pwc-archive/papers-with-abstracts huggingface dataset:
Incremental Sampling-based Motion Planners Using Policy Iteration Methods
Authors: Oktay Arslan, Panagiotis Tsiotras
Date: 2016-09-19 00:00:00
Tasks: Motion Planning

Recent progress in randomized motion planners has led to the development of a
new class of sampling-based algorithms that provide asymptotic optimality
guarantees, notably the RRT* and the PRM* algorithms. Careful analysis reveals
that the so-called "rewiring" step in these algorithms can be interpreted as a
local policy iteration (PI) step (i.e., a local policy evaluation step followed
by a local policy improvement step) so that asymptotically, as the number of
samples tend to infinity, both algorithms converge to the optimal path almost
surely (with probability 1). Policy iteration, along with value iteration (VI)
are common methods for solving dynamic programming (DP) problems. Based on this
observation, recently, the RRT$^{\#}$ algorithm has been proposed, which
performs, during each iteration, Bellman updates (aka "backups") on those
vertices of the graph that have the potential of being part of the optimal path
(i.e., the "promising" vertices). The RRT$^{\#}$ algorithm thus utilizes
dynamic programming ideas and implements them incrementally on randomly
generated graphs to obtain high quality solutions. In this work, and based on
this key insight, we explore a different class of dynamic programming
algorithms for solving shortest-path problems on random graphs generated by
iterative sampling methods. These class of algorithms utilize policy iteration
instead of value iteration, and thus are better suited for massive
parallelization. Contrary to the RRT* algorithm, the policy improvement during
the rewiring step is not performed only locally but rather on a set of vertices
that are classified as "promising" during the current iteration. This tends to
speed-up the whole process. The resulting algorithm, aptly named Policy
Iteration-RRT$^{\#}$ (PI-RRT$^{\#}$) is the first of a new class of DP-inspired
algorithms for randomized motion planning that utilize PI methods.
Conformalized Kernel Ridge Regression
Authors: Evgeny Burnaev, Ivan Nazarov
Date: 2016-09-19 00:00:00
Tasks: Anomaly Detection, regression

General predictive models do not provide a measure of confidence in
predictions without Bayesian assumptions. A way to circumvent potential
restrictions is to use conformal methods for constructing non-parametric
confidence regions, that offer guarantees regarding validity. In this paper we
provide a detailed description of a computationally efficient conformal
procedure for Kernel Ridge Regression (KRR), and conduct a comparative
numerical study to see how well conformal regions perform against the Bayesian
confidence sets. The results suggest that conformalized KRR can yield
predictive confidence regions with specified coverage rate, which is essential
in constructing anomaly detection systems based on predictive models.
The Optional Prisoner's Dilemma in a Spatial Environment: Coevolving Game Strategy and Link Weights
Authors: Marcos Cardinot, Colm O'Riordan, Josephine Griffith
Date: 2016-09-19 00:00:00

In this paper, the Optional Prisoner's Dilemma game in a spatial environment,
with coevolutionary rules for both the strategy and network links between
agents, is studied. Using a Monte Carlo simulation approach, a number of
experiments are performed to identify favourable configurations of the
environment for the emergence of cooperation in adverse scenarios. Results show
that abstainers play a key role in the protection of cooperators against
exploitation from defectors. Scenarios of cyclic competition and of full
dominance of cooperation are also observed. This work provides insights towards
gaining an in-depth understanding of the emergence of cooperative behaviour in
real-world systems.
Joint Semantic Segmentation and Depth Estimation with Deep Convolutional Networks
Authors: Arsalan Mousavian, Hamed Pirsiavash, Jana Kosecka
Date: 2016-04-25 00:00:00
Tasks: Depth Estimation, Segmentation, Semantic Segmentation

Multi-scale deep CNNs have been used successfully for problems mapping each
pixel to a label, such as depth estimation and semantic segmentation. It has
also been shown that such architectures are reusable and can be used for
multiple tasks. These networks are typically trained independently for each
task by varying the output layer(s) and training objective. In this work we
present a new model for simultaneous depth estimation and semantic segmentation
from a single RGB image. Our approach demonstrates the feasibility of training
parts of the model for each task and then fine tuning the full, combined model
on both tasks simultaneously using a single loss function. Furthermore we
couple the deep CNN with fully connected CRF, which captures the contextual
relationships and interactions between the semantic and depth cues improving
the accuracy of the final results. The proposed model is trained and evaluated
on NYUDepth V2 dataset outperforming the state of the art methods on semantic
segmentation and achieving comparable results on the task of depth estimation.
Iteratively Reweighted Least Squares Algorithms for L1-Norm Principal Component Analysis
Authors: Young Woong Park, Diego Klabjan
Date: 2016-09-10 00:00:00

Principal component analysis (PCA) is often used to reduce the dimension of
data by selecting a few orthonormal vectors that explain most of the variance
structure of the data. L1 PCA uses the L1 norm to measure error, whereas the
conventional PCA uses the L2 norm. For the L1 PCA problem minimizing the
fitting error of the reconstructed data, we propose an exact reweighted and an
approximate algorithm based on iteratively reweighted least squares. We provide
convergence analyses, and compare their performance against benchmark
algorithms in the literature. The computational experiment shows that the
proposed algorithms consistently perform best.
Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models
Authors: Bryan A. Plummer, Li-Wei Wang, Chris M. Cervantes, Juan C. Caicedo, Julia Hockenmaier, Svetlana Lazebnik
Date: 2015-05-19 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Description, Phrase Grounding, Retrieval, Sentence, Sentence Retrieval

The Flickr30k dataset has become a standard benchmark for sentence-based
image description. This paper presents Flickr30k Entities, which augments the
158k captions from Flickr30k with 244k coreference chains, linking mentions of
the same entities across different captions for the same image, and associating
them with 276k manually annotated bounding boxes. Such annotations are
essential for continued progress in automatic image description and grounded
language understanding. They enable us to define a new benchmark for
localization of textual entity mentions in an image. We present a strong
baseline for this task that combines an image-text embedding, detectors for
common objects, a color classifier, and a bias towards selecting larger
objects. While our baseline rivals in accuracy more complex state-of-the-art
models, we show that its gains cannot be easily parlayed into improvements on
such tasks as image-sentence retrieval, thus underlining the limitations of
current methods and the need for further research.
Online and Distributed learning of Gaussian mixture models by Bayesian Moment Matching
Authors: Priyank Jaini, Pascal Poupart
Date: 2016-09-19 00:00:00
Tasks: Clustering, parameter estimation

The Gaussian mixture model is a classic technique for clustering and data
modeling that is used in numerous applications. With the rise of big data,
there is a need for parameter estimation techniques that can handle streaming
data and distribute the computation over several processors. While online
variants of the Expectation Maximization (EM) algorithm exist, their data
efficiency is reduced by a stochastic approximation of the E-step and it is not
clear how to distribute the computation over multiple processors. We propose a
Bayesian learning technique that lends itself naturally to online and
distributed computation. Since the Bayesian posterior is not tractable, we
project it onto a family of tractable distributions after each observation by
matching a set of sufficient moments. This Bayesian moment matching technique
compares favorably to online EM in terms of time and accuracy on a set of data
modeling benchmarks.
Accelerating Deep Learning with Shrinkage and Recall
Authors: Shuai Zheng, Abhinav Vishnu, Chris Ding
Date: 2016-05-04 00:00:00
Tasks: Deep Learning, General Classification

Deep Learning is a very powerful machine learning model. Deep Learning trains
a large number of parameters for multiple layers and is very slow when data is
in large scale and the architecture size is large. Inspired from the shrinking
technique used in accelerating computation of Support Vector Machines (SVM)
algorithm and screening technique used in LASSO, we propose a shrinking Deep
Learning with recall (sDLr) approach to speed up deep learning computation. We
experiment shrinking Deep Learning with recall (sDLr) using Deep Neural Network
(DNN), Deep Belief Network (DBN) and Convolution Neural Network (CNN) on 4 data
sets. Results show that the speedup using shrinking Deep Learning with recall
(sDLr) can reach more than 2.0 while still giving competitive classification
performance.
Geometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes
Authors: Angelia Nedić, Alex Olshevsky, Wei Shi, César A. Uribe
Date: 2016-09-19 00:00:00
Tasks: Distributed Optimization

A recent algorithmic family for distributed optimization, DIGing's, have been
shown to have geometric convergence over time-varying undirected/directed
graphs. Nevertheless, an identical step-size for all agents is needed. In this
paper, we study the convergence rates of the Adapt-Then-Combine (ATC) variation
of the DIGing algorithm under uncoordinated step-sizes. We show that the ATC
variation of DIGing algorithm converges geometrically fast even if the
step-sizes are different among the agents. In addition, our analysis implies
that the ATC structure can accelerate convergence compared to the distributed
gradient descent (DGD) structure which has been used in the original DIGing
algorithm.
On the Phase Transition of Finding a Biclique in a larger Bipartite Graph
Authors: Roberto Alonso, Raúl Monroy, Eduardo Aguirre
Date: 2016-09-19 00:00:00
Tasks: Clustering

We report on the phase transition of finding a complete subgraph, of
specified dimensions, in a bipartite graph. Finding a complete subgraph in a
bipartite graph is a problem that has growing attention in several domains,
including bioinformatics, social network analysis and domain clustering. A key
step for a successful phase transition study is identifying a suitable order
parameter, when none is known. To this purpose, we have applied a decision tree
classifier to real-world instances of this problem, in order to understand what
problem features separate an instance that is hard to solve from those that is
not. We have successfully identified one such order parameter and with it the
phase transition of finding a complete bipartite subgraph of specified
dimensions. Our phase transition study shows an
easy-to-hard-to-easy-to-hard-to-easy pattern. Further, our results indicate
that the hardest instances are in a region where it is more likely that the
corresponding bipartite graph will have a complete subgraph of specified
dimensions, a positive answer. By contrast, instances with a negative answer
are more likely to appear in a region where the computational cost is
negligible. This behaviour is remarkably similar for problems of a number of
different sizes.
Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography
Authors: Avisek Lahiri, Abhijit Guha Roy, Debdoot Sheet, Prabir Kumar Biswas
Date: 2016-09-19 00:00:00
Tasks: Diversity, Medical Image Analysis, Retinal Vessel Segmentation, Segmentation

Automated segmentation of retinal blood vessels in label-free fundus images
entails a pivotal role in computed aided diagnosis of ophthalmic pathologies,
viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases.
The challenge remains active in medical image analysis research due to varied
distribution of blood vessels, which manifest variations in their dimensions of
physical appearance against a noisy background.
  In this paper we formulate the segmentation challenge as a classification
task. Specifically, we employ unsupervised hierarchical feature learning using
ensemble of two level of sparsely trained denoised stacked autoencoder. First
level training with bootstrap samples ensures decoupling and second level
ensemble formed by different network architectures ensures architectural
revision. We show that ensemble training of auto-encoders fosters diversity in
learning dictionary of visual kernels for vessel segmentation. SoftMax
classifier is used for fine tuning each member auto-encoder and multiple
strategies are explored for 2-level fusion of ensemble members. On DRIVE
dataset, we achieve maximum average accuracy of 95.33\% with an impressively
low standard deviation of 0.003 and Kappa agreement coefficient of 0.708 .
Comparison with other major algorithms substantiates the high efficacy of our
model.
A Cheap Linear Attention Mechanism with Fast Lookups and Fixed-Size Representations
Authors: Alexandre de Brébisson, Pascal Vincent
Date: 2016-09-19 00:00:00
Tasks: Question Answering

The softmax content-based attention mechanism has proven to be very
beneficial in many applications of recurrent neural networks. Nevertheless it
suffers from two major computational limitations. First, its computations for
an attention lookup scale linearly in the size of the attended sequence.
Second, it does not encode the sequence into a fixed-size representation but
instead requires to memorize all the hidden states. These two limitations
restrict the use of the softmax attention mechanism to relatively small-scale
applications with short sequences and few lookups per sequence. In this work we
introduce a family of linear attention mechanisms designed to overcome the two
limitations listed above. We show that removing the softmax non-linearity from
the traditional attention formulation yields constant-time attention lookups
and fixed-size representations of the attended sequences. These properties make
these linear attention mechanisms particularly suitable for large-scale
applications with extreme query loads, real-time requirements and memory
constraints. Early experiments on a question answering task show that these
linear mechanisms yield significantly better accuracy results than no
attention, but obviously worse than their softmax alternative.
WaveNet: A Generative Model for Raw Audio
Authors: Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, Koray Kavukcuoglu
Date: 2016-09-12 00:00:00
Tasks: Audio Generation, model, Phoneme Recognition, Speech Synthesis, text-to-speech, Text to Speech

This paper introduces WaveNet, a deep neural network for generating raw audio
waveforms. The model is fully probabilistic and autoregressive, with the
predictive distribution for each audio sample conditioned on all previous ones;
nonetheless we show that it can be efficiently trained on data with tens of
thousands of samples per second of audio. When applied to text-to-speech, it
yields state-of-the-art performance, with human listeners rating it as
significantly more natural sounding than the best parametric and concatenative
systems for both English and Mandarin. A single WaveNet can capture the
characteristics of many different speakers with equal fidelity, and can switch
between them by conditioning on the speaker identity. When trained to model
music, we find that it generates novel and often highly realistic musical
fragments. We also show that it can be employed as a discriminative model,
returning promising results for phoneme recognition.
Fast Algorithms for Robust PCA via Gradient Descent
Authors: Xinyang Yi, Dohyung Park, Yudong Chen, Constantine Caramanis
Date: 2016-05-25 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Matrix Completion

We consider the problem of Robust PCA in the fully and partially observed
settings. Without corruptions, this is the well-known matrix completion
problem. From a statistical standpoint this problem has been recently
well-studied, and conditions on when recovery is possible (how many
observations do we need, how many corruptions can we tolerate) via
polynomial-time algorithms is by now understood. This paper presents and
analyzes a non-convex optimization approach that greatly reduces the
computational complexity of the above problems, compared to the best available
algorithms. In particular, in the fully observed case, with $r$ denoting rank
and $d$ dimension, we reduce the complexity from
$\mathcal{O}(r^2d^2\log(1/\varepsilon))$ to
$\mathcal{O}(rd^2\log(1/\varepsilon))$ -- a big savings when the rank is big.
For the partially observed case, we show the complexity of our algorithm is no
more than $\mathcal{O}(r^4d \log d \log(1/\varepsilon))$. Not only is this the
best-known run-time for a provable algorithm under partial observation, but in
the setting where $r$ is small compared to $d$, it also allows for
near-linear-in-$d$ run-time that can be exploited in the fully-observed case as
well, by simply running our algorithm on a subset of the observations.
Scope for Machine Learning in Digital Manufacturing
Authors: Martin Baumers, Ender Ozcan
Date: 2016-09-19 00:00:00
Tasks: BIG-bench Machine Learning

This provocation paper provides an overview of the underlying optimisation
problem in the emerging field of Digital Manufacturing. Initially, this paper
discusses how the notion of Digital Manufacturing is transforming from a term
describing a suite of software tools for the integration of production and
design functions towards a more general concept incorporating computerised
manufacturing and supply chain processes, as well as information collection and
utilisation across the product life cycle. On this basis, we use the example of
one such manufacturing process, Additive Manufacturing, to identify an
integrated multi-objective optimisation problem underlying Digital
Manufacturing. Forming an opportunity for a concurrent application of data
science and optimisation, a set of challenges arising from this problem is
outlined.
Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
Authors: Ergys Ristani, Francesco Solera, Roger S. Zou, Rita Cucchiara, Carlo Tomasi
Date: 2016-09-06 00:00:00

To help accelerate progress in multi-target, multi-camera tracking systems,
we present (i) a new pair of precision-recall measures of performance that
treats errors of all types uniformly and emphasizes correct identification over
sources of error; (ii) the largest fully-annotated and calibrated data set to
date with more than 2 million frames of 1080p, 60fps video taken by 8 cameras
observing more than 2,700 identities over 85 minutes; and (iii) a reference
software system as a comparison baseline. We show that (i) our measures
properly account for bottom-line identity match performance in the multi-camera
setting; (ii) our data set poses realistic challenges to current trackers; and
(iii) the performance of our system is comparable to the state of the art.
MUG: A Parameterless No-Reference JPEG Quality Evaluator Robust to Block Size and Misalignment
Authors: Hossein Ziaei Nafchi, Atena Shahkolaei, Rachid Hedjam, Mohamed Cheriet
Date: 2016-09-12 00:00:00
Tasks: Image Quality Assessment, No-Reference Image Quality Assessment, NR-IQA

In this letter, a very simple no-reference image quality assessment (NR-IQA)
model for JPEG compressed images is proposed. The proposed metric called median
of unique gradients (MUG) is based on the very simple facts of unique gradient
magnitudes of JPEG compressed images. MUG is a parameterless metric and does
not need training. Unlike other NR-IQAs, MUG is independent to block size and
cropping. A more stable index called MUG+ is also introduced. The experimental
results on six benchmark datasets of natural images and a benchmark dataset of
synthetic images show that MUG is comparable to the state-of-the-art indices in
literature. In addition, its performance remains unchanged for the case of the
cropped images in which block boundaries are not known. The MATLAB source code
of the proposed metrics is available at
https://dl.dropboxusercontent.com/u/74505502/MUG.m and
https://dl.dropboxusercontent.com/u/74505502/MUGplus.m.
Temporal Logic Programs with Variables
Authors: Felicidad Aguado, Pedro Cabalar, Martín Diéguez, Gilberto Pérez, Concepción Vidal
Date: 2016-09-19 00:00:00

In this note we consider the problem of introducing variables in temporal
logic programs under the formalism of "Temporal Equilibrium Logic" (TEL), an
extension of Answer Set Programming (ASP) for dealing with linear-time modal
operators. To this aim, we provide a definition of a first-order version of TEL
that shares the syntax of first-order Linear-time Temporal Logic (LTL) but has
a different semantics, selecting some LTL models we call "temporal stable
models". Then, we consider a subclass of theories (called "splittable temporal
logic programs") that are close to usual logic programs but allowing a
restricted use of temporal operators. In this setting, we provide a syntactic
definition of "safe variables" that suffices to show the property of "domain
independence" -- that is, addition of arbitrary elements in the universe does
not vary the set of temporal stable models. Finally, we present a method for
computing the derivable facts by constructing a non-temporal logic program with
variables that is fed to a standard ASP grounder. The information provided by
the grounder is then used to generate a subset of ground temporal rules which
is equivalent to (and generally smaller than) the full program instantiation.
Context-aware Sequential Recommendation
Authors: Qiang Liu, Shu Wu, Diyi Wang, Zhaokang Li, Liang Wang
Date: 2016-09-19 00:00:00
Tasks: Sequential Recommendation

Since sequential information plays an important role in modeling user
behaviors, various sequential recommendation methods have been proposed.
Methods based on Markov assumption are widely-used, but independently combine
several most recent components. Recently, Recurrent Neural Networks (RNN) based
methods have been successfully applied in several sequential modeling tasks.
However, for real-world applications, these methods have difficulty in modeling
the contextual information, which has been proved to be very important for
behavior modeling. In this paper, we propose a novel model, named Context-Aware
Recurrent Neural Networks (CA-RNN). Instead of using the constant input matrix
and transition matrix in conventional RNN models, CA-RNN employs adaptive
context-specific input matrices and adaptive context-specific transition
matrices. The adaptive context-specific input matrices capture external
situations where user behaviors happen, such as time, location, weather and so
on. And the adaptive context-specific transition matrices capture how lengths
of time intervals between adjacent behaviors in historical sequences affect the
transition of global sequential features. Experimental results show that the
proposed CA-RNN model yields significant improvements over state-of-the-art
sequential recommendation methods and context-aware recommendation methods on
two public datasets, i.e., the Taobao dataset and the Movielens-1M dataset.