pwc-archive/papers-with-abstracts huggingface dataset:
Deep CNN based feature extractor for text-prompted speaker recognition
Authors: Sergey Novoselov, Oleg Kudashev, Vadim Schemelinin, Ivan Kremnev, Galina Lavrentyeva
Date: 2018-03-13 00:00:00
Tasks: Speaker Recognition, Speaker Verification

Deep learning is still not a very common tool in speaker verification field.
We study deep convolutional neural network performance in the text-prompted
speaker verification task. The prompted passphrase is segmented into word
states - i.e. digits -to test each digit utterance separately. We train a
single high-level feature extractor for all states and use cosine similarity
metric for scoring. The key feature of our network is the Max-Feature-Map
activation function, which acts as an embedded feature selector. By using
multitask learning scheme to train the high-level feature extractor we were
able to surpass the classic baseline systems in terms of quality and achieved
impressive results for such a novice approach, getting 2.85% EER on the RSR2015
evaluation set. Fusion of the proposed and the baseline systems improves this
result.
Face Spoofing Detection by Fusing Binocular Depth and Spatial Pyramid Coding Micro-Texture Features
Authors: Xiao Song, Xu Zhao, Tianwei Lin
Date: 2018-03-13 00:00:00

Robust features are of vital importance to face spoofing detection, because
various situations make feature space extremely complicated to partition. Thus
in this paper, two novel and robust features for anti-spoofing are proposed.
The first one is a binocular camera based depth feature called Template Face
Matched Binocular Depth (TFBD) feature. The second one is a high-level
micro-texture based feature called Spatial Pyramid Coding Micro-Texture (SPMT)
feature. Novel template face registration algorithm and spatial pyramid coding
algorithm are also introduced along with the two novel features. Multi-modal
face spoofing detection is implemented based on these two robust features.
Experiments are conducted on a widely used dataset and a comprehensive dataset
constructed by ourselves. The results reveal that face spoofing detection with
the fusion of our proposed features is of strong robustness and time
efficiency, meanwhile outperforming other state-of-the-art traditional methods.
Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code
Authors: Nghi D. Q. Bui, Lingxiao Jiang
Date: 2018-03-13 00:00:00
Tasks: Translation, Word Embeddings

Translating a program written in one programming language to another can be
useful for software development tasks that need functionality implementations
in different languages. Although past studies have considered this problem,
they may be either specific to the language grammars, or specific to certain
kinds of code elements (e.g., tokens, phrases, API uses). This paper proposes a
new approach to automatically learn cross-language representations for various
kinds of structural code elements that may be used for program translation. Our
key idea is two folded: First, we normalize and enrich code token streams with
additional structural and semantic information, and train cross-language vector
representations for the tokens (a.k.a. shared embeddings based on word2vec, a
neural-network-based technique for producing word embeddings; Second,
hierarchically from bottom up, we construct shared embeddings for code elements
of higher levels of granularity (e.g., expressions, statements, methods) from
the embeddings for their constituents, and then build mappings among code
elements across languages based on similarities among embeddings.
  Our preliminary evaluations on about 40,000 Java and C# source files from 9
software projects show that our approach can automatically learn shared
embeddings for various code elements in different languages and identify their
cross-language mappings with reasonable Mean Average Precision scores. When
compared with an existing tool for mapping library API methods, our approach
identifies many more mappings accurately. The mapping results and code can be
accessed at
https://github.com/bdqnghi/hierarchical-programming-language-mapping. We
believe that our idea for learning cross-language vector representations with
code structural information can be a useful step towards automated program
translation.
3D Human Pose Estimation in RGBD Images for Robotic Task Learning
Authors: Christian Zimmermann, Tim Welschehold, Christian Dornhege, Wolfram Burgard, Thomas Brox
Date: 2018-03-07 00:00:00
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Pose Estimation

We propose an approach to estimate 3D human pose in real world units from a
single RGBD image and show that it exceeds performance of monocular 3D pose
estimation approaches from color as well as pose estimation exclusively from
depth. Our approach builds on robust human keypoint detectors for color images
and incorporates depth for lifting into 3D. We combine the system with our
learning from demonstration framework to instruct a service robot without the
need of markers. Experiments in real world settings demonstrate that our
approach enables a PR2 robot to imitate manipulation actions observed from a
human teacher.
Combination of Hidden Markov Random Field and Conjugate Gradient for Brain Image Segmentation
Authors: EL-Hachemi Guerrout, Samy Ait-Aoudia, Dominique Michelucci, Ramdane Mahiou
Date: 2017-05-13 00:00:00
Tasks: Brain Image Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation is the process of partitioning the image into significant
regions easier to analyze. Nowadays, segmentation has become a necessity in
many practical medical imaging methods as locating tumors and diseases. Hidden
Markov Random Field model is one of several techniques used in image
segmentation. It provides an elegant way to model the segmentation process.
This modeling leads to the minimization of an objective function. Conjugate
Gradient algorithm (CG) is one of the best known optimization techniques. This
paper proposes the use of the Conjugate Gradient algorithm (CG) for image
segmentation, based on the Hidden Markov Random Field. Since derivatives are
not available for this expression, finite differences are used in the CG
algorithm to approximate the first derivative. The approach is evaluated using
a number of publicly available images, where ground truth is known. The Dice
Coefficient is used as an objective criterion to measure the quality of
segmentation. The results show that the proposed CG approach compares favorably
with other variants of Hidden Markov Random Field segmentation algorithms.
Multimodal Recurrent Neural Networks with Information Transfer Layers for Indoor Scene Labeling
Authors: Abrar H. Abdulnabi, Bing Shuai, Zhen Zuo, Lap-Pui Chau, Gang Wang
Date: 2018-03-13 00:00:00
Tasks: Scene Labeling, Semantic Segmentation

This paper proposes a new method called Multimodal RNNs for RGB-D scene
semantic segmentation. It is optimized to classify image pixels given two input
sources: RGB color channels and Depth maps. It simultaneously performs training
of two recurrent neural networks (RNNs) that are crossly connected through
information transfer layers, which are learnt to adaptively extract relevant
cross-modality features. Each RNN model learns its representations from its own
previous hidden states and transferred patterns from the other RNNs previous
hidden states; thus, both model-specific and crossmodality features are
retained. We exploit the structure of quad-directional 2D-RNNs to model the
short and long range contextual information in the 2D input image. We carefully
designed various baselines to efficiently examine our proposed model structure.
We test our Multimodal RNNs method on popular RGB-D benchmarks and show how it
outperforms previous methods significantly and achieves competitive results
with other state-of-the-art works.
The Network Nullspace Property for Compressed Sensing of Big Data over Networks
Authors: Alexander Jung, Madelon Hulsebos
Date: 2017-05-11 00:00:00
Tasks: compressed sensing

We present a novel condition, which we term the net- work nullspace property,
which ensures accurate recovery of graph signals representing massive
network-structured datasets from few signal values. The network nullspace
property couples the cluster structure of the underlying network-structure with
the geometry of the sampling set. Our results can be used to design efficient
sampling strategies based on the network topology.
Hierarchical Reinforcement Learning: Approximating Optimal Discounted TSP Using Local Policies
Authors: Tom Zahavy, Avinatan Hasidim, Haim Kaplan, Yishay Mansour
Date: 2018-03-13 00:00:00
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Traveling Salesman Problem

In this work, we provide theoretical guarantees for reward decomposition in
deterministic MDPs. Reward decomposition is a special case of Hierarchical
Reinforcement Learning, that allows one to learn many policies in parallel and
combine them into a composite solution. Our approach builds on mapping this
problem into a Reward Discounted Traveling Salesman Problem, and then deriving
approximate solutions for it. In particular, we focus on approximate solutions
that are local, i.e., solutions that only observe information about the current
state. Local policies are easy to implement and do not require substantial
computational resources as they do not perform planning. While local
deterministic policies, like Nearest Neighbor, are being used in practice for
hierarchical reinforcement learning, we propose three stochastic policies that
guarantee better performance than any deterministic policy.
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection
Authors: Andy Brown, Aaron Tuor, Brian Hutchinson, Nicole Nichols
Date: 2018-03-13 00:00:00
Tasks: Anomaly Detection, Deep Learning, Intrusion Detection, Malware Detection

Deep learning has recently demonstrated state-of-the art performance on key
tasks related to the maintenance of computer systems, such as intrusion
detection, denial of service attack detection, hardware and software system
failures, and malware detection. In these contexts, model interpretability is
vital for administrator and analyst to trust and act on the automated analysis
of machine learning models. Deep learning methods have been criticized as black
box oracles which allow limited insight into decision factors. In this work we
seek to "bridge the gap" between the impressive performance of deep learning
models and the need for interpretable model introspection. To this end we
present recurrent neural network (RNN) language models augmented with attention
for anomaly detection in system logs. Our methods are generally applicable to
any computer system and logging source.
  By incorporating attention variants into our RNN language models we create
opportunities for model introspection and analysis without sacrificing
state-of-the art performance.
  We demonstrate model performance and illustrate model interpretability on an
intrusion detection task using the Los Alamos National Laboratory (LANL) cyber
security dataset, reporting upward of 0.99 area under the receiver operator
characteristic curve despite being trained only on a single day's worth of
data.
Dynamic Vision Sensors for Human Activity Recognition
Authors: Stefanie Anna Baby, Bimal Vinod, Chaitanya Chinni, Kaushik Mitra
Date: 2018-03-13 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Unlike conventional cameras which capture video at a fixed frame rate,
Dynamic Vision Sensors (DVS) record only changes in pixel intensity values. The
output of DVS is simply a stream of discrete ON/OFF events based on the
polarity of change in its pixel values. DVS has many attractive features such
as low power consumption, high temporal resolution, high dynamic range and
fewer storage requirements. All these make DVS a very promising camera for
potential applications in wearable platforms where power consumption is a major
concern.
  In this paper, we explore the feasibility of using DVS for Human Activity
Recognition (HAR). We propose to use the various slices (such as $x-y$, $x-t$,
and $y-t$) of the DVS video as a feature map for HAR and denote them as Motion
Maps. We show that fusing motion maps with Motion Boundary Histogram (MBH) give
good performance on the benchmark DVS dataset as well as on a real DVS gesture
dataset collected by us. Interestingly, the performance of DVS is comparable to
that of conventional videos although DVS captures only sparse motion
information.
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
Authors: Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung
Date: 2017-12-06 00:00:00
Proceeding: NAACL 2018 6
Tasks: Knowledge Base Completion, Link Prediction, valid

In this paper, we propose a novel embedding model, named ConvKB, for
knowledge base completion. Our model ConvKB advances state-of-the-art models by
employing a convolutional neural network, so that it can capture global
relationships and transitional characteristics between entities and relations
in knowledge bases. In ConvKB, each triple (head entity, relation, tail entity)
is represented as a 3-column matrix where each column vector represents a
triple element. This 3-column matrix is then fed to a convolution layer where
multiple filters are operated on the matrix to generate different feature maps.
These feature maps are then concatenated into a single feature vector
representing the input triple. The feature vector is multiplied with a weight
vector via a dot product to return a score. This score is then used to predict
whether the triple is valid or not. Experiments show that ConvKB achieves
better link prediction performance than previous state-of-the-art embedding
models on two benchmark datasets WN18RR and FB15k-237.
Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence
Authors: Maryam Aziz, Jesse Anderton, Emilie Kaufmann, Javed Aslam
Date: 2018-03-13 00:00:00

We consider the problem of near-optimal arm identification in the fixed
confidence setting of the infinitely armed bandit problem when nothing is known
about the arm reservoir distribution. We (1) introduce a PAC-like framework
within which to derive and cast results; (2) derive a sample complexity lower
bound for near-optimal arm identification; (3) propose an algorithm that
identifies a nearly-optimal arm with high probability and derive an upper bound
on its sample complexity which is within a log factor of our lower bound; and
(4) discuss whether our log^2(1/delta) dependence is inescapable for
"two-phase" (select arms first, identify the best later) algorithms in the
infinite setting. This work permits the application of bandit models to a
broader class of problems where fewer assumptions hold.
Multi-Player Bandits Revisited
Authors: Lilian Besson, Emilie Kaufmann
Date: 2017-11-07 00:00:00
Tasks: Multi-Armed Bandits

Multi-player Multi-Armed Bandits (MAB) have been extensively studied in the
literature, motivated by applications to Cognitive Radio systems. Driven by
such applications as well, we motivate the introduction of several levels of
feedback for multi-player MAB algorithms. Most existing work assume that
sensing information is available to the algorithm. Under this assumption, we
improve the state-of-the-art lower bound for the regret of any decentralized
algorithms and introduce two algorithms, RandTopM and MCTopM, that are shown to
empirically outperform existing algorithms. Moreover, we provide strong
theoretical guarantees for these algorithms, including a notion of asymptotic
optimality in terms of the number of selections of bad arms. We then introduce
a promising heuristic, called Selfish, that can operate without sensing
information, which is crucial for emerging applications to Internet of Things
networks. We investigate the empirical performance of this algorithm and
provide some first theoretical elements for the understanding of its behavior.
Binary Matrix Completion Using Unobserved Entries
Authors: Masayoshi Hayashi, Tomoya Sakai, Masashi Sugiyama
Date: 2018-03-13 00:00:00
Tasks: General Classification, Matrix Completion, Recommendation Systems

A matrix completion problem, which aims to recover a complete matrix from its
partial observations, is one of the important problems in the machine learning
field and has been studied actively. However, there is a discrepancy between
the mainstream problem setting, which assumes continuous-valued observations,
and some practical applications such as recommendation systems and SNS link
predictions where observations take discrete or even binary values. To cope
with this problem, Davenport et al. (2014) proposed a binary matrix completion
(BMC) problem, where observations are quantized into binary values. Hsieh et
al. (2015) proposed a PU (Positive and Unlabeled) matrix completion problem,
which is an extension of the BMC problem. This problem targets the setting
where we cannot observe negative values, such as SNS link predictions. In the
construction of their method for this setting, they introduced a methodology of
the classification problem, regarding each matrix entry as a sample. Their
risk, which defines losses over unobserved entries as well, indicates the
possibility of the use of unobserved entries. In this paper, motivated by a
semi-supervised classification method recently proposed by Sakai et al. (2017),
we develop a method for the BMC problem which can use all of positive,
negative, and unobserved entries, by combining the risks of Davenport et al.
(2014) and Hsieh et al. (2015). To the best of our knowledge, this is the first
BMC method which exploits all kinds of matrix entries. We experimentally show
that an appropriate mixture of risks improves the performance.
Simulation and Calibration of a Fully Bayesian Marked Multidimensional Hawkes Process with Dissimilar Decays
Authors: Kar Wai Lim, Young Lee, Leif Hanlen, Hongbiao Zhao
Date: 2018-03-13 00:00:00
Tasks: Point Processes

We propose a simulation method for multidimensional Hawkes processes based on
superposition theory of point processes. This formulation allows us to design
efficient simulations for Hawkes processes with differing exponentially
decaying intensities. We demonstrate that inter-arrival times can be decomposed
into simpler auxiliary variables that can be sampled directly, giving exact
simulation with no approximation. We establish that the auxiliary variables
provides information on the parent process for each event time. The algorithm
correctness is shown by verifying the simulated intensities with their
theoretical moments. A modular inference procedure consisting of Gibbs samplers
through the auxiliary variable augmentation and adaptive rejection sampling is
presented. Finally, we compare our proposed simulation method against existing
methods, and find significant improvement in terms of algorithm speed. Our
inference algorithm is used to discover the strengths of mutually excitations
in real dark networks.
On Cryptographic Attacks Using Backdoors for SAT
Authors: Alexander Semenov, Oleg Zaikin, Ilya Otpuschennikov, Stepan Kochemazov, Alexey Ignatiev
Date: 2018-03-13 00:00:00
Tasks: Cryptanalysis

Propositional satisfiability (SAT) is at the nucleus of state-of-the-art
approaches to a variety of computationally hard problems, one of which is
cryptanalysis. Moreover, a number of practical applications of SAT can only be
tackled efficiently by identifying and exploiting a subset of formula's
variables called backdoor set (or simply backdoors). This paper proposes a new
class of backdoor sets for SAT used in the context of cryptographic attacks,
namely guess-and-determine attacks. The idea is to identify the best set of
backdoor variables subject to a statistically estimated hardness of the
guess-and-determine attack using a SAT solver. Experimental results on weakened
variants of the renowned encryption algorithms exhibit advantage of the
proposed approach compared to the state of the art in terms of the estimated
hardness of the resulting guess-and-determine attacks.
Algorithmic learning of probability distributions from random data in the limit
Authors: George Barmpalias, Frank Stephan
Date: 2017-10-31 00:00:00
Tasks: Learning Theory

We study the problem of identifying a probability distribution for some given
randomly sampled data in the limit, in the context of algorithmic learning
theory as proposed recently by Vinanyi and Chater. We show that there exists a
computable partial learner for the computable probability measures, while by
Bienvenu, Monin and Shen it is known that there is no computable learner for
the computable probability measures. Our main result is the characterization of
the oracles that compute explanatory learners for the computable (continuous)
probability measures as the high oracles. This provides an analogue of a
well-known result of Adleman and Blum in the context of learning computable
probability distributions. We also discuss related learning notions such as
behaviorally correct learning and orther variations of explanatory learning, in
the context of learning probability distributions from data.
Thompson Sampling for Combinatorial Semi-Bandits
Authors: Siwei Wang, Wei Chen
Date: 2018-03-13 00:00:00
Proceeding: ICML 2018 7
Tasks: Thompson Sampling

In this paper, we study the application of the Thompson sampling (TS) methodology to the stochastic combinatorial multi-armed bandit (CMAB) framework. We first analyze the standard TS algorithm for the general CMAB model when the outcome distributions of all the base arms are independent, and obtain a distribution-dependent regret bound of $O(m\log K_{\max}\log T / \Delta_{\min})$, where $m$ is the number of base arms, $K_{\max}$ is the size of the largest super arm, $T$ is the time horizon, and $\Delta_{\min}$ is the minimum gap between the expected reward of the optimal solution and any non-optimal solution. This regret upper bound is better than the $O(m(\log K_{\max})^2\log T / \Delta_{\min})$ bound in prior works. Moreover, our novel analysis techniques can help to tighten the regret bounds of other existing UCB-based policies (e.g., ESCB), as we improve the method of counting the cumulative regret. Then we consider the matroid bandit setting (a special class of CMAB model), where we could remove the independence assumption across arms and achieve a regret upper bound that matches the lower bound. Except for the regret upper bounds, we also point out that one cannot directly replace the exact offline oracle (which takes the parameters of an offline problem instance as input and outputs the exact best action under this instance) with an approximation oracle in TS algorithm for even the classical MAB problem. Finally, we use some experiments to show the comparison between regrets of TS and other existing algorithms, the experimental results show that TS outperforms existing baselines.