pwc-archive/papers-with-abstracts huggingface dataset:
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets
Authors: Denis Steckelmacher, Diederik M. Roijers, Anna Harutyunyan, Peter Vrancx, Hélène Plisnier, Ann Nowé
Date: 2017-08-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Many real-world reinforcement learning problems have a hierarchical nature,
and often exhibit some degree of partial observability. While hierarchy and
partial observability are usually tackled separately (for instance by combining
recurrent neural networks and options), we show that addressing both problems
simultaneously is simpler and more efficient in many cases. More specifically,
we make the initiation set of options conditional on the previously-executed
option, and show that options with such Option-Observation Initiation Sets
(OOIs) are at least as expressive as Finite State Controllers (FSCs), a
state-of-the-art approach for learning in POMDPs. OOIs are easy to design based
on an intuitive description of the task, lead to explainable policies and keep
the top-level and option policies memoryless. Our experiments show that OOIs
allow agents to learn optimal policies in challenging POMDPs, while being much
more sample-efficient than a recurrent neural network over options.
Interpreting Shared Deep Learning Models via Explicable Boundary Trees
Authors: Huijun Wu, Chen Wang, Jie Yin, Kai Lu, Liming Zhu
Date: 2017-09-12 00:00:00
Tasks: Decision Making, Deep Learning

Despite outperforming the human in many tasks, deep neural network models are
also criticized for the lack of transparency and interpretability in decision
making. The opaqueness results in uncertainty and low confidence when deploying
such a model in model sharing scenarios, when the model is developed by a third
party. For a supervised machine learning model, sharing training process
including training data provides an effective way to gain trust and to better
understand model predictions. However, it is not always possible to share all
training data due to privacy and policy constraints. In this paper, we propose
a method to disclose a small set of training data that is just sufficient for
users to get the insight of a complicated model. The method constructs a
boundary tree using selected training data and the tree is able to approximate
the complicated model with high fidelity. We show that traversing data points
in the tree gives users significantly better understanding of the model and
paves the way for trustworthy model sharing.
Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies
Authors: Paolo Di Lorenzo, Paolo Banelli, Elvin Isufi, Sergio Barbarossa, Geert Leus
Date: 2017-09-12 00:00:00
Tasks: Graph Sampling

The goal of this paper is to propose novel strategies for adaptive learning
of signals defined over graphs, which are observed over a (randomly
time-varying) subset of vertices. We recast two classical adaptive algorithms
in the graph signal processing framework, namely, the least mean squares (LMS)
and the recursive least squares (RLS) adaptive estimation strategies. For both
methods, a detailed mean-square analysis illustrates the effect of random
sampling on the adaptive reconstruction capability and the steady-state
performance. Then, several probabilistic sampling strategies are proposed to
design the sampling probability at each node in the graph, with the aim of
optimizing the tradeoff between steady-state performance, graph sampling rate,
and convergence rate of the adaptive algorithms. Finally, a distributed RLS
strategy is derived and is shown to be convergent to its centralized
counterpart. Numerical simulations carried out over both synthetic and real
data illustrate the good performance of the proposed sampling and
reconstruction strategies for (possibly distributed) adaptive learning of
signals defined over graphs.
Supervised Deep Hashing for Hierarchical Labeled Data
Authors: Dan Wang, He-Yan Huang, Chi Lu, Bo-Si Feng, Liqiang Nie, Guihua Wen, Xian-Ling Mao
Date: 2017-04-07 00:00:00
Tasks: Deep Hashing, Image Retrieval, Retrieval

Recently, hashing methods have been widely used in large-scale image
retrieval. However, most existing hashing methods did not consider the
hierarchical relation of labels, which means that they ignored the rich
information stored in the hierarchy. Moreover, most of previous works treat
each bit in a hash code equally, which does not meet the scenario of
hierarchical labeled data. In this paper, we propose a novel deep hashing
method, called supervised hierarchical deep hashing (SHDH), to perform hash
code learning for hierarchical labeled data. Specifically, we define a novel
similarity formula for hierarchical labeled data by weighting each layer, and
design a deep convolutional neural network to obtain a hash code for each data
point. Extensive experiments on several real-world public datasets show that
the proposed method outperforms the state-of-the-art baselines in the image
retrieval task.
A low cost non-wearable gaze detection system based on infrared image processing
Authors: Ehsan Arbabi, Mohammad Shabani, Ali Yarigholi
Date: 2017-09-12 00:00:00

Human eye gaze detection plays an important role in various fields, including
human-computer interaction, virtual reality and cognitive science. Although
different relatively accurate systems of eye tracking and gaze detection exist,
they are usually either too expensive to be bought for low cost applications or
too complex to be implemented easily. In this article, we propose a
non-wearable system for eye tracking and gaze detection with low complexity and
cost. The proposed system provides a medium accuracy which makes it suitable
for general applications in which low cost and easy implementation is more
important than achieving very precise gaze detection. The proposed method
includes pupil and marker detection using infrared image processing, and gaze
evaluation using an interpolation-based strategy. The interpolation-based
strategy exploits the positions of the detected pupils and markers in a target
captured image and also in some previously captured training images for
estimating the position of a point that the user is gazing at. The proposed
system has been evaluated by three users in two different lighting conditions.
The experimental results show that the accuracy of this low cost system can be
between 90% and 100% for finding major gazing directions.
Evaluation of Classical Features and Classifiers in Brain-Computer Interface Tasks
Authors: Ehsan Arbabi, Mohammad Bagher Shamsollahi
Date: 2017-09-11 00:00:00
Tasks: Brain Computer Interface, General Classification

Brain-Computer Interface (BCI) uses brain signals in order to provide a new
method for communication between human and outside world. Feature extraction,
selection and classification are among the main matters of concerns in signal
processing stage of BCI. In this article, we present our findings about the
most effective features and classifiers in some brain tasks. Six different
groups of classical features and twelve classifiers have been examined in nine
datasets of brain signal. The results indicate that energy of brain signals in
{\alpha} and \b{eta} frequency bands, together with some statistical parameters
are more effective, comparing to the other types of extracted features. In
addition, Bayesian classifier with Gaussian distribution assumption and also
Support Vector Machine (SVM) show to classify different BCI datasets more
accurately than the other classifiers. We believe that the results can give an
insight about a strategy for blind classification of brain signals in
brain-computer interface.
RRA: Recurrent Residual Attention for Sequence Learning
Authors: Cheng Wang
Date: 2017-09-12 00:00:00
Tasks: Sentiment Analysis

In this paper, we propose a recurrent neural network (RNN) with residual
attention (RRA) to learn long-range dependencies from sequential data. We
propose to add residual connections across timesteps to RNN, which explicitly
enhances the interaction between current state and hidden states that are
several timesteps apart. This also allows training errors to be directly
back-propagated through residual connections and effectively alleviates
gradient vanishing problem. We further reformulate an attention mechanism over
residual connections. An attention gate is defined to summarize the individual
contribution from multiple previous hidden states in computing the current
state. We evaluate RRA on three tasks: the adding problem, pixel-by-pixel MNIST
classification and sentiment analysis on the IMDB dataset. Our experiments
demonstrate that RRA yields better performance, faster convergence and more
stable training compared to a standard LSTM network. Furthermore, RRA shows
highly competitive performance to the state-of-the-art methods.
PQk-means: Billion-scale Clustering for Product-quantized Codes
Authors: Yusuke Matsui, Keisuke Ogaki, Toshihiko Yamasaki, Kiyoharu Aizawa
Date: 2017-09-12 00:00:00
Tasks: Clustering

Data clustering is a fundamental operation in data analysis. For handling
large-scale data, the standard k-means clustering method is not only slow, but
also memory-inefficient. We propose an efficient clustering method for
billion-scale feature vectors, called PQk-means. By first compressing input
vectors into short product-quantized (PQ) codes, PQk-means achieves fast and
memory-efficient clustering, even for high-dimensional vectors. Similar to
k-means, PQk-means repeats the assignment and update steps, both of which can
be performed in the PQ-code domain. Experimental results show that even
short-length (32 bit) PQ-codes can produce competitive results compared with
k-means. This result is of practical importance for clustering in
memory-restricted environments. Using the proposed PQk-means scheme, the
clustering of one billion 128D SIFT features with K = 10^5 is achieved within
14 hours, using just 32 GB of memory consumption on a single computer.
A unified treatment of multiple testing with prior knowledge using the p-filter
Authors: Aaditya Ramdas, Rina Foygel Barber, Martin J. Wainwright, Michael. I. Jordan
Date: 2017-03-18 00:00:00

There is a significant literature on methods for incorporating knowledge into multiple testing procedures so as to improve their power and precision. Some common forms of prior knowledge include (a) beliefs about which hypotheses are null, modeled by non-uniform prior weights; (b) differing importances of hypotheses, modeled by differing penalties for false discoveries; (c) multiple arbitrary partitions of the hypotheses into (possibly overlapping) groups; and (d) knowledge of independence, positive or arbitrary dependence between hypotheses or groups, suggesting the use of more aggressive or conservative procedures. We present a unified algorithmic framework called p-filter for global null testing and false discovery rate (FDR) control that allows the scientist to incorporate all four types of prior knowledge (a)-(d) simultaneously, recovering a variety of known algorithms as special cases.
Automatic Ground Truths: Projected Image Annotations for Omnidirectional Vision
Authors: Victor Stamatescu, Peter Barsznica, Manjung Kim, Kin K. Liu, Mark McKenzie, Will Meakin, Gwilyn Saunders, Sebastien C. Wong, Russell S. A. Brinkworth
Date: 2017-09-12 00:00:00
Tasks: object-detection, Object Detection, Scene Understanding, Visual Tracking

We present a novel data set made up of omnidirectional video of multiple
objects whose centroid positions are annotated automatically. Omnidirectional
vision is an active field of research focused on the use of spherical imagery
in video analysis and scene understanding, involving tasks such as object
detection, tracking and recognition. Our goal is to provide a large and
consistently annotated video data set that can be used to train and evaluate
new algorithms for these tasks. Here we describe the experimental setup and
software environment used to capture and map the 3D ground truth positions of
multiple objects into the image. Furthermore, we estimate the expected
systematic error on the mapped positions. In addition to final data products,
we release publicly the software tools and raw data necessary to re-calibrate
the camera and/or redo this mapping. The software also provides a simple
framework for comparing the results of standard image annotation tools or
visual tracking systems against our mapped ground truth annotations.
A simple efficient density estimator that enables fast systematic search
Authors: Jonathan R. Wells, Kai Ming Ting
Date: 2017-07-03 00:00:00
Tasks: Small Data Image Classification

This paper introduces a simple and efficient density estimator that enables
fast systematic search. To show its advantage over commonly used kernel density
estimator, we apply it to outlying aspects mining. Outlying aspects mining
discovers feature subsets (or subspaces) that describe how a query stand out
from a given dataset. The task demands a systematic search of subspaces. We
identify that existing outlying aspects miners are restricted to datasets with
small data size and dimensions because they employ kernel density estimator,
which is computationally expensive, for subspace assessments. We show that a
recent outlying aspects miner can run orders of magnitude faster by simply
replacing its density estimator with the proposed density estimator, enabling
it to deal with large datasets with thousands of dimensions that would
otherwise be impossible.
Joint Dictionaries for Zero-Shot Learning
Authors: Soheil Kolouri, Mohammad Rostami, Yuri Owechko, Kyungnam Kim
Date: 2017-09-12 00:00:00
Tasks: Attribute, Dictionary Learning, Zero-Shot Learning

A classic approach toward zero-shot learning (ZSL) is to map the input domain
to a set of semantically meaningful attributes that could be used later on to
classify unseen classes of data (e.g. visual data). In this paper, we propose
to learn a visual feature dictionary that has semantically meaningful atoms.
Such dictionary is learned via joint dictionary learning for the visual domain
and the attribute domain, while enforcing the same sparse coding for both
dictionaries. Our novel attribute aware formulation provides an algorithmic
solution to the domain shift/hubness problem in ZSL. Upon learning the joint
dictionaries, images from unseen classes can be mapped into the attribute space
by finding the attribute aware joint sparse representation using solely the
visual data. We demonstrate that our approach provides superior or comparable
performance to that of the state of the art on benchmark datasets.
Comparative Benchmarking of Causal Discovery Techniques
Authors: Karamjit Singh, Garima Gupta, Vartika Tewari, Gautam Shroff
Date: 2017-08-18 00:00:00
Tasks: Benchmarking, Causal Discovery, counterfactual, Counterfactual Inference

In this paper we present a comprehensive view of prominent causal discovery
algorithms, categorized into two main categories (1) assuming acyclic and no
latent variables, and (2) allowing both cycles and latent variables, along with
experimental results comparing them from three perspectives: (a) structural
accuracy, (b) standard predictive accuracy, and (c) accuracy of counterfactual
inference. For (b) and (c) we train causal Bayesian networks with structures as
predicted by each causal discovery technique to carry out counterfactual or
standard predictive inference. We compare causal algorithms on two pub- licly
available and one simulated datasets having different sample sizes: small,
medium and large. Experiments show that structural accuracy of a technique does
not necessarily correlate with higher accuracy of inferencing tasks. Fur- ther,
surveyed structure learning algorithms do not perform well in terms of
structural accuracy in case of datasets having large number of variables.
Throughput Optimal Decentralized Scheduling of Multi-Hop Networks with End-to-End Deadline Constraints: II Wireless Networks with Interference
Authors: Rahul Singh, P. R. Kumar, Eytan Modiano
Date: 2017-09-06 00:00:00
Tasks: Scheduling

Consider a multihop wireless network serving multiple flows in which wireless
link interference constraints are described by a link interference graph. For
such a network, we design routing-scheduling policies that maximize the
end-to-end timely throughput of the network. Timely throughput of a flow $f$ is
defined as the average rate at which packets of flow $f$ reach their
destination node $d_f$ within their deadline.
  Our policy has several surprising characteristics. Firstly, we show that the
optimal routing-scheduling decision for an individual packet that is present at
a wireless node $i\in V$ is solely a function of its location, and "age". Thus,
a wireless node $i$ does not require the knowledge of the "global" network
state in order to maximize the timely throughput. We notice that in comparison,
under the backpressure routing policy, a node $i$ requires only the knowledge
of its neighbours queue lengths in order to guarantee maximal stability, and
hence is decentralized. The key difference arises due to the fact that in our
set-up the packets loose their utility once their "age" has crossed their
deadline, thus making the task of optimizing timely throughput much more
challenging than that of ensuring network stability. Of course, due to this key
difference, the decision process involved in maximizing the timely throughput
is also much more complex than that involved in ensuring network-wide queue
stabilization. In view of this, our results are somewhat surprising.
A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling
Authors: Yan Zhao, Xiao Fang, David Simchi-Levi
Date: 2017-09-12 00:00:00
Tasks: Decision Making

Randomized experiments have been critical tools of decision making for
decades. However, subjects can show significant heterogeneity in response to
treatments in many important applications. Therefore it is not enough to simply
know which treatment is optimal for the entire population. What we need is a
model that correctly customize treatment assignment base on subject
characteristics. The problem of constructing such models from randomized
experiments data is known as Uplift Modeling in the literature. Many algorithms
have been proposed for uplift modeling and some have generated promising
results on various data sets. Yet little is known about the theoretical
properties of these algorithms. In this paper, we propose a new tree-based
ensemble algorithm for uplift modeling. Experiments show that our algorithm can
achieve competitive results on both synthetic and industry-provided data. In
addition, by properly tuning the "node size" parameter, our algorithm is proved
to be consistent under mild regularity conditions. This is the first consistent
algorithm for uplift modeling that we are aware of.
Adversarial Discriminative Heterogeneous Face Recognition
Authors: Lingxiao Song, Man Zhang, Xiang Wu, Ran He
Date: 2017-09-12 00:00:00
Tasks: Face Hallucination, Face Recognition, Hallucination, Heterogeneous Face Recognition

The gap between sensing patterns of different face modalities remains a
challenging problem in heterogeneous face recognition (HFR). This paper
proposes an adversarial discriminative feature learning framework to close the
sensing gap via adversarial learning on both raw-pixel space and compact
feature space. This framework integrates cross-spectral face hallucination and
discriminative feature learning into an end-to-end adversarial network. In the
pixel space, we make use of generative adversarial networks to perform
cross-spectral face hallucination. An elaborate two-path model is introduced to
alleviate the lack of paired images, which gives consideration to both global
structures and local textures. In the feature space, an adversarial loss and a
high-order variance discrepancy loss are employed to measure the global and
local discrepancy between two heterogeneous distributions respectively. These
two losses enhance domain-invariant feature learning and modality independent
noise removing. Experimental results on three NIR-VIS databases show that our
proposed approach outperforms state-of-the-art HFR methods, without requiring
of complex network or large-scale training dataset.
Rapid Near-Neighbor Interaction of High-dimensional Data via Hierarchical Clustering
Authors: Nikos Pitsianis, Dimitris Floros, Alexandros-Stavros Iliopoulos, Kostas Mylonakis, Nikos Sismanis, Xiaobai Sun
Date: 2017-09-12 00:00:00
Tasks: Clustering, Vocal Bursts Intensity Prediction

Calculation of near-neighbor interactions among high dimensional, irregularly
distributed data points is a fundamental task to many graph-based or
kernel-based machine learning algorithms and applications. Such calculations,
involving large, sparse interaction matrices, expose the limitation of
conventional data-and-computation reordering techniques for improving space and
time locality on modern computer memory hierarchies. We introduce a novel
method for obtaining a matrix permutation that renders a desirable sparsity
profile. The method is distinguished by the guiding principle to obtain a
profile that is block-sparse with dense blocks. Our profile model and measure
capture the essential properties affecting space and time locality, and permit
variation in sparsity profile without imposing a restriction to a fixed
pattern. The second distinction lies in an efficient algorithm for obtaining a
desirable profile, via exploring and exploiting multi-scale cluster structure
hidden in but intrinsic to the data. The algorithm accomplishes its task with
key components for lower-dimensional embedding with data-specific principal
feature axes, hierarchical data clustering, multi-level matrix compression
storage, and multi-level interaction computations. We provide experimental
results from case studies with two important data analysis algorithms. The
resulting performance is remarkably comparable to the BLAS performance for the
best-case interaction governed by a regularly banded matrix with the same
sparsity.
Community Recovery in Hypergraphs
Authors: Kwangjun Ahn, Kangwook Lee, Changho Suh
Date: 2017-09-12 00:00:00
Tasks: Clustering, Face Clustering, Motion Segmentation

Community recovery is a central problem that arises in a wide variety of
applications such as network clustering, motion segmentation, face clustering
and protein complex detection. The objective of the problem is to cluster data
points into distinct communities based on a set of measurements, each of which
is associated with the values of a certain number of data points. While most of
the prior works focus on a setting in which the number of data points involved
in a measurement is two, this work explores a generalized setting in which the
number can be more than two. Motivated by applications particularly in machine
learning and channel coding, we consider two types of measurements: (1)
homogeneity measurement which indicates whether or not the associated data
points belong to the same community; (2) parity measurement which denotes the
modulo-2 sum of the values of the data points. Such measurements are possibly
corrupted by Bernoulli noise. We characterize the fundamental limits on the
number of measurements required to reconstruct the communities for the
considered models.