pwc-archive/papers-with-abstracts huggingface dataset:
A witness function based construction of discriminative models using Hermite polynomials
Authors: H. N. Mhaskar, A. Cloninger, X. Cheng
Date: 2019-01-10 00:00:00
Tasks: Dimensionality Reduction, General Classification

In machine learning, we are given a dataset of the form
$\{(\mathbf{x}_j,y_j)\}_{j=1}^M$, drawn as i.i.d. samples from an unknown
probability distribution $\mu$; the marginal distribution for the
$\mathbf{x}_j$'s being $\mu^*$. We propose that rather than using a positive
kernel such as the Gaussian for estimation of these measures, using a
non-positive kernel that preserves a large number of moments of these measures
yields an optimal approximation. We use multi-variate Hermite polynomials for
this purpose, and prove optimal and local approximation results in a supremum
norm in a probabilistic sense. Together with a permutation test developed with
the same kernel, we prove that the kernel estimator serves as a `witness
function' in classification problems. Thus, if the value of this estimator at a
point $\mathbf{x}$ exceeds a certain threshold, then the point is reliably in a
certain class. This approach can be used to modify pretrained algorithms, such
as neural networks or nonlinear dimension reduction techniques, to identify
in-class vs out-of-class regions for the purposes of generative models,
classification uncertainty, or finding robust centroids. This fact is
demonstrated in a number of real world data sets including MNIST, CIFAR10,
Science News documents, and LaLonde data sets.
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers
Authors: Daniel Liu, Ronald Yu, Hao Su
Date: 2019-01-10 00:00:00
Tasks: 3D Object Classification, General Classification, image-classification, Image Classification

3D object classification and segmentation using deep neural networks has been extremely successful. As the problem of identifying 3D objects has many safety-critical applications, the neural networks have to be robust against adversarial changes to the input data set. There is a growing body of research on generating human-imperceptible adversarial attacks and defenses against them in the 2D image classification domain. However, 3D objects have various differences with 2D images, and this specific domain has not been rigorously studied so far. We present a preliminary evaluation of adversarial attacks on deep 3D point cloud classifiers, namely PointNet and PointNet++, by evaluating both white-box and black-box adversarial attacks that were proposed for 2D images and extending those attacks to reduce the perceptibility of the perturbations in 3D space. We also show the high effectiveness of simple defenses against those attacks by proposing new defenses that exploit the unique structure of 3D point clouds. Finally, we attempt to explain the effectiveness of the defenses through the intrinsic structures of both the point clouds and the neural network architectures. Overall, we find that networks that process 3D point cloud data are weak to adversarial attacks, but they are also more easily defensible compared to 2D image classifiers. Our investigation will provide the groundwork for future studies on improving the robustness of deep neural networks that handle 3D data.
Quantized Epoch-SGD for Communication-Efficient Distributed Learning
Authors: Shen-Yi Zhao, Hao Gao, Wu-Jun Li
Date: 2019-01-10 00:00:00
Tasks: Quantization

Due to its efficiency and ease to implement, stochastic gradient descent
(SGD) has been widely used in machine learning. In particular, SGD is one of
the most popular optimization methods for distributed learning. Recently,
quantized SGD (QSGD), which adopts quantization to reduce the communication
cost in SGD-based distributed learning, has attracted much attention. Although
several QSGD methods have been proposed, some of them are heuristic without
theoretical guarantee, and others have high quantization variance which makes
the convergence become slow. In this paper, we propose a new method, called
Quantized Epoch-SGD (QESGD), for communication-efficient distributed learning.
QESGD compresses (quantizes) the parameter with variance reduction, so that it
can get almost the same performance as that of SGD with less communication
cost. QESGD is implemented on the Parameter Server framework, and empirical
results on distributed deep learning show that QESGD can outperform other
state-of-the-art quantization methods to achieve the best performance.
GM-PLL: Graph Matching based Partial Label Learning
Authors: Gengyu Lyu, Songhe Feng, Tao Wang, Congyan Lang, Yidong Li
Date: 2019-01-10 00:00:00
Tasks: Graph Matching, Partial Label Learning

Partial Label Learning (PLL) aims to learn from the data where each training
example is associated with a set of candidate labels, among which only one is
correct. The key to deal with such problem is to disambiguate the candidate
label sets and obtain the correct assignments between instances and their
candidate labels. In this paper, we interpret such assignments as
instance-to-label matchings, and reformulate the task of PLL as a matching
selection problem. To model such problem, we propose a novel Graph Matching
based Partial Label Learning (GM-PLL) framework, where Graph Matching (GM)
scheme is incorporated owing to its excellent capability of exploiting the
instance and label relationship. Meanwhile, since conventional one-to-one GM
algorithm does not satisfy the constraint of PLL problem that multiple
instances may correspond to the same label, we extend a traditional one-to-one
probabilistic matching algorithm to the many-to-one constraint, and make the
proposed framework accommodate to the PLL problem. Moreover, we also propose a
relaxed matching prediction model, which can improve the prediction accuracy
via GM strategy. Extensive experiments on both artificial and real-world data
sets demonstrate that the proposed method can achieve superior or comparable
performance against the state-of-the-art methods.
An MBO scheme for clustering and semi-supervised clustering of signed networks
Authors: Mihai Cucuringu, Andrea Pizzoferrato, Yves van Gennip
Date: 2019-01-10 00:00:00
Tasks: Clustering

We introduce a principled method for the signed clustering problem, where the goal is to partition a graph whose edge weights take both positive and negative values, such that edges within the same cluster are mostly positive, while edges spanning across clusters are mostly negative. Our method relies on a graph-based diffuse interface model formulation utilizing the Ginzburg-Landau functional, based on an adaptation of the classic numerical Merriman-Bence-Osher (MBO) scheme for minimizing such graph-based functionals. The proposed objective function aims to minimize the total weight of inter-cluster positively-weighted edges, while maximizing the total weight of the inter-cluster negatively-weighted edges. Our method scales to large sparse networks, and can be easily adjusted to incorporate labelled data information, as is often the case in the context of semi-supervised learning. We tested our method on a number of both synthetic stochastic block models and real-world data sets (including financial correlation matrices), and obtained promising results that compare favourably against a number of state-of-the-art approaches from the recent literature.
Active Learning for One-Class Classification Using Two One-Class Classifiers
Authors: Patrick Schlachter, Bin Yang
Date: 2019-01-10 00:00:00
Tasks: Active Learning, General Classification, One-Class Classification, Vocal Bursts Valence Prediction

This paper introduces a novel, generic active learning method for one-class
classification. Active learning methods play an important role to reduce the
efforts of manual labeling in the field of machine learning. Although many
active learning approaches have been proposed during the last years, most of
them are restricted on binary or multi-class problems. One-class classifiers
use samples from only one class, the so-called target class, during training
and hence require special active learning strategies. The few strategies
proposed for one-class classification either suffer from their limitation on
specific one-class classifiers or their performance depends on particular
assumptions about datasets like imbalance. Our proposed method bases on using
two one-class classifiers, one for the desired target class and one for the
so-called outlier class. It allows to invent new query strategies, to use
binary query strategies and to define simple stopping criteria. Based on the
new method, two query strategies are proposed. The provided experiments compare
the proposed approach with known strategies on various datasets and show
improved results in almost all situations.
Harnessing the Power of Serverless Runtimes for Large-Scale Optimization
Authors: Arda Aytekin, Mikael Johansson
Date: 2019-01-10 00:00:00
Tasks: regression

The event-driven and elastic nature of serverless runtimes makes them a very
efficient and cost-effective alternative for scaling up computations. So far,
they have mostly been used for stateless, data parallel and ephemeral
computations. In this work, we propose using serverless runtimes to solve
generic, large-scale optimization problems. Specifically, we build a
master-worker setup using AWS Lambda as the source of our workers, implement a
parallel optimization algorithm to solve a regularized logistic regression
problem, and show that relative speedups up to 256 workers and efficiencies
above 70% up to 64 workers can be expected. We also identify possible
algorithmic and system-level bottlenecks, propose improvements, and discuss the
limitations and challenges in realizing these improvements.
Motion Perception in Reinforcement Learning with Dynamic Objects
Authors: Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun, Thomas Brox
Date: 2019-01-10 00:00:00
Tasks: continuous-control, Continuous Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In dynamic environments, learned controllers are supposed to take motion into
account when selecting the action to be taken. However, in existing
reinforcement learning works motion is rarely treated explicitly; it is rather
assumed that the controller learns the necessary motion representation from
temporal stacks of frames implicitly. In this paper, we show that for
continuous control tasks learning an explicit representation of motion improves
the quality of the learned controller in dynamic scenarios. We demonstrate this
on common benchmark tasks (Walker, Swimmer, Hopper), on target reaching and
ball catching tasks with simulated robotic arms, and on a dynamic single ball
juggling task. Moreover, we find that when equipped with an appropriate network
architecture, the agent can, on some tasks, learn motion features also with
pure reinforcement learning, without additional supervision. Further we find
that using an image difference between the current and the previous frame as an
additional input leads to better results than a temporal stack of frames.
Generalization Studies of Neural Network Models for Cardiac Disease Detection Using Limited Channel ECG
Authors: Deepta Rajan, David Beymer, Girish Narayan
Date: 2019-01-05 00:00:00
Tasks: BIG-bench Machine Learning

Acceleration of machine learning research in healthcare is challenged by lack
of large annotated and balanced datasets. Furthermore, dealing with measurement
inaccuracies and exploiting unsupervised data are considered to be central to
improving existing solutions. In particular, a primary objective in predictive
modeling is to generalize well to both unseen variations within the observed
classes, and unseen classes. In this work, we consider such a challenging
problem in machine learning driven diagnosis: detecting a gamut of
cardiovascular conditions (e.g. infarction, dysrhythmia etc.) from limited
channel ECG measurements. Though deep neural networks have achieved
unprecedented success in predictive modeling, they rely solely on
discriminative models that can generalize poorly to unseen classes. We argue
that unsupervised learning can be utilized to construct effective latent spaces
that facilitate better generalization. This work extensively compares the
generalization of our proposed approach against a state-of-the-art deep
learning solution. Our results show significant improvements in F1-scores.
An Analysis of the Accuracy of the P300 BCI
Authors: Nitzan S. Artzi, Oren Shriki
Date: 2018-12-11 00:00:00
Tasks: Brain Computer Interface, General Classification

The P300 Brain-Computer Interface (BCI) is a well-established communication
channel for severely disabled people. The P300 event-related potential is
mostly characterized by its amplitude or its area, which correlate with the
spelling accuracy of the P300 speller. Here, we introduce a novel approach for
estimating the efficiency of this BCI by considering the P300 signal-to-noise
ratio (SNR), a parameter that estimates the spatial and temporal noise levels
and has a significantly stronger correlation with spelling accuracy.
Furthermore, we suggest a Gaussian noise model, which utilizes the P300
event-related potential SNR to predict spelling accuracy under various
conditions for LDA-based classification. We demonstrate the utility of this
analysis using real data and discuss its potential applications, such as
speeding up the process of electrode selection.
Accelerated Flow for Probability Distributions
Authors: Amirhossein Taghvaei, Prashant G. Mehta
Date: 2019-01-10 00:00:00

This paper presents a methodology and numerical algorithms for constructing
accelerated gradient flows on the space of probability distributions. In
particular, we extend the recent variational formulation of accelerated
gradient methods in (wibisono, et. al. 2016) from vector valued variables to
probability distributions. The variational problem is modeled as a mean-field
optimal control problem. The maximum principle of optimal control theory is
used to derive Hamilton's equations for the optimal gradient flow. The
Hamilton's equation are shown to achieve the accelerated form of density
transport from any initial probability distribution to a target probability
distribution. A quantitative estimate on the asymptotic convergence rate is
provided based on a Lyapunov function construction, when the objective
functional is displacement convex. Two numerical approximations are presented
to implement the Hamilton's equations as a system of $N$ interacting particles.
The continuous limit of the Nesterov's algorithm is shown to be a special case
with $N=1$. The algorithm is illustrated with numerical examples.
Making AI meaningful again
Authors: Jobst Landgrebe, Barry Smith
Date: 2019-01-09 00:00:00
Tasks: Philosophy

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm
in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of
frustration when genuinely useful AI applications failed to be forthcoming.
Today, we are experiencing once again a period of enthusiasm, fired above all
by the successes of the technology of deep neural networks or deep machine
learning. In this paper we draw attention to what we take to be serious
problems underlying current views of artificial intelligence encouraged by
these successes, especially in the domain of language processing. We then show
an alternative approach to language-centric AI, in which we identify a role for
philosophy.
PFML-based Semantic BCI Agent for Game of Go Learning and Prediction
Authors: Chang-Shing Lee, Mei-Hui Wang, Li-Wei Ko, Bo-Yu Tsai, Yi-Lin Tsai, Sheng-Chi Yang, Lu-An Lin, Yi-Hsiu Lee, Hirofumi Ohashi, Naoyuki Kubota, Nan Shuo
Date: 2019-01-10 00:00:00
Tasks: Brain Computer Interface, Game of Go

This paper presents a semantic brain computer interface (BCI) agent with
particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for Go
learning and prediction applications. Additionally, we also establish an Open
Go Darkforest (OGD) cloud platform with Facebook AI research (FAIR) open source
Darkforest and ELF OpenGo AI bots. The Japanese robot Palro will simultaneously
predict the move advantage in the board game Go to the Go players for reference
or learning. The proposed semantic BCI agent operates efficiently by the
human-based BCI data from their brain waves and machine-based game data from
the prediction of the OGD cloud platform for optimizing the parameters between
humans and machines. Experimental results show that the proposed human and
smart machine co-learning mechanism performs favorably. We hope to provide
students with a better online learning environment, combining different kinds
of handheld devices, robots, or computer equipment, to achieve a desired and
intellectual learning goal in the future.
Myocardial Infarction Quantification From Late Gadolinium Enhancement MRI Using Top-hat Transforms and Neural Networks
Authors: Ezequiel de la Rosa, Désiré Sidibé, Thomas Decourselle, Thibault Leclercq, Alexandre Cochet, Alain Lalande
Date: 2019-01-09 00:00:00
Tasks: Segmentation

Significance: Late gadolinium enhanced magnetic resonance imaging (LGE-MRI)
is the gold standard technique for myocardial viability assessment. Although
the technique accurately reflects the damaged tissue, there is no clinical
standard for quantifying myocardial infarction (MI), demanding most algorithms
to be expert dependent. Objectives and Methods: In this work a new automatic
method for MI quantification from LGE-MRI is proposed. Our novel segmentation
approach is devised for accurately detecting not only hyper-enhanced lesions,
but also microvascular-obstructed areas. Moreover, it includes a myocardial
disease detection step which extends the algorithm for working under healthy
scans. The method is based on a cascade approach where firstly, diseased slices
are identified by a convolutional neural network (CNN). Secondly, by means of
morphological operations a fast coarse scar segmentation is obtained. Thirdly,
the segmentation is refined by a boundary-voxel reclassification strategy using
an ensemble of CNNs. For its validation, reproducibility and further comparison
against other methods, we tested the method on a big multi-field expert
annotated LGE-MRI database including healthy and diseased cases. Results and
Conclusion: In an exhaustive comparison against nine reference algorithms, the
proposal achieved state-of-the-art segmentation performances and showed to be
the only method agreeing in volumetric scar quantification with the expert
delineations. Moreover, the method was able to reproduce the intra- and
inter-observer variability ranges. It is concluded that the method could
suitably be transferred to clinical scenarios.
SalSi: A new seismic attribute for salt dome detection
Authors: Muhammad Amir Shafiq, Tariq Alshawi, Zhiling Long, Ghassan AlRegib
Date: 2019-01-09 00:00:00
Tasks: Attribute, Seismic Interpretation

In this paper, we propose a saliency-based attribute, SalSi, to detect salt
dome bodies within seismic volumes. SalSi is based on the saliency theory and
modeling of the human vision system (HVS). In this work, we aim to highlight
the parts of the seismic volume that receive highest attention from the human
interpreter, and based on the salient features of a seismic image, we detect
the salt domes. Experimental results show the effectiveness of SalSi on the
real seismic dataset acquired from the North Sea, F3 block. Subjectively, we
have used the ground truth and the output of different salt dome delineation
algorithms to validate the results of SalSi. For the objective evaluation of
results, we have used the receiver operating characteristics (ROC) curves and
area under the curves (AUC) to demonstrate SalSi is a promising and an
effective attribute for seismic interpretation.
DASPS: A Database for Anxious States based on a Psychological Stimulation
Authors: Asma Baghdadi, Yassine Aribi, Rahma Fourati, Najla Halouani, Patrick Siarry, Adel M. ALIMI
Date: 2019-01-09 00:00:00
Tasks: Anxiety Detection, EEG, Electroencephalogram (EEG)

Anxiety affects human capabilities and behavior as much as it affects productivity and quality of life. It can be considered as the main cause of depression and suicide. Anxious states are easily detectable by humans due to their acquired cognition, humans interpret the interlocutor's tone of speech, gesture, facial expressions and recognize their mental state. There is a need for non-invasive reliable techniques that performs the complex task of anxiety detection. In this paper, we present DASPS database containing recorded Electroencephalogram (EEG) signals of 23 participants during anxiety elicitation by means of face-to-face psychological stimuli. EEG signals were captured with Emotiv Epoc headset as it's a wireless wearable low-cost equipment. In our study, we investigate the impact of different parameters, notably: trial duration, feature type, feature combination and anxiety levels number. Our findings showed that anxiety is well elicited in 1 second. For instance, stacked sparse autoencoder with different type of features achieves 83.50% and 74.60% for 2 and 4 anxiety levels detection, respectively. The presented results prove the benefits of the use of a low-cost EEG headset instead of medical non-wireless devices and create a starting point for new researches in the field of anxiety detection.
Composite Shape Modeling via Latent Space Factorization
Authors: Anastasia Dubrovina, Fei Xia, Panos Achlioptas, Mira Shalah, Raphael Groscot, Leonidas Guibas
Date: 2019-01-09 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Shape Modeling

We present a novel neural network architecture, termed Decomposer-Composer, for semantic structure-aware 3D shape modeling. Our method utilizes an auto-encoder-based pipeline, and produces a novel factorized shape embedding space, where the semantic structure of the shape collection translates into a data-dependent sub-space factorization, and where shape composition and decomposition become simple linear operations on the embedding coordinates. We further propose to model shape assembly using an explicit learned part deformation module, which utilizes a 3D spatial transformer network to perform an in-network volumetric grid deformation, and which allows us to train the whole system end-to-end. The resulting network allows us to perform part-level shape manipulation, unattainable by existing approaches. Our extensive ablation study, comparison to baseline methods and qualitative analysis demonstrate the improved performance of the proposed method.
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
Authors: He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, Leonidas J. Guibas
Date: 2019-01-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, Mixed Reality, Object, Pose Estimation

The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image. Contrary to "instance-level" 6D pose estimation tasks, our problem assumes that no exact object CAD models are available during either training or testing time. To handle different and unseen object instances in a given category, we introduce a Normalized Object Coordinate Space (NOCS)---a shared canonical representation for all possible object instances within a category. Our region-based neural network is then trained to directly infer the correspondence from observed pixels to this shared object representation (NOCS) along with other object information such as class label and instance mask. These predictions can be combined with the depth map to jointly estimate the metric 6D pose and dimensions of multiple objects in a cluttered scene. To train our network, we present a new context-aware technique to generate large amounts of fully annotated mixed reality data. To further improve our model and evaluate its performance on real data, we also provide a fully annotated real-world dataset with large environment and instance variation. Extensive experiments demonstrate that the proposed method is able to robustly estimate the pose and size of unseen object instances in real environments while also achieving state-of-the-art performance on standard 6D pose estimation benchmarks.