pwc-archive/papers-with-abstracts huggingface dataset:
Copying Machine Learning Classifiers
Authors: Irene Unceta, Jordi Nin, Oriol Pujol
Date: 2019-03-05 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

We study model-agnostic copies of machine learning classifiers. We develop the theory behind the problem of copying, highlighting its differences with that of learning, and propose a framework to copy the functionality of any classifier using no prior knowledge of its parameters or training data distribution. We identify the different sources of loss and provide guidelines on how best to generate synthetic sets for the copying process. We further introduce a set of metrics to evaluate copies in practice. We validate our framework through extensive experiments using data from a series of well-known problems. We demonstrate the value of copies in use cases where desiderata such as interpretability, fairness or productivization constrains need to be addressed. Results show that copies can be exploited to enhance existing solutions and improve them adding new features and characteristics.
Bipolar in Temporal Argumentation Framework
Authors: Maximiliano C. D. Budán, Maria Laura Cobo, Diego C. Martinez, Guillermo R. Simari
Date: 2019-03-05 00:00:00
Tasks: valid

A Timed Argumentation Framework (TAF) is a formalism where arguments are only
valid for consideration in a given period of time, called availability
intervals, which are defined for every individual argument. The original
proposal is based on a single, abstract notion of attack between arguments that
remains static and permanent in time. Thus, in general, when identifying the
set of acceptable arguments, the outcome associated with a TAF will vary over
time. In this work we introduce an extension of TAF adding the capability of
modeling a support relation between arguments. In this sense, the resulting
framework provides a suitable model for different time-dependent issues. Thus,
the main contribution here is to provide an enhanced framework for modeling a
positive (support) and negative (attack) interaction varying over time, which
are relevant in many real-world situations. This leads to a Timed Bipolar
Argumentation Framework (T-BAF), where classical argument extensions can be
defined. The proposal aims at advancing in the integration of temporal
argumentation in different application domain.
Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series
Authors: Babak Hosseini, Barbara Hammer
Date: 2019-03-05 00:00:00
Tasks: Clustering, Dictionary Learning, Online Clustering, Time Series, Time Series Analysis

There exist many approaches for description and recognition of unseen classes
in datasets. Nevertheless, it becomes a challenging problem when we deal with
multivariate time-series (MTS) (e.g., motion data), where we cannot apply the
vectorial algorithms directly to the inputs. In this work, we propose a novel
multiple-kernel dictionary learning (MKD) which learns semantic attributes
based on specific combinations of MTS dimensions in the feature space. Hence,
MKD can fully/partially reconstructs the unseen classes based on the training
data (seen classes). Furthermore, we obtain sparse encodings for unseen classes
based on the learned MKD attributes, and upon which we propose a simple but
effective incremental clustering algorithm to categorize the unseen MTS classes
in an unsupervised way. According to the empirical evaluation of our MKD
framework on real benchmarks, it provides an interpretable reconstruction of
unseen MTS data as well as a high performance regarding their online
clustering.
An Approach to Characterize Graded Entailment of Arguments through a Label-based Framework
Authors: Maximiliano C. D. Budán, Gerardo I. Simari, Ignacio Viglizzo, Guillermo R. Simari
Date: 2019-03-05 00:00:00

Argumentation theory is a powerful paradigm that formalizes a type of
commonsense reasoning that aims to simulate the human ability to resolve a
specific problem in an intelligent manner. A classical argumentation process
takes into account only the properties related to the intrinsic logical
soundness of an argument in order to determine its acceptability status.
However, these properties are not always the only ones that matter to establish
the argument's acceptability---there exist other qualities, such as strength,
weight, social votes, trust degree, relevance level, and certainty degree,
among others.
Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection
Authors: Zhixin Wang, Kui Jia
Date: 2019-03-05 00:00:00
Tasks: 3D Object Detection, object-detection, Object Detection, Region Proposal

In this work, we propose a novel method termed \emph{Frustum ConvNet (F-ConvNet)} for amodal 3D object detection from point clouds. Given 2D region proposals in an RGB image, our method first generates a sequence of frustums for each region proposal, and uses the obtained frustums to group local points. F-ConvNet aggregates point-wise features as frustum-level feature vectors, and arrays these feature vectors as a feature map for use of its subsequent component of fully convolutional network (FCN), which spatially fuses frustum-level features and supports an end-to-end and continuous estimation of oriented boxes in the 3D space. We also propose component variants of F-ConvNet, including an FCN variant that extracts multi-resolution frustum features, and a refined use of F-ConvNet over a reduced 3D space. Careful ablation studies verify the efficacy of these component variants. F-ConvNet assumes no prior knowledge of the working 3D environment and is thus dataset-agnostic. We present experiments on both the indoor SUN-RGBD and outdoor KITTI datasets. F-ConvNet outperforms all existing methods on SUN-RGBD, and at the time of submission it outperforms all published works on the KITTI benchmark. Code has been made available at: {\url{https://github.com/zhixinwang/frustum-convnet}.}
Virtual Ground Truth, and Pre-selection of 3D Interest Points for Improved Repeatability Evaluation of 2D Detectors
Authors: Simon R Lang, Martin H Luerssen, David M Powers
Date: 2019-03-05 00:00:00
Tasks: General Classification

In Computer Vision, finding simple features is performed using classifiers
called interest point (IP) detectors, which are often utilised to track
features as the scene changes. For 2D based classifiers it has been intuitive
to measure repeated point reliability using 2D metrics given the difficulty to
establish ground truth beyond 2D. The aim is to bridge the gap between 2D
classifiers and 3D environments, and improve performance analysis of 2D IP
classification on 3D objects. This paper builds on existing work with 3D
scanned and artificial models to test conventional 2D feature detectors with
the assistance of virtualised 3D scenes. Virtual space depth is leveraged in
tests to perform pre-selection of closest repeatable points in both 2D and 3D
contexts before repeatability is measured. This more reliable ground truth is
used to analyse testing configurations with a singular and 12 model dataset
across affine transforms in x, y and z rotation, as well as x,y scaling with 9
well known IP detectors. The virtual scene's ground truth demonstrates that 3D
pre-selection eliminates a large portion of false positives that are normally
considered repeated in 2D configurations. The results indicate that 3D virtual
environments can provide assistance in comparing the performance of
conventional detectors when extending their applications to 3D environments,
and can result in better classification of features when testing prospective
classifiers' performance. A ROC based informedness measure also highlights
tradeoffs in 2D/3D performance compared to conventional repeatability measures.
Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization
Authors: Le Thi Khanh Hien, Nicolas Gillis, Panagiotis Patrinos
Date: 2019-03-05 00:00:00
Proceeding: ICML 2020 1

We propose inertial versions of block coordinate descent methods for solving non-convex non-smooth composite optimization problems. Our methods possess three main advantages compared to current state-of-the-art accelerated first-order methods: (1) they allow using two different extrapolation points to evaluate the gradients and to add the inertial force (we will empirically show that it is more efficient than using a single extrapolation point), (2) they allow to randomly picking the block of variables to update, and (3) they do not require a restarting step. We prove the subsequential convergence of the generated sequence under mild assumptions, prove the global convergence under some additional assumptions, and provide convergence rates. We deploy the proposed methods to solve non-negative matrix factorization (NMF) and show that they compete favorably with the state-of-the-art NMF algorithms. Additional experiments on non-negative approximate canonical polyadic decomposition, also known as non-negative tensor factorization, are also provided.
HexagDLy - Processing hexagonally sampled data with CNNs in PyTorch
Authors: Constantin Steppa, Tim Lukas Holch
Date: 2019-03-05 00:00:00

HexagDLy is a Python-library extending the PyTorch deep learning framework
with convolution and pooling operations on hexagonal grids. It aims to ease the
access to convolutional neural networks for applications that rely on
hexagonally sampled data as, for example, commonly found in ground-based
astroparticle physics experiments.
Towards Design Space Exploration and Optimization of Fast Algorithms for Convolutional Neural Networks (CNNs) on FPGAs
Authors: Afzal Ahmad, Muhammad Adeel Pasha
Date: 2019-03-05 00:00:00

Convolutional Neural Networks (CNNs) have gained widespread popularity in the
field of computer vision and image processing. Due to huge computational
requirements of CNNs, dedicated hardware-based implementations are being
explored to improve their performance. Hardware platforms such as Field
Programmable Gate Arrays (FPGAs) are widely being used to design parallel
architectures for this purpose. In this paper, we analyze Winograd minimal
filtering or fast convolution algorithms to reduce the arithmetic complexity of
convolutional layers of CNNs. We explore a complex design space to find the
sets of parameters that result in improved throughput and power-efficiency. We
also design a pipelined and parallel Winograd convolution engine that improves
the throughput and power-efficiency while reducing the computational complexity
of the overall system. Our proposed designs show up to 4.75$\times$ and
1.44$\times$ improvements in throughput and power-efficiency, respectively, in
comparison to the state-of-the-art design while using approximately
2.67$\times$ more multipliers. Furthermore, we obtain savings of up to 53.6\%
in logic resources compared with the state-of-the-art implementation.
Robot Localization in Floor Plans Using a Room Layout Edge Extraction Network
Authors: Federico Boniardi, Abhinav Valada, Rohit Mohan, Tim Caselitz, Wolfram Burgard
Date: 2019-03-05 00:00:00
Tasks: Indoor Localization

Indoor localization is one of the crucial enablers for deployment of service robots. Although several successful techniques for indoor localization have been proposed, the majority of them relies on maps generated from data gathered with the same sensor modality used for localization. Typically, tedious labor by experts is needed to acquire this data, thus limiting the readiness of the system as well as its ease of installation for inexperienced operators. In this paper, we propose a memory and computationally efficient monocular camera-based localization system that allows a robot to estimate its pose given an architectural floor plan. Our method employs a convolutional neural network to predict room layout edges from a single camera image and estimates the robot pose using a particle filter that matches the extracted edges to the given floor plan. We evaluate our localization system using multiple real-world experiments and demonstrate that it has the robustness and accuracy required for reliable indoor navigation.
Leveraging Shape Completion for 3D Siamese Tracking
Authors: Silvio Giancola, Jesus Zarzar, Bernard Ghanem
Date: 2019-03-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Object Tracking, Autonomous Vehicles, Object, Object Tracking

Point clouds are challenging to process due to their sparsity, therefore
autonomous vehicles rely more on appearance attributes than pure geometric
features. However, 3D LIDAR perception can provide crucial information for
urban navigation in challenging light or weather conditions. In this paper, we
investigate the versatility of Shape Completion for 3D Object Tracking in LIDAR
point clouds. We design a Siamese tracker that encodes model and candidate
shapes into a compact latent representation. We regularize the encoding by
enforcing the latent representation to decode into an object model shape. We
observe that 3D object tracking and 3D shape completion complement each other.
Learning a more meaningful latent representation shows better discriminatory
capabilities, leading to improved tracking performance. We test our method on
the KITTI Tracking set using car 3D bounding boxes. Our model reaches a 76.94%
Success rate and 81.38% Precision for 3D Object Tracking, with the shape
completion regularization leading to an improvement of 3% in both metrics.
A New Approach to Adaptive Data Analysis and Learning via Maximal Leakage
Authors: Amedeo Roberto Esposito, Michael Gastpar, Ibrahim Issa
Date: 2019-03-05 00:00:00

There is an increasing concern that most current published research findings
are false. The main cause seems to lie in the fundamental disconnection between
theory and practice in data analysis. While the former typically relies on
statistical independence, the latter is an inherently adaptive process: new
hypotheses are formulated based on the outcomes of previous analyses. A recent
line of work tries to mitigate these issues by enforcing constraints, such as
differential privacy, that compose adaptively while degrading gracefully and
thus provide statistical guarantees even in adaptive contexts. Our contribution
consists in the introduction of a new approach, based on the concept of Maximal
Leakage, an information-theoretic measure of leakage of information. The main
result allows us to compare the probability of an event happening when
adaptivity is considered with respect to the non-adaptive scenario. The bound
we derive represents a generalization of the bounds used in non-adaptive
scenarios (e.g., McDiarmid's inequality for $c$-sensitive functions, false
discovery error control via significance level, etc.), and allows us to
replicate or even improve, in certain regimes, the results obtained using
Max-Information or Differential Privacy. In contrast with the line of work
started by Dwork et al., our results do not rely on Differential Privacy but
are, in principle, applicable to every algorithm that has a bounded leakage,
including the differentially private algorithms and the ones with a short
description length.
Visualisation of Pareto Front Approximation: A Short Survey and Empirical Comparisons
Authors: Huiru Gao, Haifeng Nie, Ke Li
Date: 2019-03-05 00:00:00
Tasks: Decision Making

Visualisation is an effective way to facilitate the analysis and
understanding of multivariate data. In the context of multi-objective
optimisation, comparing to quantitative performance metrics, visualisation is,
in principle, able to provide a decision maker better insights about Pareto
front approximation sets (e.g. the distribution of solutions, the geometric
characteristics of Pareto front approximation) thus to facilitate the
decision-making (e.g. the exploration of trade-off relationship, the knee
region or region of interest). In this paper, we overview some currently
prevalent visualisation techniques according to the way how data is
represented. To have a better understanding of the pros and cons of different
visualisation techniques, we empirically compare six representative
visualisation techniques for the exploratory analysis of different Pareto front
approximation sets obtained by four state-of-the-art evolutionary
multi-objective optimisation algorithms on the classic DTLZ benchmark test
problems. From the empirical results, we find that visual comparisons also
follow the \textit{No-Free-Lunch} theorem where no single visualisation
technique is able to provide a comprehensive understanding of the
characteristics of a Pareto front approximation set. In other words, a specific
type of visualisation technique is only good at exploring a particular aspect
of the data.
Towards Understanding Chinese Checkers with Heuristics, Monte Carlo Tree Search, and Deep Reinforcement Learning
Authors: Ziyu Liu, Meng Zhou, Weiqing Cao, Qiang Qu, Henry Wing Fung Yeung, Vera Yuk Ying Chung
Date: 2019-03-05 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

The game of Chinese Checkers is a challenging traditional board game of
perfect information that differs from other traditional games in two main
aspects: first, unlike Chess, all checkers remain indefinitely in the game and
hence the branching factor of the search tree does not decrease as the game
progresses; second, unlike Go, there are also no upper bounds on the depth of
the search tree since repetitions and backward movements are allowed.
Therefore, even in a restricted game instance, the state-space of the game can
still be unbounded, making it challenging for a computer program to excel. In
this work, we present an approach that effectively combines the use of
heuristics, Monte Carlo tree search, and deep reinforcement learning for
building a Chinese Checkers agent without the use of any human game-play data.
Experiment results show that our agent is competent under different scenarios
and reaches the level of experienced human players.
How to Prove Your Model Belongs to You: A Blind-Watermark based Framework to Protect Intellectual Property of DNN
Authors: Zheng Li, Chengyu Hu, Yang Zhang, Shanqing Guo
Date: 2019-03-05 00:00:00
Tasks: Machine Translation

Deep learning techniques have made tremendous progress in a variety of challenging tasks, such as image recognition and machine translation, during the past decade. Training deep neural networks is computationally expensive and requires both human and intellectual resources. Therefore, it is necessary to protect the intellectual property of the model and externally verify the ownership of the model. However, previous studies either fail to defend against the evasion attack or have not explicitly dealt with fraudulent claims of ownership by adversaries. Furthermore, they can not establish a clear association between the model and the creator's identity. To fill these gaps, in this paper, we propose a novel intellectual property protection (IPP) framework based on blind-watermark for watermarking deep neural networks that meet the requirements of security and feasibility. Our framework accepts ordinary samples and the exclusive logo as inputs, outputting newly generated samples as watermarks, which are almost indistinguishable from the origin, and infuses these watermarks into DNN models by assigning specific labels, leaving the backdoor as the basis for our copyright claim. We evaluated our IPP framework on two benchmark datasets and 15 popular deep learning models. The results show that our framework successfully verifies the ownership of all the models without a noticeable impact on their primary task. Most importantly, we are the first to successfully design and implement a blind-watermark based framework, which can achieve state-of-art performances on undetectability against evasion attack and unforgeability against fraudulent claims of ownership. Further, our framework shows remarkable robustness and establishes a clear association between the model and the author's identity.
Hue Modification Localization By Pair Matching
Authors: Quoc-Tin Phan, Michele Vascotto, Giulia Boato
Date: 2019-03-05 00:00:00
Tasks: Demosaicking, Patch Matching

Hue modification is the adjustment of hue property on color images.
Conducting hue modification on an image is trivial, and it can be abused to
falsify opinions of viewers. Since shapes, edges or textural information
remains unchanged after hue modification, this type of manipulation is
relatively hard to be detected and localized. Since small patches inherit the
same Color Filter Array (CFA) configuration and demosaicing, any distortion
made by local hue modification can be detected by patch matching within the
same image. In this paper, we propose to localize hue modification by means of
a Siamese neural network specifically designed for matching two inputs. By
crafting the network outputs, we are able to form a heatmap which potentially
highlights malicious regions. Our proposed method deals well not only with
uncompressed images but also with the presence of JPEG compression, an
operation usually hindering the exploitation of CFA and demosaicing artifacts.
Experimental evidences corroborate the effectiveness of the proposed method.
A Novel Efficient Approach with Data-Adaptive Capability for OMP-based Sparse Subspace Clustering
Authors: Jiaqiyu Zhan, Zhiqiang Bai, Yuesheng Zhu
Date: 2019-03-05 00:00:00
Tasks: Clustering

Orthogonal Matching Pursuit (OMP) plays an important role in data science and its applications such as sparse subspace clustering and image processing. However, the existing OMP-based approaches lack of data adaptiveness so that the data cannot be represented well enough and may lose the accuracy. This paper proposes a novel approach to enhance the data-adaptive capability for OMP-based sparse subspace clustering. In our method a parameter selection process is developed to adjust the parameters based on the data distribution for information representation. Our theoretical analysis indicates that the parameter selection process can efficiently coordinate with any OMP-based methods to improve the clustering performance. Also a new Self-Expressive-Affinity (SEA) ratio metric is defined to measure the sparse representation conversion efficiency for spectral clustering to obtain data segmentations. Our experiments show that proposed approach can achieve better performances compared with other OMP-based sparse subspace clustering algorithms in terms of clustering accuracy, SEA ratio and representation quality, also keep the time efficiency and anti-noise ability.
Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification
Authors: Rémi Domingues
Date: 2019-03-05 00:00:00
Tasks: Decoder, Fraud Detection, Gaussian Processes, Intrusion Detection, Medical Diagnosis, Novelty Detection

Novelty detection is the unsupervised problem of identifying anomalies in
test data which significantly differ from the training set. Novelty detection
is one of the classic challenges in Machine Learning and a core component of
several research areas such as fraud detection, intrusion detection, medical
diagnosis, data cleaning, and fault prevention. While numerous algorithms were
designed to address this problem, most methods are only suitable to model
continuous numerical data. Tackling datasets composed of mixed-type features,
such as numerical and categorical data, or temporal datasets describing
discrete event sequences is a challenging task. In addition to the supported
data types, the key criteria for efficient novelty detection methods are the
ability to accurately dissociate novelties from nominal samples, the
interpretability, the scalability and the robustness to anomalies located in
the training data.
  In this thesis, we investigate novel ways to tackle these issues. In
particular, we propose (i) an experimental comparison of novelty detection
methods for mixed-type data (ii) an experimental comparison of novelty
detection methods for sequence data, (iii) a probabilistic nonparametric
novelty detection method for mixed-type data based on Dirichlet process
mixtures and exponential-family distributions and (iv) an autoencoder-based
novelty detection model with encoder/decoder modelled as deep Gaussian
processes.