pwc-archive/papers-with-abstracts huggingface dataset:
On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits
Authors: Roman Pogodin, Tor Lattimore
Date: 2019-03-19 00:00:00

We make three contributions to the theory of k-armed adversarial bandits. First, we prove a first-order bound for a modified variant of the INF strategy by Audibert and Bubeck [2009], without sacrificing worst case optimality or modifying the loss estimators. Second, we provide a variance analysis for algorithms based on follow the regularised leader, showing that without adaptation the variance of the regret is typically {\Omega}(n^2) where n is the horizon. Finally, we study bounds that depend on the degree of separation of the arms, generalising the results by Cowan and Katehakis [2015] from the stochastic setting to the adversarial and improving the result of Seldin and Slivkins [2014] by a factor of log(n)/log(log(n)).
Hybrid Approaches for our Participation to the n2c2 Challenge on Cohort Selection for Clinical Trials
Authors: Xavier Tannier, Nicolas Paris, Hugo Cisneros, Christel Daniel, Matthieu Doutreligne, Catherine Duclos, Nicolas Griffon, Claire Hassen-Khodja, Ivan Lerner, Adrien Parrot, Éric Sadou, Cyrina Saussol, Pascal Vaillant
Date: 2019-03-19 00:00:00

Objective: Natural language processing can help minimize human intervention in identifying patients meeting eligibility criteria for clinical trials, but there is still a long way to go to obtain a general and systematic approach that is useful for researchers. We describe two methods taking a step in this direction and present their results obtained during the n2c2 challenge on cohort selection for clinical trials. Materials and Methods: The first method is a weakly supervised method using an unlabeled corpus (MIMIC) to build a silver standard, by producing semi-automatically a small and very precise set of rules to detect some samples of positive and negative patients. This silver standard is then used to train a traditional supervised model. The second method is a terminology-based approach where a medical expert selects the appropriate concepts, and a procedure is defined to search the terms and check the structural or temporal constraints. Results: On the n2c2 dataset containing annotated data about 13 selection criteria on 288 patients, we obtained an overall F1-measure of 0.8969, which is the third best result out of 45 participant teams, with no statistically significant difference with the best-ranked team. Discussion: Both approaches obtained very encouraging results and apply to different types of criteria. The weakly supervised method requires explicit descriptions of positive and negative examples in some reports. The terminology-based method is very efficient when medical concepts carry most of the relevant information. Conclusion: It is unlikely that much more annotated data will be soon available for the task of identifying a wide range of patient phenotypes. One must focus on weakly or non-supervised learning methods using both structured and unstructured data and relying on a comprehensive representation of the patients.
Pose-Invariant Object Recognition for Event-Based Vision with Slow-ELM
Authors: Rohan Ghosh, Siyi Tang, Mahdi Rasouli, Nitish Thakor, Sunil Kukreja
Date: 2019-03-19 00:00:00
Tasks: CPU, Event-based vision, General Classification, Object Recognition, Pose Estimation

Neuromorphic image sensors produce activity-driven spiking output at every
pixel. These low-power consuming imagers which encode visual change information
in the form of spikes help reduce computational overhead and realize complex
real-time systems; object recognition and pose-estimation to name a few.
However, there exists a lack of algorithms in event-based vision aimed towards
capturing invariance to transformations. In this work, we propose a methodology
for recognizing objects invariant to their pose with the Dynamic Vision Sensor
(DVS). A novel slow-ELM architecture is proposed which combines the
effectiveness of Extreme Learning Machines and Slow Feature Analysis. The
system, tested on an Intel Core i5-4590 CPU, can perform 10,000 classifications
per second and achieves 1% classification error for 8 objects with views
accumulated over 90 degrees of 2D pose.
Cross Domain Knowledge Transfer for Unsupervised Vehicle Re-identification
Authors: Jinjia Peng, Huibing Wang, Tongtong Zhao, Xianping Fu
Date: 2019-03-19 00:00:00
Tasks: Domain Adaptation, Generative Adversarial Network, Image-to-Image Translation, Transfer Learning, Translation, Unsupervised Vehicle Re-Identification, Vehicle Re-Identification

Vehicle re-identification (reID) is to identify a target vehicle in different
cameras with non-overlapping views. When deploy the well-trained model to a new
dataset directly, there is a severe performance drop because of differences
among datasets named domain bias. To address this problem, this paper proposes
an domain adaptation framework which contains an image-to-image translation
network named vehicle transfer generative adversarial network (VTGAN) and an
attention-based feature learning network (ATTNet). VTGAN could make images from
the source domain (well-labeled) have the style of target domain (unlabeled)
and preserve identity information of source domain. To further improve the
domain adaptation ability for various backgrounds, ATTNet is proposed to train
generated images with the attention structure for vehicle reID. Comprehensive
experimental results clearly demonstrate that our method achieves excellent
performance on VehicleID dataset.
Class-incremental Learning via Deep Model Consolidation
Authors: Junting Zhang, Jie Zhang, Shalini Ghosh, Dawei Li, Serafettin Tasci, Larry Heck, Heming Zhang, C. -C. Jay Kuo
Date: 2019-03-19 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, image-classification, Image Classification, Incremental Learning, model, object-detection, Object Detection

Deep neural networks (DNNs) often suffer from "catastrophic forgetting" during incremental learning (IL) --- an abrupt degradation of performance on the original set of classes when the training objective is adapted to a newly added set of classes. Existing IL approaches tend to produce a model that is biased towards either the old classes or new classes, unless with the help of exemplars of the old data. To address this issue, we propose a class-incremental learning paradigm called Deep Model Consolidation (DMC), which works well even when the original training data is not available. The idea is to first train a separate model only for the new classes, and then combine the two individual models trained on data of two distinct set of classes (old classes and new classes) via a novel double distillation training objective. The two existing models are consolidated by exploiting publicly available unlabeled auxiliary data. This overcomes the potential difficulties due to the unavailability of original training data. Compared to the state-of-the-art techniques, DMC demonstrates significantly better performance in image classification (CIFAR-100 and CUB-200) and object detection (PASCAL VOC 2007) in the single-headed IL setting.
Personalized Neural Embeddings for Collaborative Filtering with Text
Authors: Guang-Neng Hu
Date: 2019-03-19 00:00:00
Proceeding: NAACL 2019 6
Tasks: Collaborative Filtering, Recommendation Systems, Word Embeddings

Collaborative filtering (CF) is a core technique for recommender systems.
Traditional CF approaches exploit user-item relations (e.g., clicks, likes, and
views) only and hence they suffer from the data sparsity issue. Items are
usually associated with unstructured text such as article abstracts and product
reviews. We develop a Personalized Neural Embedding (PNE) framework to exploit
both interactions and words seamlessly. We learn such embeddings of users,
items, and words jointly, and predict user preferences on items based on these
learned representations. PNE estimates the probability that a user will like an
item by two terms---behavior factors and semantic factors. On two real-world
datasets, PNE shows better performance than four state-of-the-art baselines in
terms of three metrics. We also show that PNE learns meaningful word embeddings
by visualization.
Hindsight Generative Adversarial Imitation Learning
Authors: Naijun Liu, Tao Lu, Yinghao Cai, Boyao Li, Shuo Wang
Date: 2019-03-19 00:00:00
Tasks: Imitation Learning, Reinforcement Learning, Reinforcement Learning (RL)

Compared to reinforcement learning, imitation learning (IL) is a powerful
paradigm for training agents to learn control policies efficiently from expert
demonstrations. However, in most cases, obtaining demonstration data is costly
and laborious, which poses a significant challenge in some scenarios. A
promising alternative is to train agent learning skills via imitation learning
without expert demonstrations, which, to some extent, would extremely expand
imitation learning areas. To achieve such expectation, in this paper, we
propose Hindsight Generative Adversarial Imitation Learning (HGAIL) algorithm,
with the aim of achieving imitation learning satisfying no need of
demonstrations. Combining hindsight idea with the generative adversarial
imitation learning (GAIL) framework, we realize implementing imitation learning
successfully in cases of expert demonstration data are not available.
Experiments show that the proposed method can train policies showing comparable
performance to current imitation learning methods. Further more, HGAIL
essentially endows curriculum learning mechanism which is critical for learning
policies.
Shrinking the Upper Confidence Bound: A Dynamic Product Selection Problem for Urban Warehouses
Authors: Rong Jin, David Simchi-Levi, Li Wang, Xinshang Wang, Sen Yang
Date: 2019-03-19 00:00:00

The recent rising popularity of ultra-fast delivery services on retail platforms fuels the increasing use of urban warehouses, whose proximity to customers makes fast deliveries viable. The space limit in urban warehouses poses a problem for the online retailers: the number of products (SKUs) they carry is no longer "the more, the better", yet it can still be significantly large, reaching hundreds or thousands in a product category. In this paper, we study algorithms for dynamically identifying a large number of products (i.e., SKUs) with top customer purchase probabilities on the fly, from an ocean of potential products to offer on retailers' ultra-fast delivery platforms. We distill the product selection problem into a semi-bandit model with linear generalization. There are in total $N$ different arms, each with a feature vector of dimension $d$. The player pulls $K$ arms in each period and observes the bandit feedback from each of the pulled arms. We focus on the setting where $K$ is much greater than the number of total time periods $T$ or the dimension of product features $d$. We first analyze a standard UCB algorithm and show its regret bound can be expressed as the sum of a $T$-independent part $\tilde O(K d^{3/2})$ and a $T$-dependent part $\tilde O(d\sqrt{KT})$, which we refer to as "fixed cost" and "variable cost" respectively. To reduce the fixed cost for large $K$ values, we propose a novel online learning algorithm, which iteratively shrinks the upper confidence bounds within each period, and show its fixed cost is reduced by a factor of $d$ to $\tilde O(K \sqrt{d})$. Moreover, we test the algorithms on an industrial dataset from Alibaba Group. Experimental results show that our new algorithm reduces the total regret of the standard UCB algorithm by at least 10%.
The Probabilistic Object Detection Challenge
Authors: John Skinner, David Hall, Haoyang Zhang, Feras Dayoub, Niko Sünderhauf
Date: 2019-03-19 00:00:00
Tasks: Object, object-detection, Object Detection

We introduce a new challenge for computer and robotic vision, the first ACRV
Robotic Vision Challenge, Probabilistic Object Detection. Probabilistic object
detection is a new variation on traditional object detection tasks, requiring
estimates of spatial and semantic uncertainty. We extend the traditional
bounding box format of object detection to express spatial uncertainty using
gaussian distributions for the box corners. The challenge introduces a new test
dataset of video sequences, which are designed to more closely resemble the
kind of data available to a robotic system. We evaluate probabilistic
detections using a new probability-based detection quality (PDQ) measure. The
goal in creating this challenge is to draw the computer and robotic vision
communities together, toward applying object detection solutions for practical
robotics applications.
A Note on KL-UCB+ Policy for the Stochastic Bandit
Authors: Junya Honda
Date: 2019-03-19 00:00:00

A classic setting of the stochastic K-armed bandit problem is considered in
this note. In this problem it has been known that KL-UCB policy achieves the
asymptotically optimal regret bound and KL-UCB+ policy empirically performs
better than the KL-UCB policy although the regret bound for the original form
of the KL-UCB+ policy has been unknown. This note demonstrates that a simple
proof of the asymptotic optimality of the KL-UCB+ policy can be given by the
same technique as those used for analyses of other known policies.
Turing-Completeness of Dynamics in Abstract Persuasion Argumentation
Authors: Ryuta Arisaka
Date: 2019-03-19 00:00:00

Abstract Persuasion Argumentation (APA) is a dynamic argumentation formalism
that extends Dung argumentation with persuasion relations. In this work, we
show through two-counter Minsky machine encoding that APA dynamics is
Turing-complete.
Non-negative representation based discriminative dictionary learning for face recognition
Authors: Zhe Chen, Xiao-Jun Wu, Josef Kittler
Date: 2019-03-19 00:00:00
Tasks: Dictionary Learning, Face Recognition, General Classification

In this paper, we propose a non-negative representation based discriminative dictionary learning algorithm (NRDL) for multicategory face classification. In contrast to traditional dictionary learning methods, NRDL investigates the use of non-negative representation (NR), which contributes to learning discriminative dictionary atoms. In order to make the learned dictionary more suitable for classification, NRDL seamlessly incorporates nonnegative representation constraint, discriminative dictionary learning and linear classifier training into a unified model. Specifically, NRDL introduces a positive constraint on representation matrix to find distinct atoms from heterogeneous training samples, which results in sparse and discriminative representation. Moreover, a discriminative dictionary encouraging function is proposed to enhance the uniqueness of class-specific sub-dictionaries. Meanwhile, an inter-class incoherence constraint and a compact graph based regularization term are constructed to respectively improve the discriminability of learned classifier. Experimental results on several benchmark face data sets verify the advantages of our NRDL algorithm over the state-of-the-art dictionary learning methods.
Fisher Discriminative Least Squares Regression for Image Classification
Authors: Zhe Chen, Xiao-Jun Wu, Josef Kittler
Date: 2019-03-19 00:00:00
Tasks: Classification, Face Recognition, General Classification, image-classification, Image Classification, regression

Discriminative least squares regression (DLSR) has been shown to achieve promising performance in multi-class image classification tasks. Its key idea is to force the regression labels of different classes to move in opposite directions by means of the proposed the joint use of the $\epsilon$-draggings technique, yielding discriminative regression model exhibiting wider margins, and the Fisher criterion. The $\epsilon$-draggings technique ignores an important problem: its non-negative relaxation matrix is dynamically updated in optimization, which means the dragging values can also cause the labels from the same class to be uncorrelated. In order to learn a more powerful discriminative projection, as well as regression labels, we propose a Fisher regularized DLSR (FDLSR) framework by constraining the relaxed labels using the Fisher criterion. On one hand, the Fisher criterion improves the intra-class compactness of the relaxed labels during relaxation learning. On the other hand, it is expected further to enhance the inter-class separability of $\epsilon$-draggings technique. FDLSR for the first time ever attempts to integrate the Fisher discriminant criterion and $\epsilon$-draggings technique into one unified model because they are absolutely complementary in learning discriminative projection. Extensive experiments on various datasets demonstrate that the proposed FDLSR method achieves performance that is superior to other state-of-the-art classification methods. The Matlab codes of this paper are available at https://github.com/chenzhe207/FDLSR.
Low-Rank Discriminative Least Squares Regression for Image Classification
Authors: Zhe Chen, Xiao-Jun Wu, Josef Kittler
Date: 2019-03-19 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, regression

Latest least squares regression (LSR) methods mainly try to learn slack regression targets to replace strict zero-one labels. However, the difference of intra-class targets can also be highlighted when enlarging the distance between different classes, and roughly persuing relaxed targets may lead to the problem of overfitting. To solve above problems, we propose a low-rank discriminative least squares regression model (LRDLSR) for multi-class image classification. Specifically, LRDLSR class-wisely imposes low-rank constraint on the intra-class regression targets to encourage its compactness and similarity. Moreover, LRDLSR introduces an additional regularization term on the learned targets to avoid the problem of overfitting. These two improvements are helpful to learn a more discriminative projection for regression and thus achieving better classification performance. Experimental results over a range of image databases demonstrate the effectiveness of the proposed LRDLSR method.
Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation
Authors: Yong Liu, Yinan Zhang, Qiong Wu, Chunyan Miao, Lizhen Cui, Binqiang Zhao, Yin Zhao, Lu Guan
Date: 2019-03-19 00:00:00
Tasks: Deep Reinforcement Learning, Diversity, Interactive Recommendation, Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Interactive recommendation that models the explicit interactions between
users and the recommender system has attracted a lot of research attentions in
recent years. Most previous interactive recommendation systems only focus on
optimizing recommendation accuracy while overlooking other important aspects of
recommendation quality, such as the diversity of recommendation results. In
this paper, we propose a novel recommendation model, named
\underline{D}iversity-promoting \underline{D}eep \underline{R}einforcement
\underline{L}earning (D$^2$RL), which encourages the diversity of
recommendation results in interaction recommendations. More specifically, we
adopt a Determinantal Point Process (DPP) model to generate diverse, while
relevant item recommendations. A personalized DPP kernel matrix is maintained
for each user, which is constructed from two parts: a fixed similarity matrix
capturing item-item similarity, and the relevance of items dynamically learnt
through an actor-critic reinforcement learning framework. We performed
extensive offline experiments as well as simulated online experiments with real
world datasets to demonstrate the effectiveness of the proposed model.
Machine Learning for removing EEG artifacts: Setting the benchmark
Authors: Subhrajit Roy
Date: 2019-03-19 00:00:00
Tasks: BIG-bench Machine Learning, EEG, Electroencephalogram (EEG)

Electroencephalograms (EEG) are often contaminated by artifacts which make
interpreting them more challenging for clinicians. Hence, automated artifact
recognition systems have the potential to aid the clinical workflow. In this
abstract, we share the first results on applying various machine learning
algorithms to the recently released world's largest open-source artifact
recognition dataset. We envision that these results will serve as a benchmark
for researchers who might work with this dataset in future.
Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning
Authors: Joseph Y. Cheng, Feiyu Chen, Christopher Sandino, Morteza Mardani, John M. Pauly, Shreyas S. Vasanawala
Date: 2019-03-19 00:00:00
Tasks: compressed sensing, Diagnostic

Compressed sensing in MRI enables high subsampling factors while maintaining
diagnostic image quality. This technique enables shortened scan durations
and/or improved image resolution. Further, compressed sensing can increase the
diagnostic information and value from each scan performed. Overall, compressed
sensing has significant clinical impact in improving the diagnostic quality and
patient experience for imaging exams. However, a number of challenges exist
when moving compressed sensing from research to the clinic. These challenges
include hand-crafted image priors, sensitive tuning parameters, and long
reconstruction times. Data-driven learning provides a solution to address these
challenges. As a result, compressed sensing can have greater clinical impact.
In this tutorial, we will review the compressed sensing formulation and outline
steps needed to transform this formulation to a deep learning framework.
Supplementary open source code in python will be used to demonstrate this
approach with open databases. Further, we will discuss considerations in
applying data-driven compressed sensing in the clinical setting.
A semi-supervised deep learning algorithm for abnormal EEG identification
Authors: Subhrajit Roy, Kiran Kate, Martin Hirzel
Date: 2019-03-19 00:00:00
Tasks: Deep Learning, EEG, Electroencephalogram (EEG)

Systems that can automatically analyze EEG signals can aid neurologists by reducing heavy workload and delays. However, such systems need to be first trained using a labeled dataset. While large corpuses of EEG data exist, a fraction of them are labeled. Hand-labeling data increases workload for the very neurologists we try to aid. This paper proposes a semi-supervised learning workflow that can not only extract meaningful information from large unlabeled EEG datasets but also make predictions with minimal supervision, using labeled datasets as small as 5 examples.
POP-CNN: Predicting Odor's Pleasantness with Convolutional Neural Network
Authors: Danli Wu, Yu Cheng, Dehan Luo, Kin-Yeung Wong, Kevin Hung, Zhijing Yang
Date: 2019-03-19 00:00:00

Predicting odor's pleasantness simplifies the evaluation of odors and has the
potential to be applied in perfumes and environmental monitoring industry.
Classical algorithms for predicting odor's pleasantness generally use a manual
feature extractor and an independent classifier. Manual designing a good
feature extractor depend on expert knowledge and experience is the key to the
accuracy of the algorithms. In order to circumvent this difficulty, we proposed
a model for predicting odor's pleasantness by using convolutional neural
network. In our model, the convolutional neural layers replace manual feature
extractor and show better performance. The experiments show that the
correlation between our model and human is over 90% on pleasantness rating. And
our model has 99.9% accuracy in distinguishing between absolutely pleasant or
unpleasant odors.