pwc-archive/papers-with-abstracts huggingface dataset:
Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation
Authors: Mirko Polato, Fabio Aiolli
Date: 2016-12-17 00:00:00
Tasks: Collaborative Filtering

The increasing availability of implicit feedback datasets has raised the
interest in developing effective collaborative filtering techniques able to
deal asymmetrically with unambiguous positive feedback and ambiguous negative
feedback. In this paper, we propose a principled kernel-based collaborative
filtering method for top-N item recommendation with implicit feedback. We
present an efficient implementation using the linear kernel, and we show how to
generalize it to kernels of the dot product family preserving the efficiency.
We also investigate on the elements which influence the sparsity of a standard
cosine kernel. This analysis shows that the sparsity of the kernel strongly
depends on the properties of the dataset, in particular on the long tail
distribution. We compare our method with state-of-the-art algorithms achieving
good results both in terms of efficiency and effectiveness.
Microscopic Muscle Image Enhancement
Authors: Xiangfei Kong, Lin Yang
Date: 2016-12-17 00:00:00
Tasks: Deblurring, Image Deconvolution, Image Enhancement

We propose a robust image enhancement algorithm dedicated for muscle fiber
specimen images captured by optical microscopes. Blur or out of focus problems
are prevalent in muscle images during the image acquisition stage. Traditional
image deconvolution methods do not work since they assume the blur kernels are
known and also produce ring artifacts. We provide a compact framework which
involves a novel spatially non-uniform blind deblurring approach specialized to
muscle images which automatically detects and alleviates degraded regions. Ring
artifacts problems are addressed and a kernel propagation strategy is proposed
to speedup the algorithm and deals with the high non-uniformity of the blur
kernels on muscle images. Experiments show that the proposed framework performs
well on muscle images taken with modern advanced optical microscopes. Our
framework is free of laborious parameter settings and is computationally
efficient.
A Fusion Method Based on Decision Reliability Ratio for Finger Vein Verification
Authors: Liao Ni, Yi Zhang, He Zheng, Shilei Liu, Houjun Huang, Wenxin Li
Date: 2016-12-17 00:00:00

Finger vein verification has developed a lot since its first proposal, but
there is still not a perfect algorithm. It is proved that algorithms with the
same overall accuracy may have different misclassified patterns. We could make
use of this complementation to fuse individual algorithms together for more
precise result. According to our observation, algorithm has different
confidence on its decisions but it is seldom considered in fusion methods. Our
work is first to define decision reliability ratio to quantify this confidence,
and then propose the Maximum Decision Reliability Ratio (MDRR) fusion method
incorporating Weighted Voting. Experiment conducted on a data set of 1000
fingers and 5 images per finger proves the effectiveness of the method. The
classifier obtained by MDRR method gets an accuracy of 99.42% while the maximum
accuracy of the original individual classifiers is 97.77%. The experiment
results also show the MDRR outperforms the traditional fusion methods as
Voting, Weighted Voting, Sum and Weighted Sum.
A Way out of the Odyssey: Analyzing and Combining Recent Insights for LSTMs
Authors: Shayne Longpre, Sabeek Pradhan, Caiming Xiong, Richard Socher
Date: 2016-11-16 00:00:00
Tasks: General Classification, text-classification, Text Classification

LSTMs have become a basic building block for many deep NLP models. In recent
years, many improvements and variations have been proposed for deep sequence
models in general, and LSTMs in particular. We propose and analyze a series of
augmentations and modifications to LSTM networks resulting in improved
performance for text classification datasets. We observe compounding
improvements on traditional LSTMs using Monte Carlo test-time model averaging,
average pooling, and residual connections, along with four other suggested
modifications. Our analysis provides a simple, reliable, and high quality
baseline model.
Mutual information for fitting deep nonlinear models
Authors: Jacob S. Hunter, Nathan O. Hodas
Date: 2016-12-17 00:00:00

Deep nonlinear models pose a challenge for fitting parameters due to lack of
knowledge of the hidden layer and the potentially non-affine relation of the
initial and observed layers. In the present work we investigate the use of
information theoretic measures such as mutual information and Kullback-Leibler
(KL) divergence as objective functions for fitting such models without
knowledge of the hidden layer. We investigate one model as a proof of concept
and one application of cogntive performance. We further investigate the use of
optimizers with these methods. Mutual information is largely successful as an
objective, depending on the parameters. KL divergence is found to be similarly
succesful, given some knowledge of the statistics of the hidden layer.
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification
Authors: Ting Chen, Yizhou Sun
Date: 2016-12-08 00:00:00
Tasks: Feature Engineering, Network Embedding

In this paper, we study the problem of author identification under
double-blind review setting, which is to identify potential authors given
information of an anonymized paper. Different from existing approaches that
rely heavily on feature engineering, we propose to use network embedding
approach to address the problem, which can automatically represent nodes into
lower dimensional feature vectors. However, there are two major limitations in
recent studies on network embedding: (1) they are usually general-purpose
embedding methods, which are independent of the specific tasks; and (2) most of
these approaches can only deal with homogeneous networks, where the
heterogeneity of the network is ignored. Hence, challenges faced here are two
folds: (1) how to embed the network under the guidance of the author
identification task, and (2) how to select the best type of information due to
the heterogeneity of the network.
  To address the challenges, we propose a task-guided and path-augmented
heterogeneous network embedding model. In our model, nodes are first embedded
as vectors in latent feature space. Embeddings are then shared and jointly
trained according to task-specific and network-general objectives. We extend
the existing unsupervised network embedding to incorporate meta paths in
heterogeneous networks, and select paths according to the specific task. The
guidance from author identification task for network embedding is provided both
explicitly in joint training and implicitly during meta path selection. Our
experiments demonstrate that by using path-augmented network embedding with
task guidance, our model can obtain significantly better accuracy at
identifying the true authors comparing to existing methods.
Optimal Target Assignment and Path Finding for Teams of Agents
Authors: Hang Ma, Sven Koenig
Date: 2016-12-17 00:00:00
Tasks: Multi-Agent Path Finding

We study the TAPF (combined target-assignment and path-finding) problem for
teams of agents in known terrain, which generalizes both the anonymous and
non-anonymous multi-agent path-finding problems. Each of the teams is given the
same number of targets as there are agents in the team. Each agent has to move
to exactly one target given to its team such that all targets are visited. The
TAPF problem is to first assign agents to targets and then plan collision-free
paths for the agents to their targets in a way such that the makespan is
minimized. We present the CBM (Conflict-Based Min-Cost-Flow) algorithm, a
hierarchical algorithm that solves TAPF instances optimally by combining ideas
from anonymous and non-anonymous multi-agent path-finding algorithms. On the
low level, CBM uses a min-cost max-flow algorithm on a time-expanded network to
assign all agents in a single team to targets and plan their paths. On the high
level, CBM uses conflict-based search to resolve collisions among agents in
different teams. Theoretically, we prove that CBM is correct, complete and
optimal. Experimentally, we show the scalability of CBM to TAPF instances with
dozens of teams and hundreds of agents and adapt it to a simulated warehouse
system.
On the efficient representation and execution of deep acoustic models
Authors: Raziel Alvarez, Rohit Prabhavalkar, Anton Bakhtin
Date: 2016-07-15 00:00:00
Tasks: Quantization, speech-recognition, Speech Recognition

In this paper we present a simple and computationally efficient quantization
scheme that enables us to reduce the resolution of the parameters of a neural
network from 32-bit floating point values to 8-bit integer values. The proposed
quantization scheme leads to significant memory savings and enables the use of
optimized hardware instructions for integer arithmetic, thus significantly
reducing the cost of inference. Finally, we propose a "quantization aware"
training process that applies the proposed scheme during network training and
find that it allows us to recover most of the loss in accuracy introduced by
quantization. We validate the proposed techniques by applying them to a long
short-term memory-based acoustic model on an open-ended large vocabulary speech
recognition task.
Causal Learning via Manifold Regularization
Authors: Steven M. Hill, Chris. J. Oates, Duncan A. Blythe, Sach Mukherjee
Date: 2016-12-16 00:00:00
Tasks: Causal Discovery

This paper frames causal structure estimation as a machine learning task. The idea is to treat indicators of causal relationships between variables as `labels' and to exploit available data on the variables of interest to provide features for the labelling task. Background scientific knowledge or any available interventional data provide labels on some causal relationships and the remainder are treated as unlabelled. To illustrate the key ideas, we develop a distance-based approach (based on bivariate histograms) within a manifold regularization framework. We present empirical results on three different biological data sets (including examples where causal effects can be verified by experimental intervention), that together demonstrate the efficacy and general nature of the approach as well as its simplicity from a user's point of view.
Learning in Games: Robustness of Fast Convergence
Authors: Dylan J. Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Eva Tardos
Date: 2016-06-20 00:00:00
Proceeding: NeurIPS 2016 12

We show that learning algorithms satisfying a $\textit{low approximate
regret}$ property experience fast convergence to approximate optimality in a
large class of repeated games. Our property, which simply requires that each
learner has small regret compared to a $(1+\epsilon)$-multiplicative
approximation to the best action in hindsight, is ubiquitous among learning
algorithms; it is satisfied even by the vanilla Hedge forecaster. Our results
improve upon recent work of Syrgkanis et al. [SALS15] in a number of ways. We
require only that players observe payoffs under other players' realized
actions, as opposed to expected payoffs. We further show that convergence
occurs with high probability, and show convergence under bandit feedback.
Finally, we improve upon the speed of convergence by a factor of $n$, the
number of players. Both the scope of settings and the class of algorithms for
which our analysis provides fast convergence are considerably broader than in
previous work.
  Our framework applies to dynamic population games via a low approximate
regret property for shifting experts. Here we strengthen the results of
Lykouris et al. [LST16] in two ways: We allow players to select learning
algorithms from a larger class, which includes a minor variant of the basic
Hedge algorithm, and we increase the maximum churn in players for which
approximate optimality is achieved.
  In the bandit setting we present a new algorithm which provides a "small
loss"-type bound with improved dependence on the number of actions in utility
settings, and is both simple and efficient. This result may be of independent
interest.
A Data-Driven Compressive Sensing Framework Tailored For Energy-Efficient Wearable Sensing
Authors: Kai Xu, Yixing Li, Fengbo Ren
Date: 2016-12-15 00:00:00
Tasks: Compressive Sensing

Compressive sensing (CS) is a promising technology for realizing
energy-efficient wireless sensors for long-term health monitoring. However,
conventional model-driven CS frameworks suffer from limited compression ratio
and reconstruction quality when dealing with physiological signals due to
inaccurate models and the overlook of individual variability. In this paper, we
propose a data-driven CS framework that can learn signal characteristics and
personalized features from any individual recording of physiologic signals to
enhance CS performance with a minimized number of measurements. Such
improvements are accomplished by a co-training approach that optimizes the
sensing matrix and the dictionary towards improved restricted isometry property
and signal sparsity, respectively. Experimental results upon ECG signals show
that the proposed method, at a compression ratio of 10x, successfully reduces
the isometry constant of the trained sensing matrices by 86% against random
matrices and improves the overall reconstructed signal-to-noise ratio by 15dB
over conventional model-driven approaches.
Small Representations of Big Kidney Exchange Graphs
Authors: John P. Dickerson, Aleksandr M. Kazachkov, Ariel D. Procaccia, Tuomas Sandholm
Date: 2016-05-25 00:00:00

Kidney exchanges are organized markets where patients swap willing but
incompatible donors. In the last decade, kidney exchanges grew from small and
regional to large and national---and soon, international. This growth results
in more lives saved, but exacerbates the empirical hardness of the
$\mathcal{NP}$-complete problem of optimally matching patients to donors.
State-of-the-art matching engines use integer programming techniques to clear
fielded kidney exchanges, but these methods must be tailored to specific models
and objective functions, and may fail to scale to larger exchanges. In this
paper, we observe that if the kidney exchange compatibility graph can be
encoded by a constant number of patient and donor attributes, the clearing
problem is solvable in polynomial time. We give necessary and sufficient
conditions for losslessly shrinking the representation of an arbitrary
compatibility graph. Then, using real compatibility graphs from the UNOS
nationwide kidney exchange, we show how many attributes are needed to encode
real compatibility graphs. The experiments show that, indeed, small numbers of
attributes suffice.
Delta Networks for Optimized Recurrent Network Computation
Authors: Daniel Neil, Jun Haeng Lee, Tobi Delbruck, Shih-Chii Liu
Date: 2016-12-16 00:00:00
Proceeding: ICML 2017 8
Tasks: speech-recognition, Speech Recognition

Many neural networks exhibit stability in their activation patterns over time
in response to inputs from sensors operating under real-world conditions. By
capitalizing on this property of natural signals, we propose a Recurrent Neural
Network (RNN) architecture called a delta network in which each neuron
transmits its value only when the change in its activation exceeds a threshold.
The execution of RNNs as delta networks is attractive because their states must
be stored and fetched at every timestep, unlike in convolutional neural
networks (CNNs). We show that a naive run-time delta network implementation
offers modest improvements on the number of memory accesses and computes, but
optimized training techniques confer higher accuracy at higher speedup. With
these optimizations, we demonstrate a 9X reduction in cost with negligible loss
of accuracy for the TIDIGITS audio digit recognition benchmark. Similarly, on
the large Wall Street Journal speech recognition benchmark even existing
networks can be greatly accelerated as delta networks, and a 5.7x improvement
with negligible loss of accuracy can be obtained through training. Finally, on
an end-to-end CNN trained for steering angle prediction in a driving dataset,
the RNN cost can be reduced by a substantial 100X.
Scalable Computation of Optimized Queries for Sequential Diagnosis
Authors: Patrick Rodler, Wolfgang Schmid, Kostyantyn Shchekotykhin
Date: 2016-12-14 00:00:00
Tasks: Sequential Diagnosis

In many model-based diagnosis applications it is impossible to provide such a
set of observations and/or measurements that allow to identify the real cause
of a fault. Therefore, diagnosis systems often return many possible candidates,
leaving the burden of selecting the correct diagnosis to a user. Sequential
diagnosis techniques solve this problem by automatically generating a sequence
of queries to some oracle. The answers to these queries provide additional
information necessary to gradually restrict the search space by removing
diagnosis candidates inconsistent with the answers.
  During query computation, existing sequential diagnosis methods often require
the generation of many unnecessary query candidates and strongly rely on
expensive logical reasoners. We tackle this issue by devising efficient
heuristic query search methods. The proposed methods enable for the first time
a completely reasoner-free query generation while at the same time guaranteeing
optimality conditions, e.g. minimal cardinality or best understandability, of
the returned query that existing methods cannot realize. Hence, the performance
of this approach is independent of the (complexity of the) diagnosed system.
Experiments conducted using real-world problems show that the new approach is
highly scalable and outperforms existing methods by orders of magnitude.
Priors on exchangeable directed graphs
Authors: Diana Cai, Nathanael Ackerman, Cameron Freer
Date: 2015-10-28 00:00:00

Directed graphs occur throughout statistical modeling of networks, and
exchangeability is a natural assumption when the ordering of vertices does not
matter. There is a deep structural theory for exchangeable undirected graphs,
which extends to the directed case via measurable objects known as digraphons.
Using digraphons, we first show how to construct models for exchangeable
directed graphs, including special cases such as tournaments, linear orderings,
directed acyclic graphs, and partial orderings. We then show how to construct
priors on digraphons via the infinite relational digraphon model (di-IRM), a
new Bayesian nonparametric block model for exchangeable directed graphs, and
demonstrate inference on synthetic data.
A new cut-based genetic algorithm for graph partitioning applied to cell formation
Authors: Boulif Menouar
Date: 2016-12-16 00:00:00
Tasks: graph partitioning

Cell formation is a critical step in the design of cellular manufacturing
systems. Recently, it was tackled using a cut-based-graph-partitioning model.
This model meets real-life production systems requirements as it uses the
actual amount of product flows, it looks for the suitable number of cells, and
it takes into account the natural constraints such as operation sequences,
maximum cell size, cohabitation and non-cohabitation constraints. Based on this
model, we propose an original encoding representation to solve the problem by
using a genetic algorithm. We discuss the performance of this new GA in
comparison to some approaches taken from the literature on a set of medium
sized instances. Given the results we obtained, it is reasonable to assume that
the new GA will provide similar results for large real-life problems. Keywords:
Group Technology, Manufacturing Cell Formation, Graph Partitioning, Graph Cuts,
Genetic Algorithm, Encoding representation.
LipNet: End-to-End Sentence-level Lipreading
Authors: Yannis M. Assael, Brendan Shillingford, Shimon Whiteson, Nando de Freitas
Date: 2016-11-05 00:00:00
Tasks: General Classification, Lipreading, Sentence

Lipreading is the task of decoding text from the movement of a speaker's
mouth. Traditional approaches separated the problem into two stages: designing
or learning visual features, and prediction. More recent deep lipreading
approaches are end-to-end trainable (Wand et al., 2016; Chung & Zisserman,
2016a). However, existing work on models trained end-to-end perform only word
classification, rather than sentence-level sequence prediction. Studies have
shown that human lipreading performance increases for longer words (Easton &
Basala, 1982), indicating the importance of features capturing temporal context
in an ambiguous communication channel. Motivated by this observation, we
present LipNet, a model that maps a variable-length sequence of video frames to
text, making use of spatiotemporal convolutions, a recurrent network, and the
connectionist temporal classification loss, trained entirely end-to-end. To the
best of our knowledge, LipNet is the first end-to-end sentence-level lipreading
model that simultaneously learns spatiotemporal visual features and a sequence
model. On the GRID corpus, LipNet achieves 95.2% accuracy in sentence-level,
overlapped speaker split task, outperforming experienced human lipreaders and
the previous 86.4% word-level state-of-the-art accuracy (Gergen et al., 2016).
On the crucial impact of the coupling projector-backprojector in iterative tomographic reconstruction
Authors: Filippo Arcadu, Marco Stampanoni, Federica Marone
Date: 2016-12-16 00:00:00

The performance of an iterative reconstruction algorithm for X-ray tomography
is strongly determined by the features of the used forward and backprojector.
For this reason, a large number of studies has focused on the to design of
projectors with increasingly higher accuracy and speed. To what extent the
accuracy of an iterative algorithm is affected by the mathematical affinity and
the similarity between the actual implementation of the forward and
backprojection, referred here as "coupling projector-backprojector", has been
an overlooked aspect so far. The experimental study presented here shows that
the reconstruction quality and the convergence of an iterative algorithm
greatly rely on a good matching between the implementation of the tomographic
operators. In comparison, other aspects like the accuracy of the standalone
operators, the usage of physical constraints or the choice of stopping criteria
may even play a less relevant role.
Context-Aware Proactive Content Caching with Service Differentiation in Wireless Networks
Authors: Sabrina Müller, Onur Atan, Mihaela van der Schaar, Anja Klein
Date: 2016-06-14 00:00:00

Content caching in small base stations or wireless infostations is considered
to be a suitable approach to improve the efficiency in wireless content
delivery. Placing the optimal content into local caches is crucial due to
storage limitations, but it requires knowledge about the content popularity
distribution, which is often not available in advance. Moreover, local content
popularity is subject to fluctuations since mobile users with different
interests connect to the caching entity over time. Which content a user prefers
may depend on the user's context. In this paper, we propose a novel algorithm
for context-aware proactive caching. The algorithm learns context-specific
content popularity online by regularly observing context information of
connected users, updating the cache content and observing cache hits
subsequently. We derive a sublinear regret bound, which characterizes the
learning speed and proves that our algorithm converges to the optimal cache
content placement strategy in terms of maximizing the number of cache hits.
Furthermore, our algorithm supports service differentiation by allowing
operators of caching entities to prioritize customer groups. Our numerical
results confirm that our algorithm outperforms state-of-the-art algorithms in a
real world data set, with an increase in the number of cache hits of at least
14%.