pwc-archive/papers-with-abstracts huggingface dataset:
Constraint Satisfaction Propagation: Non-stationary Policy Synthesis for Temporal Logic Planning
Authors: Thomas J. Ringstrom, Paul R. Schrater
Date: 2019-01-29 00:00:00

Problems arise when using reward functions to capture dependencies between
sequential time-constrained goal states because the state-space must be
prohibitively expanded to accommodate a history of successfully achieved
sub-goals. Also, policies and value functions derived with stationarity
assumptions are not readily decomposable, leading to a tension between reward
maximization and task generalization. We demonstrate a logic-compatible
approach using model-based knowledge of environment dynamics and deadline
information to directly infer non-stationary policies composed of reusable
stationary policies. The policies are constructed to maximize the probability
of satisfying time-sensitive goals while respecting time-varying obstacles. Our
approach explicitly maintains two different spaces, a high-level logical task
specification where the task-variables are grounded onto the low-level
state-space of a Markov decision process. Computing satisfiability at the
task-level is made possible by a Bellman-like equation which operates on a
tensor that links the temporal relationship between the two spaces; the
equation solves for a value function that can be explicitly interpreted as the
probability of sub-goal satisfaction under the synthesized non-stationary
policy, an approach we term Constraint Satisfaction Propagation (CSP).
Minimax-optimal decoding of movement goals from local field potentials using complex spectral features
Authors: Marko Angjelichinoski, Taposh Banerjee, John Choi, Bijan Pesaran, Vahid Tarokh
Date: 2019-01-29 00:00:00
Tasks: Decoder

We consider the problem of predicting eye movement goals from local field
potentials (LFP) recorded through a multielectrode array in the macaque
prefrontal cortex. The monkey is tasked with performing memory-guided saccades
to one of eight targets during which LFP activity is recorded and used to train
a decoder. Previous reports have mainly relied on the spectral amplitude of the
LFPs as a feature in the decoding step to limited success, while neglecting the
phase without proper theoretical justification. This paper formulates the
problem of decoding eye movement intentions in a statistically optimal
framework and uses Gaussian sequence modeling and Pinsker's theorem to generate
minimax-optimal estimates of the LFP signals which are later used as features
in the decoding step. The approach is shown to act as a low-pass filter and
each LFP in the feature space is represented via its complex Fourier
coefficients after appropriate shrinking such that higher frequency components
are attenuated; this way, the phase information inherently present in the LFP
signal is naturally embedded into the feature space. The proposed complex
spectrum-based decoder achieves prediction accuracy of up to $94\%$ at
superficial electrode depths near the surface of the prefrontal cortex, which
marks a significant performance improvement over conventional power
spectrum-based decoders.
On the Effect of Low-Rank Weights on Adversarial Robustness of Neural Networks
Authors: Peter Langenberg, Emilio Rafael Balda, Arash Behboodi, Rudolf Mathar
Date: 2019-01-29 00:00:00
Tasks: Adversarial Robustness

Recently, there has been an abundance of works on designing Deep Neural Networks (DNNs) that are robust to adversarial examples. In particular, a central question is which features of DNNs influence adversarial robustness and, therefore, can be to used to design robust DNNs. In this work, this problem is studied through the lens of compression which is captured by the low-rank structure of weight matrices. It is first shown that adversarial training tends to promote simultaneously low-rank and sparse structure in the weight matrices of neural networks. This is measured through the notions of effective rank and effective sparsity. In the reverse direction, when the low rank structure is promoted by nuclear norm regularization and combined with sparsity inducing regularizations, neural networks show significantly improved adversarial robustness. The effect of nuclear norm regularization on adversarial robustness is paramount when it is applied to convolutional neural networks. Although still not competing with adversarial training, this result contributes to understanding the key properties of robust classifiers.
Anomaly Locality in Video Surveillance
Authors: Federico Landi, Cees G. M. Snoek, Rita Cucchiara
Date: 2019-01-29 00:00:00
Tasks: Anomaly Detection

This paper strives for the detection of real-world anomalies such as
burglaries and assaults in surveillance videos. Although anomalies are
generally local, as they happen in a limited portion of the frame, none of the
previous works on the subject has ever studied the contribution of locality. In
this work, we explore the impact of considering spatiotemporal tubes instead of
whole-frame video segments. For this purpose, we enrich existing surveillance
videos with spatial and temporal annotations: it is the first dataset for
anomaly detection with bounding box supervision in both its train and test set.
Our experiments show that a network trained with spatiotemporal tubes performs
better than its analogous model trained with whole-frame videos. In addition,
we discover that the locality is robust to different kinds of errors in the
tube extraction phase at test time. Finally, we demonstrate that our network
can provide spatiotemporal proposals for unseen surveillance videos leveraging
only video-level labels. By doing, we enlarge our spatiotemporal anomaly
dataset without the need for further human labeling.
Computing Optimal Assignments in Linear Time for Approximate Graph Matching
Authors: Nils M. Kriege, Pierre-Louis Giscard, Franka Bause, Richard C. Wilson
Date: 2019-01-29 00:00:00
Tasks: Graph Matching

Finding an optimal assignment between two sets of objects is a fundamental problem arising in many applications, including the matching of `bag-of-words' representations in natural language processing and computer vision. Solving the assignment problem typically requires cubic time and its pairwise computation is expensive on large datasets. In this paper, we develop an algorithm which can find an optimal assignment in linear time when the cost function between objects is represented by a tree distance. We employ the method to approximate the edit distance between two graphs by matching their vertices in linear time. To this end, we propose two tree distances, the first of which reflects discrete and structural differences between vertices, and the second of which can be used to compare continuous labels. We verify the effectiveness and efficiency of our methods using synthetic and real-world datasets.
Stochastic Frank-Wolfe for Composite Convex Minimization
Authors: Francesco Locatello, Alp Yurtsever, Olivier Fercoq, Volkan Cevher
Date: 2019-01-29 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Stochastic Optimization

A broad class of convex optimization problems can be formulated as a semidefinite program (SDP), minimization of a convex function over the positive-semidefinite cone subject to some affine constraints. The majority of classical SDP solvers are designed for the deterministic setting where problem data is readily available. In this setting, generalized conditional gradient methods (aka Frank-Wolfe-type methods) provide scalable solutions by leveraging the so-called linear minimization oracle instead of the projection onto the semidefinite cone. Most problems in machine learning and modern engineering applications, however, contain some degree of stochasticity. In this work, we propose the first conditional-gradient-type method for solving stochastic optimization problems under affine constraints. Our method guarantees $\mathcal{O}(k^{-1/3})$ convergence rate in expectation on the objective residual and $\mathcal{O}(k^{-5/12})$ on the feasibility gap.
Quality Measures for Speaker Verification with Short Utterances
Authors: Arnab Poddar, Md Sahidullah, Goutam Saha
Date: 2019-01-29 00:00:00
Tasks: Speaker Recognition, Speaker Verification

The performances of the automatic speaker verification (ASV) systems degrade
due to the reduction in the amount of speech used for enrollment and
verification. Combining multiple systems based on different features and
classifiers considerably reduces speaker verification error rate with short
utterances. This work attempts to incorporate supplementary information during
the system combination process. We use quality of the estimated model
parameters as supplementary information. We introduce a class of novel quality
measures formulated using the zero-order sufficient statistics used during the
i-vector extraction process. We have used the proposed quality measures as side
information for combining ASV systems based on Gaussian mixture model-universal
background model (GMM-UBM) and i-vector. The proposed methods demonstrate
considerable improvement in speaker recognition performance on NIST SRE
corpora, especially in short duration conditions. We have also observed
improvement over existing systems based on different duration-based quality
measures.
A Robot for Nondestructive Assay of Holdup Deposits in Gaseous Diffusion Piping
Authors: Heather Jones, Siri Maley, Mohammadreza Mousaei, David Kohanbash, Warren Whittaker, James Teza, Andrew Zhang, Nikhil Jog, William Whittaker
Date: 2019-01-29 00:00:00

Miles of contaminated pipe must be measured, foot by foot, as part of the
decommissioning effort at deactivated gaseous diffusion enrichment facilities.
The current method requires cutting away asbestos-lined thermal enclosures and
performing repeated, elevated operations to manually measure pipe from the
outside. The RadPiper robot, part of the Pipe Crawling Activity Measurement
System (PCAMS) developed by Carnegie Mellon University and commissioned for use
at the DOE Portsmouth Gaseous Diffusion Enrichment Facility, automatically
measures U-235 in pipes from the inside. This improves certainty, increases
safety, and greatly reduces measurement time. The heart of the RadPiper robot
is a sodium iodide scintillation detector in an innovative disc-collimated
assembly. By measuring from inside pipes, the robot significantly increases its
count rate relative to external through-pipe measurements. The robot also
provides imagery, models interior pipe geometry, and precisely measures
distance in order to localize radiation measurements. Data collected by this
system provides insight into pipe interiors that is simply not possible from
exterior measurements, all while keeping operators safer. This paper describes
the technical details of the PCAMS RadPiper robot. Key features for this robot
include precision distance measurement, in-pipe obstacle detection, ability to
transform for two pipe sizes, and robustness in autonomous operation. Test
results demonstrating the robot's functionality are presented, including
deployment tolerance tests, safeguarding tests, and localization tests.
Integrated robot tests are also shown.
Limitations of Assessing Active Learning Performance at Runtime
Authors: Daniel Kottke, Jim Schellinger, Denis Huseljic, Bernhard Sick
Date: 2019-01-29 00:00:00
Tasks: Active Learning, General Classification

Classification algorithms aim to predict an unknown label (e.g., a quality
class) for a new instance (e.g., a product). Therefore, training samples
(instances and labels) are used to deduct classification hypotheses. Often, it
is relatively easy to capture instances but the acquisition of the
corresponding labels remain difficult or expensive. Active learning algorithms
select the most beneficial instances to be labeled to reduce cost. In research,
this labeling procedure is simulated and therefore a ground truth is available.
But during deployment, active learning is a one-shot problem and an evaluation
set is not available. Hence, it is not possible to reliably estimate the
performance of the classification system during learning and it is difficult to
decide when the system fulfills the quality requirements (stopping criteria).
In this article, we formalize the task and review existing strategies to assess
the performance of an actively trained classifier during training. Furthermore,
we identified three major challenges: 1)~to derive a performance distribution,
2)~to preserve representativeness of the labeled subset, and 3) to correct
against sampling bias induced by an intelligent selection strategy. In a
qualitative analysis, we evaluate different existing approaches and show that
none of them reliably estimates active learning performance stating a major
challenge for future research for such systems. All plots and experiments are
provided in a Jupyter notebook that is available for download.
Rank-one Convexification for Sparse Regression
Authors: Alper Atamturk, Andres Gomez
Date: 2019-01-29 00:00:00
Tasks: regression

Sparse regression models are increasingly prevalent due to their ease of interpretability and superior out-of-sample performance. However, the exact model of sparse regression with an $\ell_0$ constraint restricting the support of the estimators is a challenging (\NP-hard) non-convex optimization problem. In this paper, we derive new strong convex relaxations for sparse regression. These relaxations are based on the ideal (convex-hull) formulations for rank-one quadratic terms with indicator variables. The new relaxations can be formulated as semidefinite optimization problems in an extended space and are stronger and more general than the state-of-the-art formulations, including the perspective reformulation and formulations with the reverse Huber penalty and the minimax concave penalty functions. Furthermore, the proposed rank-one strengthening can be interpreted as a \textit{non-separable, non-convex, unbiased} sparsity-inducing regularizer, which dynamically adjusts its penalty according to the shape of the error function without inducing bias for the sparse solutions. In our computational experiments with benchmark datasets, the proposed conic formulations are solved within seconds and result in near-optimal solutions (with 0.4\% optimality gap) for non-convex $\ell_0$-problems. Moreover, the resulting estimators also outperform alternative convex approaches from a statistical perspective, achieving high prediction accuracy and good interpretability.
Who's Afraid of Adversarial Queries? The Impact of Image Modifications on Content-based Image Retrieval
Authors: Zhuoran Liu, Zhengyu Zhao, Martha Larson
Date: 2019-01-29 00:00:00
Tasks: Blocking, Content-Based Image Retrieval, Image Retrieval, Retrieval

An adversarial query is an image that has been modified to disrupt
content-based image retrieval (CBIR) while appearing nearly untouched to the
human eye. This paper presents an analysis of adversarial queries for CBIR
based on neural, local, and global features. We introduce an innovative neural
image perturbation approach, called Perturbations for Image Retrieval Error
(PIRE), that is capable of blocking neural-feature-based CBIR. PIRE differs
significantly from existing approaches that create images adversarial with
respect to CNN classifiers because it is unsupervised, i.e., it needs no
labelled data from the data set to which it is applied. Our experimental
analysis demonstrates the surprising effectiveness of PIRE in blocking CBIR,
and also covers aspects of PIRE that must be taken into account in practical
settings, including saving images, image quality and leaking adversarial
queries into the background collection. Our experiments also compare PIRE (a
neural approach) with existing keypoint removal and injection approaches (which
modify local features). Finally, we discuss the challenges that face multimedia
researchers in the future study of adversarial queries.
Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks
Authors: Okan Köpüklü, Ahmet Gunduz, Neslihan Kose, Gerhard Rigoll
Date: 2019-01-29 00:00:00
Tasks: Action Recognition, General Classification, Hand Gesture Recognition, Hand-Gesture Recognition

Real-time recognition of dynamic hand gestures from video streams is a challenging task since (i) there is no indication when a gesture starts and ends in the video, (ii) performed gestures should only be recognized once, and (iii) the entire architecture should be designed considering the memory and power budget. In this work, we address these challenges by proposing a hierarchical structure enabling offline-working convolutional neural network (CNN) architectures to operate online efficiently by using sliding window approach. The proposed architecture consists of two models: (1) A detector which is a lightweight CNN architecture to detect gestures and (2) a classifier which is a deep CNN to classify the detected gestures. In order to evaluate the single-time activations of the detected gestures, we propose to use Levenshtein distance as an evaluation metric since it can measure misclassifications, multiple detections, and missing detections at the same time. We evaluate our architecture on two publicly available datasets - EgoGesture and NVIDIA Dynamic Hand Gesture Datasets - which require temporal detection and classification of the performed hand gestures. ResNeXt-101 model, which is used as a classifier, achieves the state-of-the-art offline classification accuracy of 94.04% and 83.82% for depth modality on EgoGesture and NVIDIA benchmarks, respectively. In real-time detection and classification, we obtain considerable early detections while achieving performances close to offline operation. The codes and pretrained models used in this work are publicly available.
Trust Region-Guided Proximal Policy Optimization
Authors: Yuhui Wang, Hao He, Xiaoyang Tan, Yaozhong Gan
Date: 2019-01-29 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

Proximal policy optimization (PPO) is one of the most popular deep reinforcement learning (RL) methods, achieving state-of-the-art performance across a wide range of challenging tasks. However, as a model-free RL method, the success of PPO relies heavily on the effectiveness of its exploratory policy search. In this paper, we give an in-depth analysis on the exploration behavior of PPO, and show that PPO is prone to suffer from the risk of lack of exploration especially under the case of bad initialization, which may lead to the failure of training or being trapped in bad local optima. To address these issues, we proposed a novel policy optimization method, named Trust Region-Guided PPO (TRGPPO), which adaptively adjusts the clipping range within the trust region. We formally show that this method not only improves the exploration ability within the trust region but enjoys a better performance bound compared to the original PPO as well. Extensive experiments verify the advantage of the proposed method.
MultiLock: Mobile Active Authentication based on Multiple Biometric and Behavioral Patterns
Authors: Alejandro Acien, Aythami Morales, Ruben Vera-Rodriguez, Julian Fierrez
Date: 2019-01-29 00:00:00

In this paper we evaluate mobile active authentication based on an ensemble
of biometrics and behavior-based profiling signals. We consider seven different
data channels and their combination. Touch dynamics (touch gestures and
keystroking), accelerometer, gyroscope, WiFi, GPS location and app usage are
all collected during human-mobile interaction to authenticate the users. We
evaluate two approaches: one-time authentication and active authentication. In
one-time authentication, we employ the information of all channels available
during one session. For active authentication we take advantage of mobile user
behavior across multiple sessions by updating a confidence value of the
authentication score. Our experiments are conducted on the semi-uncontrolled
UMDAA-02 database. This database comprises smartphone sensor signals acquired
during natural human-mobile interaction. Our results show that different traits
can be complementary and multimodal systems clearly increase the performance
with accuracies ranging from 82.2% to 97.1% depending on the authentication
scenario.
Robust Learning from Untrusted Sources
Authors: Nikola Konstantinov, Christoph Lampert
Date: 2019-01-29 00:00:00
Tasks: Distributed Optimization, Learning Theory

Modern machine learning methods often require more data for training than a single expert can provide. Therefore, it has become a standard procedure to collect data from external sources, e.g. via crowdsourcing. Unfortunately, the quality of these sources is not always guaranteed. As additional complications, the data might be stored in a distributed way, or might even have to remain private. In this work, we address the question of how to learn robustly in such scenarios. Studying the problem through the lens of statistical learning theory, we derive a procedure that allows for learning from all available sources, yet automatically suppresses irrelevant or corrupted data. We show by extensive experiments that our method provides significant improvements over alternative approaches from robust statistics and distributed optimization.
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
Authors: Muhammad Ammad-Ud-Din, Elena Ivannikova, Suleiman A. Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, Adrian Flanagan
Date: 2019-01-29 00:00:00
Tasks: BIG-bench Machine Learning, Collaborative Filtering, Federated Learning, Privacy Preserving

The increasing interest in user privacy is leading to new privacy preserving
machine learning paradigms. In the Federated Learning paradigm, a master
machine learning model is distributed to user clients, the clients use their
locally stored data and model for both inference and calculating model updates.
The model updates are sent back and aggregated on the server to update the
master model then redistributed to the clients. In this paradigm, the user data
never leaves the client, greatly enhancing the user' privacy, in contrast to
the traditional paradigm of collecting, storing and processing user data on a
backend server beyond the user's control. In this paper we introduce, as far as
we are aware, the first federated implementation of a Collaborative Filter. The
federated updates to the model are based on a stochastic gradient approach. As
a classical case study in machine learning, we explore a personalized
recommendation system based on users' implicit feedback and demonstrate the
method's applicability to both the MovieLens and an in-house dataset. Empirical
validation confirms a collaborative filter can be federated without a loss of
accuracy compared to a standard implementation, hence enhancing the user's
privacy in a widely used recommender application while maintaining recommender
performance.