pwc-archive/papers-with-abstracts huggingface dataset:
Hybrid Forest: A Concept Drift Aware Data Stream Mining Algorithm
Authors: Radin Hamidi Rad, Maryam Amir Haeri
Date: 2019-02-10 00:00:00
Tasks: General Classification, regression

Nowadays with a growing number of online controlling systems in the
organization and also a high demand of monitoring and stats facilities that
uses data streams to log and control their subsystems, data stream mining
becomes more and more vital. Hoeffding Trees (also called Very Fast Decision
Trees a.k.a. VFDT) as a Big Data approach in dealing with the data stream for
classification and regression problems showed good performance in handling
facing challenges and making the possibility of any-time prediction. Although
these methods outperform other methods e.g. Artificial Neural Networks (ANN)
and Support Vector Regression (SVR), they suffer from high latency in adapting
with new concepts when the statistical distribution of incoming data changes.
In this article, we introduced a new algorithm that can detect and handle
concept drift phenomenon properly. This algorithms also benefits from fast
startup ability which helps systems to be able to predict faster than other
algorithms at the beginning of data stream arrival. We also have shown that our
approach will overperform other controversial approaches for classification and
regression tasks.
MOTS: Multi-Object Tracking and Segmentation
Authors: Paul Voigtlaender, Michael Krause, Aljosa Osep, Jonathon Luiten, Berin Balachandar Gnana Sekar, Andreas Geiger, Bastian Leibe
Date: 2019-02-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Multi-Object Tracking, Multi-Object Tracking and Segmentation, Multiple Object Tracking, Object, Object Tracking, Segmentation

This paper extends the popular task of multi-object tracking to multi-object
tracking and segmentation (MOTS). Towards this goal, we create dense
pixel-level annotations for two existing tracking datasets using a
semi-automatic annotation procedure. Our new annotations comprise 65,213 pixel
masks for 977 distinct objects (cars and pedestrians) in 10,870 video frames.
For evaluation, we extend existing multi-object tracking metrics to this new
task. Moreover, we propose a new baseline method which jointly addresses
detection, tracking, and segmentation with a single convolutional network. We
demonstrate the value of our datasets by achieving improvements in performance
when training on MOTS annotations. We believe that our datasets, metrics and
baseline will become a valuable resource towards developing multi-object
tracking approaches that go beyond 2D bounding boxes. We make our annotations,
code, and models available at https://www.vision.rwth-aachen.de/page/mots.
Vulnerable road user detection: state-of-the-art and open challenges
Authors: Patrick Mannion
Date: 2019-02-10 00:00:00
Tasks: Autonomous Vehicles, BIG-bench Machine Learning, object-detection, Object Detection

Correctly identifying vulnerable road users (VRUs), e.g. cyclists and
pedestrians, remains one of the most challenging environment perception tasks
for autonomous vehicles (AVs). This work surveys the current state-of-the-art
in VRU detection, covering topics such as benchmarks and datasets, object
detection techniques and relevant machine learning algorithms. The article
concludes with a discussion of remaining open challenges and promising future
research directions for this domain.
NeurAll: Towards a Unified Visual Perception Model for Automated Driving
Authors: Ganesh Sistu, Isabelle Leang, Sumanth Chennupati, Senthil Yogamani, Ciaran Hughes, Stefan Milz, Samir Rawashdeh
Date: 2019-02-10 00:00:00
Tasks: Auxiliary Learning, Computational Efficiency, Depth Estimation, Object Detection, Object Recognition, Semantic Segmentation

Convolutional Neural Networks (CNNs) are successfully used for the important automotive visual perception tasks including object recognition, motion and depth estimation, visual SLAM, etc. However, these tasks are typically independently explored and modeled. In this paper, we propose a joint multi-task network design for learning several tasks simultaneously. Our main motivation is the computational efficiency achieved by sharing the expensive initial convolutional layers between all tasks. Indeed, the main bottleneck in automated driving systems is the limited processing power available on deployment hardware. There is also some evidence for other benefits in improving accuracy for some tasks and easing development effort. It also offers scalability to add more tasks leveraging existing features and achieving better generalization. We survey various CNN based solutions for visual perception tasks in automated driving. Then we propose a unified CNN model for the important tasks and discuss several advanced optimization and architecture design techniques to improve the baseline model. The paper is partly review and partly positional with demonstration of several preliminary results promising for future research. We first demonstrate results of multi-stream learning and auxiliary learning which are important ingredients to scale to a large multi-task model. Finally, we implement a two-stream three-task network which performs better in many cases compared to their corresponding single-task models, while maintaining network size.
Learning Best Response Strategies for Agents in Ad Exchanges
Authors: Stavros Gerakaris, Subramanian Ramamoorthy
Date: 2019-02-10 00:00:00
Tasks: Q-Learning

Ad exchanges are widely used in platforms for online display advertising.
Autonomous agents operating in these exchanges must learn policies for
interacting profitably with a diverse, continually changing, but unknown
market. We consider this problem from the perspective of a publisher,
strategically interacting with an advertiser through a posted price mechanism.
The learning problem for this agent is made difficult by the fact that
information is censored, i.e., the publisher knows if an impression is sold but
no other quantitative information. We address this problem using the
Harsanyi-Bellman Ad Hoc Coordination (HBA) algorithm, which conceptualises this
interaction in terms of a Stochastic Bayesian Game and arrives at optimal
actions by best responding with respect to probabilistic beliefs maintained
over a candidate set of opponent behaviour profiles. We adapt and apply HBA to
the censored information setting of ad exchanges. Also, addressing the case of
stochastic opponents, we devise a strategy based on a Kaplan-Meier estimator
for opponent modelling. We evaluate the proposed method using simulations
wherein we show that HBA-KM achieves substantially better competitive ratio and
lower variance of return than baselines, including a Q-learning agent and a
UCB-based online learning agent, and comparable to the offline optimal
algorithm.
Angle-Closure Detection in Anterior Segment OCT based on Multi-Level Deep Network
Authors: Huazhu Fu, Yanwu Xu, Stephen Lin, Damon Wing Kee Wong, Mani Baskaran, Meenakshi Mahesh, Tin Aung, Jiang Liu
Date: 2019-02-10 00:00:00

Irreversible visual impairment is often caused by primary angle-closure
glaucoma, which could be detected via Anterior Segment Optical Coherence
Tomography (AS-OCT). In this paper, an automated system based on deep learning
is presented for angle-closure detection in AS-OCT images. Our system learns a
discriminative representation from training data that captures subtle visual
cues not modeled by handcrafted features. A Multi-Level Deep Network (MLDN) is
proposed to formulate this learning, which utilizes three particular AS-OCT
regions based on clinical priors: the global anterior segment structure, local
iris region, and anterior chamber angle (ACA) patch. In our method, a sliding
window based detector is designed to localize the ACA region, which addresses
ACA detection as a regression task. Then, three parallel sub-networks are
applied to extract AS-OCT representations for the global image and at
clinically-relevant local regions. Finally, the extracted deep features of
these sub-networks are concatenated into one fully connected layer to predict
the angle-closure detection result. In the experiments, our system is shown to
surpass previous detection methods and other deep learning systems on two
clinical AS-OCT datasets.
Colorectal Cancer Outcome Prediction from H&E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles
Authors: Xingzhi Yue, Neofytos Dimitriou, Ognjen Arandjelovic
Date: 2019-02-10 00:00:00
Tasks: whole slide images

Digital pathology (DP) is a new research area which falls under the broad
umbrella of health informatics. Owing to its potential for major public health
impact, in recent years DP has been attracting much research attention.
Nevertheless, a wide breadth of significant conceptual and technical challenges
remain, few of them greater than those encountered in the field of oncology.
The automatic analysis of digital pathology slides of cancerous tissues is
particularly problematic due to the inherent heterogeneity of the disease,
extremely large images, amongst numerous others. In this paper we introduce a
novel machine learning based framework for the prediction of colorectal cancer
outcome from whole digitized haematoxylin & eosin (H&E) stained histopathology
slides. Using a real-world data set we demonstrate the effectiveness of the
method and present a detailed analysis of its different elements which
corroborate its ability to extract and learn salient, discriminative, and
clinically meaningful content.
EvalAI: Towards Better Evaluation Systems for AI Agents
Authors: Deshraj Yadav, Rishabh Jain, Harsh Agrawal, Prithvijit Chattopadhyay, Taranjeet Singh, Akash Jain, Shiv Baran Singh, Stefan Lee, Dhruv Batra
Date: 2019-02-10 00:00:00
Tasks: Benchmarking, BIG-bench Machine Learning

We introduce EvalAI, an open source platform for evaluating and comparing
machine learning (ML) and artificial intelligence algorithms (AI) at scale.
EvalAI is built to provide a scalable solution to the research community to
fulfill the critical need of evaluating machine learning models and agents
acting in an environment against annotations or with a human-in-the-loop. This
will help researchers, students, and data scientists to create, collaborate,
and participate in AI challenges organized around the globe. By simplifying and
standardizing the process of benchmarking these models, EvalAI seeks to lower
the barrier to entry for participating in the global scientific effort to push
the frontiers of machine learning and artificial intelligence, thereby
increasing the rate of measurable progress in this domain.
Performance Advantages of Deep Neural Networks for Angle of Arrival Estimation
Authors: Oded Bialer, Noa Garnett, Tom Tirer
Date: 2019-02-10 00:00:00

The problem of estimating the number of sources and their angles of arrival
from a single antenna array observation has been an active area of research in
the signal processing community for the last few decades. When the number of
sources is large, the maximum likelihood estimator is intractable due to its
very high complexity, and therefore alternative signal processing methods have
been developed with some performance loss. In this paper, we apply a deep
neural network (DNN) approach to the problem and analyze its advantages with
respect to signal processing algorithms. We show that an appropriate designed
network can attain the maximum likelihood performance with feasible complexity
and outperform other feasible signal processing estimation methods over various
signal to noise ratios and array response inaccuracies.
Cross-spectral Face Completion for NIR-VIS Heterogeneous Face Recognition
Authors: Ran He, Jie Cao, Lingxiao Song, Zhenan Sun, Tieniu Tan
Date: 2019-02-10 00:00:00
Tasks: Face Generation, Face Recognition, Facial Inpainting, Heterogeneous Face Recognition, Translation

Near infrared-visible (NIR-VIS) heterogeneous face recognition refers to the
process of matching NIR to VIS face images. Current heterogeneous methods try
to extend VIS face recognition methods to the NIR spectrum by synthesizing VIS
images from NIR images. However, due to self-occlusion and sensing gap, NIR
face images lose some visible lighting contents so that they are always
incomplete compared to VIS face images. This paper models high resolution
heterogeneous face synthesis as a complementary combination of two components,
a texture inpainting component and pose correction component. The inpainting
component synthesizes and inpaints VIS image textures from NIR image textures.
The correction component maps any pose in NIR images to a frontal pose in VIS
images, resulting in paired NIR and VIS textures. A warping procedure is
developed to integrate the two components into an end-to-end deep network. A
fine-grained discriminator and a wavelet-based discriminator are designed to
supervise intra-class variance and visual quality respectively. One UV loss,
two adversarial losses and one pixel loss are imposed to ensure synthesis
results. We demonstrate that by attaching the correction component, we can
simplify heterogeneous face synthesis from one-to-many unpaired image
translation to one-to-one paired image translation, and minimize spectral and
pose discrepancy during heterogeneous recognition. Extensive experimental
results show that our network not only generates high-resolution VIS face
images and but also facilitates the accuracy improvement of heterogeneous face
recognition.
Task2Vec: Task Embedding for Meta-Learning
Authors: Alessandro Achille, Michael Lam, Rahul Tewari, Avinash Ravichandran, Subhransu Maji, Charless Fowlkes, Stefano Soatto, Pietro Perona
Date: 2019-02-10 00:00:00
Proceeding: ICCV 2019 10
Tasks: Meta-Learning

We introduce a method to provide vectorial representations of visual
classification tasks which can be used to reason about the nature of those
tasks and their relations. Given a dataset with ground-truth labels and a loss
function defined over those labels, we process images through a "probe network"
and compute an embedding based on estimates of the Fisher information matrix
associated with the probe network parameters. This provides a fixed-dimensional
embedding of the task that is independent of details such as the number of
classes and does not require any understanding of the class label semantics. We
demonstrate that this embedding is capable of predicting task similarities that
match our intuition about semantic and taxonomic relations between different
visual tasks (e.g., tasks based on classifying different types of plants are
similar) We also demonstrate the practical value of this framework for the
meta-task of selecting a pre-trained feature extractor for a new task. We
present a simple meta-learning framework for learning a metric on embeddings
that is capable of predicting which feature extractors will perform well.
Selecting a feature extractor with task embedding obtains a performance close
to the best available feature extractor, while costing substantially less than
exhaustively training and evaluating on all available feature extractors.
Feature Selection for multi-labeled variables via Dependency Maximization
Authors: Salimeh Yasaei Sekeh, Alfred O. Hero
Date: 2019-02-10 00:00:00
Tasks: feature selection, General Classification

Feature selection and reducing the dimensionality of data is an essential step in data analysis. In this work, we propose a new criterion for feature selection that is formulated as conditional information between features given the labeled variable. Instead of using the standard mutual information measure based on Kullback-Leibler divergence, we use our proposed criterion to filter out redundant features for the purpose of multiclass classification. This approach results in an efficient and fast non-parametric implementation of feature selection as it can be directly estimated using a geometric measure of dependency, the global Friedman-Rafsky (FR) multivariate run test statistic constructed by a global minimal spanning tree (MST). We demonstrate the advantages of our proposed feature selection approach through simulation. In addition the proposed feature selection method is applied to the MNIST data set.
Model Compression with Adversarial Robustness: A Unified Optimization Framework
Authors: Shupeng Gui, Haotao Wang, Chen Yu, Haichuan Yang, Zhangyang Wang, Ji Liu
Date: 2019-02-10 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Adversarial Robustness, Model Compression, Quantization

Deep model compression has been extensively studied, and state-of-the-art methods can now achieve high compression ratios with minimal accuracy loss. This paper studies model compression through a different lens: could we compress models without hurting their robustness to adversarial attacks, in addition to maintaining accuracy? Previous literature suggested that the goals of robustness and compactness might sometimes contradict. We propose a novel Adversarially Trained Model Compression (ATMC) framework. ATMC constructs a unified constrained optimization formulation, where existing compression means (pruning, factorization, quantization) are all integrated into the constraints. An efficient algorithm is then developed. An extensive group of experiments are presented, demonstrating that ATMC obtains remarkably more favorable trade-off among model size, accuracy and robustness, over currently available alternatives in various settings. The codes are publicly available at: https://github.com/shupenggui/ATMC.
Scattering Statistics of Generalized Spatial Poisson Point Processes
Authors: Michael Perlmutter, Jieqian He, Matthew Hirn
Date: 2019-02-10 00:00:00
Tasks: Point Processes

We present a machine learning model for the analysis of randomly generated discrete signals, modeled as the points of an inhomogeneous, compound Poisson point process. Like the wavelet scattering transform introduced by Mallat, our construction is naturally invariant to translations and reflections, but it decouples the roles of scale and frequency, replacing wavelets with Gabor-type measurements. We show that, with suitable nonlinearities, our measurements distinguish Poisson point processes from common self-similar processes, and separate different types of Poisson point processes.
Deep learning and face recognition: the state of the art
Authors: Stephen Balaban
Date: 2019-02-10 00:00:00
Tasks: Deep Learning, Face Recognition, Face Verification, image-classification, Image Classification, speech-recognition, Speech Recognition

Deep Neural Networks (DNNs) have established themselves as a dominant
technique in machine learning. DNNs have been top performers on a wide variety
of tasks including image classification, speech recognition, and face
recognition. Convolutional neural networks (CNNs) have been used in nearly all
of the top performing methods on the Labeled Faces in the Wild (LFW) dataset.
In this talk and accompanying paper, I attempt to provide a review and summary
of the deep learning techniques used in the state-of-the-art. In addition, I
highlight the need for both larger and more challenging public datasets to
benchmark these systems. The high accuracy (99.63% for FaceNet at the time of
publishing) and utilization of outside data (hundreds of millions of images in
the case of Google's FaceNet) suggest that current face verification benchmarks
such as LFW may not be challenging enough, nor provide enough data, for current
techniques. There exist a variety of organizations with mobile photo sharing
applications that would be capable of releasing a very large scale and highly
diverse dataset of facial images captured on mobile devices. Such an "ImageNet
for Face Recognition" would likely receive a warm welcome from researchers and
practitioners alike.
Scalable Fair Clustering
Authors: Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, Tal Wagner
Date: 2019-02-10 00:00:00
Tasks: Clustering, Fairness

We study the fair variant of the classic $k$-median problem introduced by Chierichetti et al. [2017]. In the standard $k$-median problem, given an input pointset $P$, the goal is to find $k$ centers $C$ and assign each input point to one of the centers in $C$ such that the average distance of points to their cluster center is minimized. In the fair variant of $k$-median, the points are colored, and the goal is to minimize the same average distance objective while ensuring that all clusters have an "approximately equal" number of points of each color. Chierichetti et al. proposed a two-phase algorithm for fair $k$-clustering. In the first step, the pointset is partitioned into subsets called fairlets that satisfy the fairness requirement and approximately preserve the $k$-median objective. In the second step, fairlets are merged into $k$ clusters by one of the existing $k$-median algorithms. The running time of this algorithm is dominated by the first step, which takes super-quadratic time. In this paper, we present a practical approximate fairlet decomposition algorithm that runs in nearly linear time. Our algorithm additionally allows for finer control over the balance of resulting clusters than the original work. We complement our theoretical bounds with empirical evaluation.
Biadversarial Variational Autoencoder
Authors: Arnaud Fickinger
Date: 2019-02-09 00:00:00

In the original version of the Variational Autoencoder, Kingma et al. assume
Gaussian distributions for the approximate posterior during the inference and
for the output during the generative process. This assumptions are good for
computational reasons, e.g. we can easily optimize the parameters of a neural
network using the reparametrization trick and the KL divergence between two
Gaussians can be computed in closed form. However it results in blurry images
due to its difficulty to represent multimodal distributions. We show that using
two adversarial networks, we can optimize the parameters without any Gaussian
assumptions.
Multi-Domain Translation by Learning Uncoupled Autoencoders
Authors: Karren D. Yang, Caroline Uhler
Date: 2019-02-09 00:00:00
Tasks: Translation

Multi-domain translation seeks to learn a probabilistic coupling between
marginal distributions that reflects the correspondence between different
domains. We assume that data from different domains are generated from a shared
latent representation based on a structural equation model. Under this
assumption, we show that the problem of computing a probabilistic coupling
between marginals is equivalent to learning multiple uncoupled autoencoders
that embed to a given shared latent distribution. In addition, we propose a new
framework and algorithm for multi-domain translation based on learning the
shared latent distribution and training autoencoders under distributional
constraints. A key practical advantage of our framework is that new
autoencoders (i.e., new domains) can be added sequentially to the model without
retraining on the other domains, which we demonstrate experimentally on image
as well as genomics datasets.
Facial Micro-Expression Spotting and Recognition using Time Contrasted Feature with Visual Memory
Authors: Sauradip Nag, Ayan Kumar Bhunia, Aishik Konwer, Partha Pratim Roy
Date: 2019-02-09 00:00:00
Tasks: Micro-Expression Spotting

Facial micro-expressions are sudden involuntary minute muscle movements which
reveal true emotions that people try to conceal. Spotting a micro-expression
and recognizing it is a major challenge owing to its short duration and
intensity. Many works pursued traditional and deep learning based approaches to
solve this issue but compromised on learning low-level features and higher
accuracy due to unavailability of datasets. This motivated us to propose a
novel joint architecture of spatial and temporal network which extracts
time-contrasted features from the feature maps to contrast out micro-expression
from rapid muscle movements. The usage of time contrasted features greatly
improved the spotting of micro-expression from inconspicuous facial movements.
Also, we include a memory module to predict the class and intensity of the
micro-expression across the temporal frames of the micro-expression clip. Our
method achieves superior performance in comparison to other conventional
approaches on CASMEII dataset.