pwc-archive/papers-with-abstracts huggingface dataset:
Capturing Object Detection Uncertainty in Multi-Layer Grid Maps
Authors: Sascha Wirges, Marcel Reith-Braun, Martin Lauer, Christoph Stiller
Date: 2019-01-31 00:00:00
Tasks: General Classification, Object, object-detection, Object Detection, Sensor Fusion, Trajectory Planning

We propose a deep convolutional object detector for automated driving
applications that also estimates classification, pose and shape uncertainty of
each detected object. The input consists of a multi-layer grid map which is
well-suited for sensor fusion, free-space estimation and machine learning.
Based on the estimated pose and shape uncertainty we approximate object hulls
with bounded collision probability which we find helpful for subsequent
trajectory planning tasks. We train our models based on the KITTI object
detection data set. In a quantitative and qualitative evaluation some models
show a similar performance and superior robustness compared to previously
developed object detectors. However, our evaluation also points to undesired
data set properties which should be addressed when training data-driven models
or creating new data sets.
Conversational Networks for Automatic Online Moderation
Authors: Etienne Papegnies, Vincent Labatut, Richard Dufour, Georges Linares
Date: 2019-01-31 00:00:00
Tasks: Abuse Detection

Moderation of user-generated content in an online community is a challenge
that has great socio-economical ramifications. However, the costs incurred by
delegating this work to human agents are high. For this reason, an automatic
system able to detect abuse in user-generated content is of great interest.
There are a number of ways to tackle this problem, but the most commonly seen
in practice are word filtering or regular expression matching. The main
limitations are their vulnerability to intentional obfuscation on the part of
the users, and their context-insensitive nature. Moreover, they are
language-dependent and may require appropriate corpora for training. In this
paper, we propose a system for automatic abuse detection that completely
disregards message content. We first extract a conversational network from raw
chat logs and characterize it through topological measures. We then use these
as features to train a classifier on our abuse detection task. We thoroughly
assess our system on a dataset of user comments originating from a French
Massively Multiplayer Online Game. We identify the most appropriate network
extraction parameters and discuss the discriminative power of our features,
relatively to their topological and temporal nature. Our method reaches an
F-measure of 83.89 when using the full feature set, improving on existing
approaches. With a selection of the most discriminative features, we
dramatically cut computing time while retaining most of the performance
(82.65).
A Theory of Regularized Markov Decision Processes
Authors: Matthieu Geist, Bruno Scherrer, Olivier Pietquin
Date: 2019-01-31 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, Reinforcement Learning

Many recent successful (deep) reinforcement learning algorithms make use of regularization, generally based on entropy or Kullback-Leibler divergence. We propose a general theory of regularized Markov Decision Processes that generalizes these approaches in two directions: we consider a larger class of regularizers, and we consider the general modified policy iteration approach, encompassing both policy iteration and value iteration. The core building blocks of this theory are a notion of regularized Bellman operator and the Legendre-Fenchel transform, a classical tool of convex optimization. This approach allows for error propagation analyses of general algorithmic schemes of which (possibly variants of) classical algorithms such as Trust Region Policy Optimization, Soft Q-learning, Stochastic Actor Critic or Dynamic Policy Programming are special cases. This also draws connections to proximal convex optimization, especially to Mirror Descent.
Improving Evolutionary Strategies with Generative Neural Networks
Authors: Louis Faury, Clement Calauzenes, Olivier Fercoq, Syrine Krichen
Date: 2019-01-31 00:00:00

Evolutionary Strategies (ES) are a popular family of black-box zeroth-order
optimization algorithms which rely on search distributions to efficiently
optimize a large variety of objective functions. This paper investigates the
potential benefits of using highly flexible search distributions in classical
ES algorithms, in contrast to standard ones (typically Gaussians). We model
such distributions with Generative Neural Networks (GNNs) and introduce a new
training algorithm that leverages their expressiveness to accelerate the ES
procedure. We show that this tailored algorithm can readily incorporate
existing ES algorithms, and outperforms the state-of-the-art on diverse
objective functions.
Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations
Authors: Yang Shi, Animashree Anandkumar
Date: 2019-01-31 00:00:00
Tasks: Dimensionality Reduction, image-classification, Image Classification

Sketching is a randomized dimensionality-reduction method that aims to preserve relevant information in large-scale datasets. Count sketch is a simple popular sketch which uses a randomized hash function to achieve compression. In this paper, we propose a novel extension known as Higher-order Count Sketch (HCS). While count sketch uses a single hash function, HCS uses multiple (smaller) hash functions for sketching. HCS reshapes the input (vector) data into a higher-order tensor and employs a tensor product of the random hash functions to compute the sketch. This results in an exponential saving (with respect to the order of the tensor) in the memory requirements of the hash functions, under certain conditions on the input data. Furthermore, when the input data itself has an underlying structure in the form of various tensor representations such as the Tucker decomposition, we obtain significant advantages. We derive efficient (approximate) computation of various tensor operations such as tensor products and tensor contractions directly on the sketched data. Thus, HCS is the first sketch to fully exploit the multi-dimensional nature of higher-order tensors. We apply HCS to tensorized neural networks where we replace fully connected layers with sketched tensor operations. We achieve nearly state of the art accuracy with significant compression on the image classification benchmark.
Semantic Hierarchy Preserving Deep Hashing for Large-scale Image Retrieval
Authors: Ming Zhang, Xuefei Zhe, Le Ou-Yang, Shifeng Chen, Hong Yan
Date: 2019-01-31 00:00:00
Tasks: Content-Based Image Retrieval, Deep Hashing, Image Retrieval, Retrieval

Deep hashing models have been proposed as an efficient method for large-scale similarity search. However, most existing deep hashing methods only utilize fine-level labels for training while ignoring the natural semantic hierarchy structure. This paper presents an effective method that preserves the classwise similarity of full-level semantic hierarchy for large-scale image retrieval. Experiments on two benchmark datasets show that our method helps improve the fine-level retrieval performance. Moreover, with the help of the semantic hierarchy, it can produce significantly better binary codes for hierarchical retrieval, which indicates its potential of providing more user-desired retrieval results.
Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning
Authors: Yichen Wu, Yair Rivenson, Hongda Wang, Yilin Luo, Eyal Ben-David, Laurent A. Bentolila, Christian Pritz, Aydogan Ozcan
Date: 2019-01-31 00:00:00

Three-dimensional (3D) fluorescence microscopy in general requires axial scanning to capture images of a sample at different planes. Here we demonstrate that a deep convolutional neural network can be trained to virtually refocus a 2D fluorescence image onto user-defined 3D surfaces within the sample volume. With this data-driven computational microscopy framework, we imaged the neuron activity of a Caenorhabditis elegans worm in 3D using a time-sequence of fluorescence images acquired at a single focal plane, digitally increasing the depth-of-field of the microscope by 20-fold without any axial scanning, additional hardware, or a trade-off of imaging resolution or speed. Furthermore, we demonstrate that this learning-based approach can correct for sample drift, tilt, and other image aberrations, all digitally performed after the acquisition of a single fluorescence image. This unique framework also cross-connects different imaging modalities to each other, enabling 3D refocusing of a single wide-field fluorescence image to match confocal microscopy images acquired at different sample planes. This deep learning-based 3D image refocusing method might be transformative for imaging and tracking of 3D biological samples, especially over extended periods of time, mitigating photo-toxicity, sample drift, aberration and defocusing related challenges associated with standard 3D fluorescence microscopy techniques.
Uncertainty Quantification in Deep MRI Reconstruction
Authors: Vineet Edupuganti, Morteza Mardani, Shreyas Vasanawala, John Pauly
Date: 2019-01-31 00:00:00
Tasks: Diagnostic, MRI Reconstruction, Uncertainty Quantification

Reliable MRI is crucial for accurate interpretation in therapeutic and diagnostic tasks. However, undersampling during MRI acquisition as well as the overparameterized and non-transparent nature of deep learning (DL) leaves substantial uncertainty about the accuracy of DL reconstruction. With this in mind, this study aims to quantify the uncertainty in image recovery with DL models. To this end, we first leverage variational autoencoders (VAEs) to develop a probabilistic reconstruction scheme that maps out (low-quality) short scans with aliasing artifacts to the diagnostic-quality ones. The VAE encodes the acquisition uncertainty in a latent code and naturally offers a posterior of the image from which one can generate pixel variance maps using Monte-Carlo sampling. Accurately predicting risk requires knowledge of the bias as well, for which we leverage Stein's Unbiased Risk Estimator (SURE) as a proxy for mean-squared-error (MSE). Extensive empirical experiments are performed for Knee MRI reconstruction under different training losses (adversarial and pixel-wise) and unrolled recurrent network architectures. Our key observations indicate that: 1) adversarial losses introduce more uncertainty; and 2) recurrent unrolled nets reduce the prediction uncertainty and risk.
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
Authors: Dongruo Zhou, Quanquan Gu
Date: 2019-01-31 00:00:00

Smooth finite-sum optimization has been widely studied in both convex and
nonconvex settings. However, existing lower bounds for finite-sum optimization
are mostly limited to the setting where each component function is (strongly)
convex, while the lower bounds for nonconvex finite-sum optimization remain
largely unsolved. In this paper, we study the lower bounds for smooth nonconvex
finite-sum optimization, where the objective function is the average of $n$
nonconvex component functions. We prove tight lower bounds for the complexity
of finding $\epsilon$-suboptimal point and $\epsilon$-approximate stationary
point in different settings, for a wide regime of the smallest eigenvalue of
the Hessian of the objective function (or each component function). Given our
lower bounds, we can show that existing algorithms including KatyushaX
(Allen-Zhu, 2018), Natasha (Allen-Zhu, 2017), RapGrad (Lan and Yang, 2018) and
StagewiseKatyusha (Chen and Yang, 2018) have achieved optimal Incremental
First-order Oracle (IFO) complexity (i.e., number of IFO calls) up to logarithm
factors for nonconvex finite-sum optimization. We also point out potential ways
to further improve these complexity results, in terms of making stronger
assumptions or by a different convergence analysis.
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model
Authors: Gi-Soo Kim, Myunghee Cho Paik
Date: 2019-01-31 00:00:00
Tasks: Recommendation Systems, Thompson Sampling

Contextual multi-armed bandit (MAB) algorithms have been shown promising for
maximizing cumulative rewards in sequential decision tasks such as news article
recommendation systems, web page ad placement algorithms, and mobile health.
However, most of the proposed contextual MAB algorithms assume linear
relationships between the reward and the context of the action. This paper
proposes a new contextual MAB algorithm for a relaxed, semiparametric reward
model that supports nonstationarity. The proposed method is less restrictive,
easier to implement and faster than two alternative algorithms that consider
the same model, while achieving a tight regret upper bound. We prove that the
high-probability upper bound of the regret incurred by the proposed algorithm
has the same order as the Thompson sampling algorithm for linear reward models.
The proposed and existing algorithms are evaluated via simulation and also
applied to Yahoo! news article recommendation log data.
Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty
Authors: Muhammad Raza Khan, Joshua E. Blumenstock
Date: 2019-01-31 00:00:00
Tasks: Graph Learning, Humanitarian

With the rapid expansion of mobile phone networks in developing countries,
large-scale graph machine learning has gained sudden relevance in the study of
global poverty. Recent applications range from humanitarian response and
poverty estimation to urban planning and epidemic containment. Yet the vast
majority of computational tools and algorithms used in these applications do
not account for the multi-view nature of social networks: people are related in
myriad ways, but most graph learning models treat relations as binary. In this
paper, we develop a graph-based convolutional network for learning on
multi-view networks. We show that this method outperforms state-of-the-art
semi-supervised learning algorithms on three different prediction tasks using
mobile phone datasets from three different developing countries. We also show
that, while designed specifically for use in poverty research, the algorithm
also outperforms existing benchmarks on a broader set of learning tasks on
multi-view networks, including node labelling in citation networks.
A Data Driven Method of Optimizing Feedforward Compensator for Autonomous Vehicle
Authors: Tianyu Shi, Pin Wang, Ching-Yao Chan, Chonghao Zou
Date: 2019-01-31 00:00:00

A reliable controller is critical and essential for the execution of safe and
smooth maneuvers of an autonomous vehicle.The controller must be robust to
external disturbances, such as road surface, weather, and wind conditions, and
so on.It also needs to deal with the internal parametric variations of vehicle
sub-systems, including power-train efficiency, measurement errors, time
delay,so on.Moreover, as in most production vehicles, the low-control commands
for the engine, brake, and steering systems are delivered through separate
electronic control units.These aforementioned factors introduce opaque and
ineffectiveness issues in controller performance.In this paper, we design a
feed-forward compensate process via a data-driven method to model and further
optimize the controller performance.We apply the principal component analysis
to the extraction of most influential features.Subsequently,we adopt a time
delay neural network and include the accuracy of the predicted error in a
future time horizon.Utilizing the predicted error,we then design a feed-forward
compensate process to improve the control performance.Finally,we demonstrate
the effectiveness of the proposed feed-forward compensate process in simulation
scenarios.
Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System
Authors: Joseph Paul Cohen, Paul Bertin, Vincent Frappier
Date: 2019-01-31 00:00:00
Proceeding: MIDL 2019 7
Tasks: Deep Learning, Disease Prediction, Out-of-Distribution Detection

In order to bridge the gap between Deep Learning researchers and medical professionals we develop a very accessible free prototype system which can be used by medical professionals to understand the reality of Deep Learning tools for chest X-ray diagnostics. The system is designed to be a second opinion where a user can process an image to confirm or aid in their diagnosis. Code and network weights are delivered via a URL to a web browser (including cell phones) but the patient data remains on the users machine and all processing occurs locally. This paper discusses the three main components in detail: out-of-distribution detection, disease prediction, and prediction explanation. The system open source and freely available here: https://mlmed.org/tools/xray
A lossless data hiding scheme in JPEG images with segment coding
Authors: Mingming Zhang, Quan Zhou, Yanlang Hu
Date: 2019-01-31 00:00:00

In this paper, we propose a lossless data hiding scheme in JPEG images. After
quantified DCT transform, coefficients have characteristics that distribution
in high frequencies is relatively sparse and absolute values are small. To
improve encoding efficiency, we put forward an encoding algorithm that searches
for a high frequency as terminate point and recode the coefficients above, so
spare space is reserved to embed secret data and appended data with no file
expansion. Receiver can obtain terminate point through data analysis, extract
additional data and recover original JPEG images lossless. Experimental results
show that the proposed method has a larger capacity than state-of-the-art
works.
A Bad Arm Existence Checking Problem
Authors: Koji Tabata, Atsuyoshi Nakamura, Junya Honda, Tamiki Komatsuzaki
Date: 2019-01-31 00:00:00

We study a bad arm existing checking problem in which a player's task is to
judge whether a positive arm exists or not among given K arms by drawing as
small number of arms as possible. Here, an arm is positive if its expected loss
suffered by drawing the arm is at least a given threshold. This problem is a
formalization of diagnosis of disease or machine failure. An interesting
structure of this problem is the asymmetry of positive and negative
(non-positive) arms' roles; finding one positive arm is enough to judge
existence while all the arms must be discriminated as negative to judge
non-existence. We propose an algorithms with arm selection policy (policy to
determine the next arm to draw) and stopping condition (condition to stop
drawing arms) utilizing this asymmetric problem structure and prove its
effectiveness theoretically and empirically.
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
Authors: Jason Wei, Kai Zou
Date: 2019-01-31 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Data Augmentation, General Classification, Sentiment Analysis, Subjectivity Analysis, Text Augmentation, Text Classification

We present EDA: easy data augmentation techniques for boosting performance on text classification tasks. EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion. On five text classification tasks, we show that EDA improves performance for both convolutional and recurrent neural networks. EDA demonstrates particularly strong results for smaller datasets; on average, across five datasets, training with EDA while using only 50% of the available training set achieved the same accuracy as normal training with all available data. We also performed extensive ablation studies and suggest parameters for practical use.
Joint Iris Segmentation and Localization Using Deep Multi-task Learning Framework
Authors: Caiyong Wang, Yuhao Zhu, Yunfan Liu, Ran He, Zhenan Sun
Date: 2019-01-31 00:00:00
Tasks: Decoder, Iris Segmentation, Medical Image Segmentation, Multi-Task Learning, Segmentation

Iris segmentation and localization in non-cooperative environment is challenging due to illumination variations, long distances, moving subjects and limited user cooperation, etc. Traditional methods often suffer from poor performance when confronted with iris images captured in these conditions. Recent studies have shown that deep learning methods could achieve impressive performance on iris segmentation task. In addition, as iris is defined as an annular region between pupil and sclera, geometric constraints could be imposed to help locating the iris more accurately and improve the segmentation results. In this paper, we propose a deep multi-task learning framework, named as IrisParseNet, to exploit the inherent correlations between pupil, iris and sclera to boost up the performance of iris segmentation and localization in a unified model. In particular, IrisParseNet firstly applies a Fully Convolutional Encoder-Decoder Attention Network to simultaneously estimate pupil center, iris segmentation mask and iris inner/outer boundary. Then, an effective post-processing method is adopted for iris inner/outer circle localization.To train and evaluate the proposed method, we manually label three challenging iris datasets, namely CASIA-Iris-Distance, UBIRIS.v2, and MICHE-I, which cover various types of noises. Extensive experiments are conducted on these newly annotated datasets, and results show that our method outperforms state-of-the-art methods on various benchmarks. All the ground-truth annotations, annotation codes and evaluation protocols are publicly available at https://github.com/xiamenwcy/IrisParseNet.
Augmenting Model Robustness with Transformation-Invariant Attacks
Authors: Houpu Yao, Zhe Wang, GuangYu Nie, Yassine Mazboudi, Yezhou Yang, Yi Ren
Date: 2019-01-31 00:00:00
Tasks: Image Cropping, model, Translation

The vulnerability of neural networks under adversarial attacks has raised serious concerns and motivated extensive research. It has been shown that both neural networks and adversarial attacks against them can be sensitive to input transformations such as linear translation and rotation, and that human vision, which is robust against adversarial attacks, is invariant to natural input transformations. Based on these, this paper tests the hypothesis that model robustness can be further improved when it is adversarially trained against transformed attacks and transformation-invariant attacks. Experiments on MNIST, CIFAR-10, and restricted ImageNet show that while transformations of attacks alone do not affect robustness, transformation-invariant attacks can improve model robustness by 2.5\% on MNIST, 3.7\% on CIFAR-10, and 1.1\% on restricted ImageNet. We discuss the intuition behind this phenomenon.
On Intra-Class Variance for Deep Learning of Classifiers
Authors: Rafal Pilarczyk, Wladyslaw Skarbek
Date: 2019-01-31 00:00:00
Tasks: Deep Learning

A novel technique for deep learning of image classifiers is presented. The
learned CNN models offer better separation of deep features (also known as
embedded vectors) measured by Euclidean proximity and also no deterioration of
the classification results by class membership probability. The latter feature
can be used for enhancing image classifiers having the classes at the model's
exploiting stage different from from classes during the training stage. While
the Shannon information of SoftMax probability for target class is extended for
mini-batch by the intra-class variance, the trained network itself is extended
by the Hadamard layer with the parameters representing the class centers.
Contrary to the existing solutions, this extra neural layer enables interfacing
of the training algorithm to the standard stochastic gradient optimizers, e.g.
AdaM algorithm. Moreover, this approach makes the computed centroids
immediately adapting to the updating embedded vectors and finally getting the
comparable accuracy in less epochs.