pwc-archive/papers-with-abstracts huggingface dataset:
An Alternative Softmax Operator for Reinforcement Learning
Authors: Kavosh Asadi, Michael L. Littman
Date: 2016-12-16 00:00:00
Proceeding: ICML 2017 8
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

A softmax operator applied to a set of values acts somewhat like the
maximization function and somewhat like an average. In sequential decision
making, softmax is often used in settings where it is necessary to maximize
utility but also to hedge against problems that arise from putting all of one's
weight behind a single maximum utility decision. The Boltzmann softmax operator
is the most commonly used softmax operator in this setting, but we show that
this operator is prone to misbehavior. In this work, we study a differentiable
softmax operator that, among other properties, is a non-expansion ensuring a
convergent behavior in learning and planning. We introduce a variant of SARSA
algorithm that, by utilizing the new operator, computes a Boltzmann policy with
a state-dependent temperature parameter. We show that the algorithm is
convergent and that it performs favorably in practice.
Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification
Authors: Yu-Gang Jiang, Zuxuan Wu, Jinhui Tang, Zechao Li, xiangyang xue, Shih-Fu Chang
Date: 2017-06-14 00:00:00
Tasks: General Classification, Video Classification

Videos are inherently multimodal. This paper studies the problem of how to
fully exploit the abundant multimodal clues for improved video categorization.
We introduce a hybrid deep learning framework that integrates useful clues from
multiple modalities, including static spatial appearance information, motion
patterns within a short time window, audio information as well as long-range
temporal dynamics. More specifically, we utilize three Convolutional Neural
Networks (CNNs) operating on appearance, motion and audio signals to extract
their corresponding features. We then employ a feature fusion network to derive
a unified representation with an aim to capture the relationships among
features. Furthermore, to exploit the long-range temporal dynamics in videos,
we apply two Long Short Term Memory networks with extracted appearance and
motion features as inputs. Finally, we also propose to refine the prediction
scores by leveraging contextual relationships among video semantics. The hybrid
deep learning framework is able to exploit a comprehensive set of multimodal
features for video classification. Through an extensive set of experiments, we
demonstrate that (1) LSTM networks which model sequences in an explicitly
recurrent manner are highly complementary with CNN models; (2) the feature
fusion network which produces a fused representation through modeling feature
relationships outperforms alternative fusion strategies; (3) the semantic
context of video classes can help further refine the predictions for improved
performance. Experimental results on two challenging benchmarks, the UCF-101
and the Columbia Consumer Videos (CCV), provide strong quantitative evidence
that our framework achieves promising results: $93.1\%$ on the UCF-101 and
$84.5\%$ on the CCV, outperforming competing methods with clear margins.
Approximating Optimization Problems using EAs on Scale-Free Networks
Authors: Ankit Chauhan, Tobias Friedrich, Francesco Quinzan
Date: 2017-04-12 00:00:00
Tasks: Clustering, Evolutionary Algorithms

It has been observed that many complex real-world networks have certain
properties, such as a high clustering coefficient, a low diameter, and a
power-law degree distribution. A network with a power-law degree distribution
is known as scale-free network. In order to study these networks, various
random graph models have been proposed, e.g. Preferential Attachment, Chung-Lu,
or Hyperbolic.
  We look at the interplay between the power-law degree distribution and the
run time of optimization techniques for well known combinatorial problems. We
observe that on scale-free networks, simple evolutionary algorithms (EAs)
quickly reach a constant-factor approximation ratio on common covering problems
  We prove that the single-objective (1+1)EA reaches a constant-factor
approximation ratio on the Minimum Dominating Set problem, the Minimum Vertex
Cover problem, the Minimum Connected Dominating Set problem, and the Maximum
Independent Set problem in expected polynomial number of calls to the fitness
function.
  Furthermore, we prove that the multi-objective GSEMO algorithm reaches a
better approximation ratio than the (1+1)EA on those problems, within
polynomial fitness evaluations.
Learning Arbitrary Sum-Product Network Leaves with Expectation-Maximization
Authors: Mattia Desana, Christoph Schnörr
Date: 2016-04-25 00:00:00
Tasks: Density Estimation

Sum-Product Networks with complex probability distribution at the leaves have
been shown to be powerful tractable-inference probabilistic models. However,
while learning the internal parameters has been amply studied, learning complex
leaf distribution is an open problem with only few results available in special
cases. In this paper we derive an efficient method to learn a very large class
of leaf distributions with Expectation-Maximization. The EM updates have the
form of simple weighted maximum likelihood problems, allowing to use any
distribution that can be learned with maximum likelihood, even approximately.
The algorithm has cost linear in the model size and converges even if only
partial optimizations are performed. We demonstrate this approach with
experiments on twenty real-life datasets for density estimation, using tree
graphical models as leaves. Our model outperforms state-of-the-art methods for
parameter learning despite using SPNs with much fewer parameters.
Count-Based Exploration with Neural Density Models
Authors: Georg Ostrovski, Marc G. Bellemare, Aaron van den Oord, Remi Munos
Date: 2017-03-03 00:00:00
Proceeding: ICML 2017 8
Tasks: Atari Games, Montezuma's Revenge, Reinforcement Learning

Bellemare et al. (2016) introduced the notion of a pseudo-count, derived from
a density model, to generalize count-based exploration to non-tabular
reinforcement learning. This pseudo-count was used to generate an exploration
bonus for a DQN agent and combined with a mixed Monte Carlo update was
sufficient to achieve state of the art on the Atari 2600 game Montezuma's
Revenge. We consider two questions left open by their work: First, how
important is the quality of the density model for exploration? Second, what
role does the Monte Carlo update play in exploration? We answer the first
question by demonstrating the use of PixelCNN, an advanced neural density model
for images, to supply a pseudo-count. In particular, we examine the intrinsic
difficulties in adapting Bellemare et al.'s approach when assumptions about the
model are violated. The result is a more practical and general algorithm
requiring no special apparatus. We combine PixelCNN pseudo-counts with
different agent architectures to dramatically improve the state of the art on
several hard Atari games. One surprising finding is that the mixed Monte Carlo
update is a powerful facilitator of exploration in the sparsest of settings,
including Montezuma's Revenge.
Large-Scale YouTube-8M Video Understanding with Deep Neural Networks
Authors: Manuk Akopyan, Eshsou Khashba
Date: 2017-06-14 00:00:00
Tasks: Classification, General Classification, Mixture-of-Experts, Video Classification, Video Understanding

Video classification problem has been studied many years. The success of
Convolutional Neural Networks (CNN) in image recognition tasks gives a powerful
incentive for researchers to create more advanced video classification
approaches. As video has a temporal content Long Short Term Memory (LSTM)
networks become handy tool allowing to model long-term temporal clues. Both
approaches need a large dataset of input data. In this paper three models
provided to address video classification using recently announced YouTube-8M
large-scale dataset. The first model is based on frame pooling approach. Two
other models based on LSTM networks. Mixture of Experts intermediate layer is
used in third model allowing to increase model capacity without dramatically
increasing computations. The set of experiments for handling imbalanced
training data has been conducted.
Bayesian optimization of hyper-parameters in reservoir computing
Authors: Jan Yperman, Thijs Becker
Date: 2016-11-16 00:00:00
Tasks: Bayesian Optimization

We describe a method for searching the optimal hyper-parameters in reservoir
computing, which consists of a Gaussian process with Bayesian optimization. It
provides an alternative to other frequently used optimization methods such as
grid, random, or manual search. In addition to a set of optimal
hyper-parameters, the method also provides a probability distribution of the
cost function as a function of the hyper-parameters. We apply this method to
two types of reservoirs: nonlinear delay nodes and echo state networks. It
shows excellent performance on all considered benchmarks, either matching or
significantly surpassing results found in the literature. In general, the
algorithm achieves optimal results in fewer iterations when compared to other
optimization methods. We have optimized up to six hyper-parameters
simultaneously, which would have been infeasible using, e.g., grid search. Due
to its automated nature, this method significantly reduces the need for expert
knowledge when optimizing the hyper-parameters in reservoir computing. Existing
software libraries for Bayesian optimization, such as Spearmint, make the
implementation of the algorithm straightforward. A fork of the Spearmint
framework along with a tutorial on how to use it in practice is available at
https://bitbucket.org/uhasseltmachinelearning/spearmint/
Idea density for predicting Alzheimer's disease from transcribed speech
Authors: Kairit Sirts, Olivier Piguet, Mark Johnson
Date: 2017-06-14 00:00:00
Proceeding: CONLL 2017 8
Tasks: Clustering, Diagnostic

Idea Density (ID) measures the rate at which ideas or elementary predications
are expressed in an utterance or in a text. Lower ID is found to be associated
with an increased risk of developing Alzheimer's disease (AD) (Snowdon et al.,
1996; Engelman et al., 2010). ID has been used in two different versions:
propositional idea density (PID) counts the expressed ideas and can be applied
to any text while semantic idea density (SID) counts pre-defined information
content units and is naturally more applicable to normative domains, such as
picture description tasks. In this paper, we develop DEPID, a novel
dependency-based method for computing PID, and its version DEPID-R that enables
to exclude repeating ideas---a feature characteristic to AD speech. We conduct
the first comparison of automatically extracted PID and SID in the diagnostic
classification task on two different AD datasets covering both closed-topic and
free-recall domains. While SID performs better on the normative dataset, adding
PID leads to a small but significant improvement (+1.7 F-score). On the
free-topic dataset, PID performs better than SID as expected (77.6 vs 72.3 in
F-score) but adding the features derived from the word embedding clustering
underlying the automatic SID increases the results considerably, leading to an
F-score of 84.8.
SalProp: Salient object proposals via aggregated edge cues
Authors: Prerana Mukherjee, Brejesh lall, Sarvaswa Tandon
Date: 2017-06-14 00:00:00
Tasks: Classification, Edge Classification, General Classification, Object, object-detection, Object Detection, Object Proposal Generation

In this paper, we propose a novel object proposal generation scheme by
formulating a graph-based salient edge classification framework that utilizes
the edge context. In the proposed method, we construct a Bayesian probabilistic
edge map to assign a saliency value to the edgelets by exploiting low level
edge features. A Conditional Random Field is then learned to effectively
combine these features for edge classification with object/non-object label. We
propose an objectness score for the generated windows by analyzing the salient
edge density inside the bounding box. Extensive experiments on PASCAL VOC 2007
dataset demonstrate that the proposed method gives competitive performance
against 10 popular generic object detection techniques while using fewer number
of proposals.
Simultaneous merging multiple grid maps using the robust motion averaging
Authors: Zutao Jiang, Jihua Zhu, Yaochen Li, Zhongyu Li, Huimin Lu
Date: 2017-06-14 00:00:00

Mapping in the GPS-denied environment is an important and challenging task in
the field of robotics. In the large environment, mapping can be significantly
accelerated by multiple robots exploring different parts of the environment.
Accordingly, a key problem is how to integrate these local maps built by
different robots into a single global map. In this paper, we propose an
approach for simultaneous merging of multiple grid maps by the robust motion
averaging. The main idea of this approach is to recover all global motions for
map merging from a set of relative motions. Therefore, it firstly adopts the
pair-wise map merging method to estimate relative motions for grid map pairs.
To obtain as many reliable relative motions as possible, a graph-based sampling
scheme is utilized to efficiently remove unreliable relative motions obtained
from the pair-wise map merging. Subsequently, the accurate global motions can
be recovered from the set of reliable relative motions by the motion averaging.
Experimental results carried on real robot data sets demonstrate that proposed
approach can achieve simultaneous merging of multiple grid maps with good
performances.
Number game
Authors: Go Sugimoto
Date: 2017-06-14 00:00:00
Tasks: Marketing

CLARIN (Common Language Resources and Technology Infrastructure) is regarded
as one of the most important European research infrastructures, offering and
promoting a wide array of useful services for (digital) research in linguistics
and humanities. However, the assessment of the users for its core technical
development has been highly limited, therefore, it is unclear if the community
is thoroughly aware of the status-quo of the growing infrastructure. In
addition, CLARIN does not seem to be fully materialised marketing and business
plans and strategies despite its strong technical assets. This article analyses
the web traffic of the Virtual Language Observatory, one of the main web
applications of CLARIN and a symbol of pan-European re-search cooperation, to
evaluate the users and performance of the service in a transparent and
scientific way. It is envisaged that the paper can raise awareness of the
pressing issues on objective and transparent operation of the infrastructure
though Open Evaluation, and the synergy between marketing and technical
development. It also investigates the "science of web analytics" in an attempt
to document the research process for the purpose of reusability and
reproducibility, thus to find universal lessons for the use of a web analytics,
rather than to merely produce a statistical report of a particular website
which loses its value outside its context.
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding
Authors: Mainak Jas, Tom Dupré La Tour, Umut Şimşekli, Alexandre Gramfort
Date: 2017-05-22 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Time Series, Time Series Analysis

Neural time-series data contain a wide variety of prototypical signal
waveforms (atoms) that are of significant importance in clinical and cognitive
research. One of the goals for analyzing such data is hence to extract such
'shift-invariant' atoms. Even though some success has been reported with
existing algorithms, they are limited in applicability due to their heuristic
nature. Moreover, they are often vulnerable to artifacts and impulsive noise,
which are typically present in raw neural recordings. In this study, we address
these issues and propose a novel probabilistic convolutional sparse coding
(CSC) model for learning shift-invariant atoms from raw neural signals
containing potentially severe artifacts. In the core of our model, which we
call $\alpha$CSC, lies a family of heavy-tailed distributions called
$\alpha$-stable distributions. We develop a novel, computationally efficient
Monte Carlo expectation-maximization algorithm for inference. The maximization
step boils down to a weighted CSC problem, for which we develop a
computationally efficient optimization algorithm. Our results show that the
proposed algorithm achieves state-of-the-art convergence speeds. Besides,
$\alpha$CSC is significantly more robust to artifacts when compared to three
competing algorithms: it can extract spike bursts, oscillations, and even
reveal more subtle phenomena such as cross-frequency coupling when applied to
noisy neural time series.
Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow
Authors: Jakub M. Tomczak, Max Welling
Date: 2017-06-07 00:00:00

In this paper, we propose a new volume-preserving flow and show that it
performs similarly to the linear general normalizing flow. The idea is to
enrich a linear Inverse Autoregressive Flow by introducing multiple
lower-triangular matrices with ones on the diagonal and combining them using a
convex combination. In the experimental studies on MNIST and Histopathology
data we show that the proposed approach outperforms other volume-preserving
flows and is competitive with current state-of-the-art linear normalizing flow.
Asynchronous Stochastic Gradient Descent with Delay Compensation
Authors: Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu, Zhi-Ming Ma, Tie-Yan Liu
Date: 2016-09-27 00:00:00
Proceeding: ICML 2017 8

With the fast development of deep learning, it has become common to learn big neural networks using massive training data. Asynchronous Stochastic Gradient Descent (ASGD) is widely adopted to fulfill this task for its efficiency, which is, however, known to suffer from the problem of delayed gradients. That is, when a local worker adds its gradient to the global model, the global model may have been updated by other workers and this gradient becomes "delayed". We propose a novel technology to compensate this delay, so as to make the optimization behavior of ASGD closer to that of sequential SGD. This is achieved by leveraging Taylor expansion of the gradient function and efficient approximation to the Hessian matrix of the loss function. We call the new algorithm Delay Compensated ASGD (DC-ASGD). We evaluated the proposed algorithm on CIFAR-10 and ImageNet datasets, and the experimental results demonstrate that DC-ASGD outperforms both synchronous SGD and asynchronous SGD, and nearly approaches the performance of sequential SGD.
Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models
Authors: Reza Mohammadi, Helene Massam, Gerard Letac
Date: 2017-06-14 00:00:00
Tasks: Computational Efficiency, Model Selection, valid

Gaussian graphical models are relevant tools to learn conditional independence structure between variables. In this class of models, Bayesian structure learning is often done by search algorithms over the graph space. The conjugate prior for the precision matrix satisfying graphical constraints is the well-known G-Wishart. With this prior, the transition probabilities in the search algorithms necessitate evaluating the ratios of the prior normalizing constants of G-Wishart. In moderate to high-dimensions, this ratio is often approximated using sampling-based methods as computationally expensive updates in the search algorithm. Calculating this ratio so far has been a major computational bottleneck. We overcome this issue by representing a search algorithm in which the ratio of normalizing constant is carried out by an explicit closed-form approximation. Using this approximation within our search algorithm yields significant improvement in the scalability of structure learning without sacrificing structure learning accuracy. We study the conditions under which the approximation is valid. We also evaluate the efficacy of our method with simulation studies. We show that the new search algorithm with our approximation outperforms state-of-the-art methods in both computational efficiency and accuracy. The implementation of our work is available in the R package BDgraph.
Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks
Authors: Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante, Martin Rajchl, Matthew Lee, Ben Glocker, Daniel Rueckert
Date: 2017-03-07 00:00:00
Tasks: Graph Similarity, Metric Learning

Evaluating similarity between graphs is of major importance in several
computer vision and pattern recognition problems, where graph representations
are often used to model objects or interactions between elements. The choice of
a distance or similarity metric is, however, not trivial and can be highly
dependent on the application at hand. In this work, we propose a novel metric
learning method to evaluate distance between graphs that leverages the power of
convolutional neural networks, while exploiting concepts from spectral graph
theory to allow these operations on irregular graphs. We demonstrate the
potential of our method in the field of connectomics, where neuronal pathways
or functional connections between brain regions are commonly modelled as
graphs. In this problem, the definition of an appropriate graph similarity
function is critical to unveil patterns of disruptions associated with certain
brain disorders. Experimental results on the ABIDE dataset show that our method
can learn a graph similarity metric tailored for a clinical application,
improving the performance of a simple k-nn classifier by 11.9% compared to a
traditional distance metric.
Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection
Authors: Manh Duong Phung, Cong Hoang Quach, Tran Hiep Dinh, Quang Ha
Date: 2017-06-14 00:00:00
Tasks: GPU

In built infrastructure monitoring, an efficient path planning algorithm is
essential for robotic inspection of large surfaces using computer vision. In
this work, we first formulate the inspection path planning problem as an
extended travelling salesman problem (TSP) in which both the coverage and
obstacle avoidance were taken into account. An enhanced discrete particle swarm
optimization (DPSO) algorithm is then proposed to solve the TSP, with
performance improvement by using deterministic initialization, random mutation,
and edge exchange. Finally, we take advantage of parallel computing to
implement the DPSO in a GPU-based framework so that the computation time can be
significantly reduced while keeping the hardware requirement unchanged. To show
the effectiveness of the proposed algorithm, experimental results are included
for datasets obtained from UAV inspection of an office building and a bridge.