problem of image search when the query is a short
textual description of the image the user is looking for. We choose to
implement the actual search process as a similarity search in a visual feature
space, by learning to translate a textual query into a visual representation.
Searching in the visual feature space has the advantage that any update to the
translation model does not require to reprocess the, typically huge, image
collection on which the search is performed. We propose Text2Vis, a neural
network that generates a visual representation, in the visual feature space of
the fc6-fc7 layers of ImageNet, from a short descriptive text. Text2Vis
optimizes two loss functions, using a stochastic loss-selection method. A
visual-focused loss is aimed at learning the actual text-to-visual feature
mapping, while a text-focused loss is aimed at modeling the higher-level
semantic concepts expressed in language and countering the overfit on
non-relevant visual components of the visual loss. We report preliminary
results on the MS-COCO dataset.
Importance sampling strategy for non-convex randomized block-coordinate descent
Authors: Rémi Flamary, Alain Rakotomamonjy, Gilles Gasso
Date: 2016-06-23 00:00:00

As the number of samples and dimensionality of optimization problems related
to statistics an machine learning explode, block coordinate descent algorithms
have gained popularity since they reduce the original problem to several
smaller ones. Coordinates to be optimized are usually selected randomly
according to a given probability distribution. We introduce an importance
sampling strategy that helps randomized coordinate descent algorithms to focus
on blocks that are still far from convergence. The framework applies to
problems composed of the sum of two possibly non-convex terms, one being
separable and non-smooth. We have compared our algorithm to a full gradient
proximal approach as well as to a randomized block coordinate algorithm that
considers uniform sampling and cyclic block coordinate descent. Experimental
evidences show the clear benefit of using an importance sampling strategy.
Event Abstraction for Process Mining using Supervised Learning Techniques
Authors: Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst
Date: 2016-06-23 00:00:00

Process mining techniques focus on extracting insight in processes from event
logs. In many cases, events recorded in the event log are too fine-grained,
causing process discovery algorithms to discover incomprehensible process
models or process models that are not representative of the event log. We show
that when process discovery algorithms are only able to discover an
unrepresentative process model from a low-level event log, structure in the
process can in some cases still be discovered by first abstracting the event
log to a higher level of granularity. This gives rise to the challenge to
bridge the gap between an original low-level event log and a desired high-level
perspective on this log, such that a more structured or more comprehensible
process model can be discovered. We show that supervised learning can be
leveraged for the event abstraction task when annotations with high-level
interpretations of the low-level events are available for a subset of the
sequences (i.e., traces). We present a method to generate feature vector
representations of events based on XES extensions, and describe an approach to
abstract events in an event log with Condition Random Fields using these event
features. Furthermore, we propose a sequence-focused metric to evaluate
supervised event abstraction results that fits closely to the tasks of process
discovery and conformance checking. We conclude this paper by demonstrating the
usefulness of supervised event abstraction for obtaining more structured and/or
more comprehensible process models using both real life event data and
synthetic event data.
Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions
Authors: Devis Tuia, Rémi Flamary, Nicolas Courty
Date: 2016-06-23 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

In this paper, we tackle the question of discovering an effective set of
spatial filters to solve hyperspectral classification problems. Instead of
fixing a priori the filters and their parameters using expert knowledge, we let
the model find them within random draws in the (possibly infinite) space of
possible filters. We define an active set feature learner that includes in the
model only features that improve the classifier. To this end, we consider a
fast and linear classifier, multiclass logistic classification, and show that
with a good representation (the filters discovered), such a simple classifier
can reach at least state of the art performances. We apply the proposed active
set learner in four hyperspectral image classification problems, including
agricultural and urban classification at different resolutions, as well as
multimodal data. We also propose a hierarchical setting, which allows to
generate more complex banks of features that can better describe the
nonlinearities present in the data.