pwc-archive/papers-with-abstracts huggingface dataset:
Robust $k$-means Clustering for Distributions with Two Moments
Authors: Yegor Klochkov, Alexey Kroshnin, Nikita Zhivotovskiy
Date: 2020-02-06 00:00:00
Tasks: Clustering, Vocal Bursts Valence Prediction

We consider the robust algorithms for the $k$-means clustering problem where a quantizer is constructed based on $N$ independent observations. Our main results are median of means based non-asymptotic excess distortion bounds that hold under the two bounded moments assumption in a general separable Hilbert space. In particular, our results extend the renowned asymptotic result of Pollard, 1981 who showed that the existence of two moments is sufficient for strong consistency of an empirically optimal quantizer in $\mathbb{R}^d$. In a special case of clustering in $\mathbb{R}^d$, under two bounded moments, we prove matching (up to constant factors) non-asymptotic upper and lower bounds on the excess distortion, which depend on the probability mass of the lightest cluster of an optimal quantizer. Our bounds have the sub-Gaussian form, and the proofs are based on the versions of uniform bounds for robust mean estimators.
Self-recognition in conversational agents
Authors: Yigit Oktar, Erdem Okur, Mehmet Turkan
Date: 2020-02-06 00:00:00
Tasks: Chatbot

In a standard Turing test, a machine has to prove its humanness to the judges. By successfully imitating a thinking entity such as a human, this machine then proves that it can also think. Some objections claim that Turing test is not a tool to demonstrate the existence of general intelligence or thinking activity. A compelling alternative is the Lovelace test, in which the agent must originate a product that the agent's creator cannot explain. Therefore, the agent must be the owner of an original product. However, for this to happen the agent must exhibit the idea of self and distinguish oneself from others. Sustaining the idea of self within the Turing test is still possible if the judge decides to act as a textual mirror. Self-recognition tests applied on animals through mirrors appear to be viable tools to demonstrate the existence of a type of general intelligence. Methodology here constructs a textual version of the mirror test by placing the agent as the one and only judge to figure out whether the contacted one is an other, a mimicker, or oneself in an unsupervised manner. This textual version of the mirror test is objective, self-contained, and devoid of humanness. Any agent passing this textual mirror test should have or can acquire a thought mechanism that can be referred to as the inner-voice, answering the original and long lasting question of Turing "Can machines think?" in a constructive manner still within the bounds of the Turing test. Moreover, it is possible that a successful self-recognition might pave way to stronger notions of self-awareness in artificial beings.
Random VLAD based Deep Hashing for Efficient Image Retrieval
Authors: Li Weng, Lingzhi Ye, Jiangmin Tian, Jiuwen Cao, Jianzhong Wang
Date: 2020-02-06 00:00:00
Tasks: Deep Hashing, Image Retrieval, Quantization, Retrieval

Image hash algorithms generate compact binary representations that can be quickly matched by Hamming distance, thus become an efficient solution for large-scale image retrieval. This paper proposes RV-SSDH, a deep image hash algorithm that incorporates the classical VLAD (vector of locally aggregated descriptors) architecture into neural networks. Specifically, a novel neural network component is formed by coupling a random VLAD layer with a latent hash layer through a transform layer. This component can be combined with convolutional layers to realize a hash algorithm. We implement RV-SSDH as a point-wise algorithm that can be efficiently trained by minimizing classification error and quantization loss. Comprehensive experiments show this new architecture significantly outperforms baselines such as NetVLAD and SSDH, and offers a cost-effective trade-off in the state-of-the-art. In addition, the proposed random VLAD layer leads to satisfactory accuracy with low complexity, thus shows promising potentials as an alternative to NetVLAD.
Social diversity and social preferences in mixed-motive reinforcement learning
Authors: Kevin R. McKee, Ian Gemp, Brian McWilliams, Edgar A. Duéñez-Guzmán, Edward Hughes, Joel Z. Leibo
Date: 2020-02-06 00:00:00
Tasks: Diversity, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent research on reinforcement learning in pure-conflict and pure-common interest games has emphasized the importance of population heterogeneity. In contrast, studies of reinforcement learning in mixed-motive games have primarily leveraged homogeneous approaches. Given the defining characteristic of mixed-motive games--the imperfect correlation of incentives between group members--we study the effect of population heterogeneity on mixed-motive reinforcement learning. We draw on interdependence theory from social psychology and imbue reinforcement learning agents with Social Value Orientation (SVO), a flexible formalization of preferences over group outcome distributions. We subsequently explore the effects of diversity in SVO on populations of reinforcement learning agents in two mixed-motive Markov games. We demonstrate that heterogeneity in SVO generates meaningful and complex behavioral variation among agents similar to that suggested by interdependence theory. Empirical results in these mixed-motive dilemmas suggest agents trained in heterogeneous populations develop particularly generalized, high-performing policies relative to those trained in homogeneous populations.
perm2vec: Graph Permutation Selection for Decoding of Error Correction Codes using Self-Attention
Authors: Nir Raviv, Avi Caciularu, Tomer Raviv, Jacob Goldberger, Yair Be'ery
Date: 2020-02-06 00:00:00

Error correction codes are an integral part of communication applications, boosting the reliability of transmission. The optimal decoding of transmitted codewords is the maximum likelihood rule, which is NP-hard due to the curse of dimensionality. For practical realizations, sub-optimal decoding algorithms are employed; yet limited theoretical insights prevent one from exploiting the full potential of these algorithms. One such insight is the choice of permutation in permutation decoding. We present a data-driven framework for permutation selection, combining domain knowledge with machine learning concepts such as node embedding and self-attention. Significant and consistent improvements in the bit error rate are introduced for all simulated codes, over the baseline decoders. To the best of the authors' knowledge, this work is the first to leverage the benefits of the neural Transformer networks in physical layer communication systems.
VGAI: End-to-End Learning of Vision-Based Decentralized Controllers for Robot Swarms
Authors: Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Zhangyang Wang, Alejandro Ribeiro, Brian M. Sadler
Date: 2020-02-06 00:00:00
Tasks: Graph Neural Network

Decentralized coordination of a robot swarm requires addressing the tension between local perceptions and actions, and the accomplishment of a global objective. In this work, we propose to learn decentralized controllers based on solely raw visual inputs. For the first time, that integrates the learning of two key components: communication and visual perception, in one end-to-end framework. More specifically, we consider that each robot has access to a visual perception of the immediate surroundings, and communication capabilities to transmit and receive messages from other neighboring robots. Our proposed learning framework combines a convolutional neural network (CNN) for each robot to extract messages from the visual inputs, and a graph neural network (GNN) over the entire swarm to transmit, receive and process these messages in order to decide on actions. The use of a GNN and locally-run CNNs results naturally in a decentralized controller. We jointly train the CNNs and the GNN so that each robot learns to extract messages from the images that are adequate for the team as a whole. Our experiments demonstrate the proposed architecture in the problem of drone flocking and show its promising performance and scalability, e.g., achieving successful decentralized flocking for large-sized swarms consisting of up to 75 drones.
Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting
Authors: Ziping Xu, Ambuj Tewari
Date: 2020-02-06 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We study reinforcement learning in non-episodic factored Markov decision processes (FMDPs). We propose two near-optimal and oracle-efficient algorithms for FMDPs. Assuming oracle access to an FMDP planner, they enjoy a Bayesian and a frequentist regret bound respectively, both of which reduce to the near-optimal bound $\widetilde{O}(DS\sqrt{AT})$ for standard non-factored MDPs. We propose a tighter connectivity measure, factored span, for FMDPs and prove a lower bound that depends on the factored span rather than the diameter $D$. In order to decrease the gap between lower and upper bounds, we propose an adaptation of the REGAL.C algorithm whose regret bound depends on the factored span. Our oracle-efficient algorithms outperform previously proposed near-optimal algorithms on computer network administration simulations.
Person Re-identification by Contour Sketch under Moderate Clothing Change
Authors: Qize Yang, An-Cong Wu, Wei-Shi Zheng
Date: 2020-02-06 00:00:00
Tasks: Person Re-Identification

Person re-identification (re-id), the process of matching pedestrian images across different camera views, is an important task in visual surveillance. Substantial development of re-id has recently been observed, and the majority of existing models are largely dependent on color appearance and assume that pedestrians do not change their clothes across camera views. This limitation, however, can be an issue for re-id when tracking a person at different places and at different time if that person (e.g., a criminal suspect) changes his/her clothes, causing most existing methods to fail, since they are heavily relying on color appearance and thus they are inclined to match a person to another person wearing similar clothes. In this work, we call the person re-id under clothing change the "cross-clothes person re-id". In particular, we consider the case when a person only changes his clothes moderately as a first attempt at solving this problem based on visible light images; that is we assume that a person wears clothes of a similar thickness, and thus the shape of a person would not change significantly when the weather does not change substantially within a short period of time. We perform cross-clothes person re-id based on a contour sketch of person image to take advantage of the shape of the human body instead of color information for extracting features that are robust to moderate clothing change. Due to the lack of a large-scale dataset for cross-clothes person re-id, we contribute a new dataset that consists of 33698 images from 221 identities. Our experiments illustrate the challenges of cross-clothes person re-id and demonstrate the effectiveness of our proposed method.
Artificial intelligence in medicine and healthcare: a review and classification of current and near-future applications and their ethical and social Impact
Authors: Emilio Gómez-González, Emilia Gomez, Javier Márquez-Rivas, Manuel Guerrero-Claro, Isabel Fernández-Lizaranzu, María Isabel Relimpio-López, Manuel E. Dorado, María José Mayorga-Buiza, Guillermo Izquierdo-Ayuso, Luis Capitán-Morales
Date: 2020-01-22 00:00:00

This paper provides an overview of the current and near-future applications of Artificial Intelligence (AI) in Medicine and Health Care and presents a classification according to their ethical and societal aspects, potential benefits and pitfalls, and issues that can be considered controversial and are not deeply discussed in the literature. This work is based on an analysis of the state of the art of research and technology, including existing software, personal monitoring devices, genetic tests and editing tools, personalized digital models, online platforms, augmented reality devices, and surgical and companion robotics. Motivated by our review, we present and describe the notion of 'extended personalized medicine', we then review existing applications of AI in medicine and healthcare and explore the public perception of medical AI systems, and how they show, simultaneously, extraordinary opportunities and drawbacks that even question fundamental medical concepts. Many of these topics coincide with urgent priorities recently defined by the World Health Organization for the coming decade. In addition, we study the transformations of the roles of doctors and patients in an age of ubiquitous information, identify the risk of a division of Medicine into 'fake-based', 'patient-generated', and 'scientifically tailored', and draw the attention of some aspects that need further thorough analysis and public debate.
Fair Correlation Clustering
Authors: Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian
Date: 2020-02-06 00:00:00
Tasks: Clustering, Combinatorial Optimization, Fairness

In this paper, we study correlation clustering under fairness constraints. Fair variants of $k$-median and $k$-center clustering have been studied recently, and approximation algorithms using a notion called fairlet decomposition have been proposed. We obtain approximation algorithms for fair correlation clustering under several important types of fairness constraints. Our results hinge on obtaining a fairlet decomposition for correlation clustering by introducing a novel combinatorial optimization problem. We define a fairlet decomposition with cost similar to the $k$-median cost and this allows us to obtain approximation algorithms for a wide range of fairness constraints. We complement our theoretical results with an in-depth analysis of our algorithms on real graphs where we show that fair solutions to correlation clustering can be obtained with limited increase in cost compared to the state-of-the-art (unfair) algorithms.
Using generative adversarial networks to synthesize artificial financial datasets
Authors: Dmitry Efimov, Di Xu, Luyang Kong, Alexey Nefedov, Archana Anandakrishnan
Date: 2020-02-06 00:00:00
Tasks: Benchmarking

Generative Adversarial Networks (GANs) became very popular for generation of realistically looking images. In this paper, we propose to use GANs to synthesize artificial financial data for research and benchmarking purposes. We test this approach on three American Express datasets, and show that properly trained GANs can replicate these datasets with high fidelity. For our experiments, we define a novel type of GAN, and suggest methods for data preprocessing that allow good training and testing performance of GANs. We also discuss methods for evaluating the quality of generated data, and their comparison with the original real data.
Looking GLAMORous: Vehicle Re-Id in Heterogeneous Cameras Networks with Global and Local Attention
Authors: Abhijit Suprem, Calton Pu
Date: 2020-02-06 00:00:00
Tasks: Vehicle Re-Identification

Vehicle re-identification (re-id) is a fundamental problem for modern surveillance camera networks. Existing approaches for vehicle re-id utilize global features and local features for re-id by combining multiple subnetworks and losses. In this paper, we propose GLAMOR, or Global and Local Attention MOdules for Re-id. GLAMOR performs global and local feature extraction simultaneously in a unified model to achieve state-of-the-art performance in vehicle re-id across a variety of adversarial conditions and datasets (mAPs 80.34, 76.48, 77.15 on VeRi-776, VRIC, and VeRi-Wild, respectively). GLAMOR introduces several contributions: a better backbone construction method that outperforms recent approaches, group and layer normalization to address conflicting loss targets for re-id, a novel global attention module for global feature extraction, and a novel local attention module for self-guided part-based local feature extraction that does not require supervision. Additionally, GLAMOR is a compact and fast model that is 10x smaller while delivering 25% better performance.
Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation
Authors: Cheng Chen, Qi Dou, Hao Chen, Jing Qin, Pheng Ann Heng
Date: 2020-02-06 00:00:00
Tasks: Domain Adaptation, Image Segmentation, Medical Image Segmentation, Organ Segmentation, Segmentation, Semantic Segmentation, Unsupervised Domain Adaptation

Unsupervised domain adaptation has increasingly gained interest in medical image computing, aiming to tackle the performance degradation of deep neural networks when being deployed to unseen data with heterogeneous characteristics. In this work, we present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA), to effectively adapt a segmentation network to an unlabeled target domain. Our proposed SIFA conducts synergistic alignment of domains from both image and feature perspectives. In particular, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features by leveraging adversarial learning in multiple aspects and with a deeply supervised mechanism. The feature encoder is shared between both adaptive perspectives to leverage their mutual benefits via end-to-end learning. We have extensively evaluated our method with cardiac substructure segmentation and abdominal multi-organ segmentation for bidirectional cross-modality adaptation between MRI and CT images. Experimental results on two different tasks demonstrate that our SIFA method is effective in improving segmentation performance on unlabeled target images, and outperforms the state-of-the-art domain adaptation approaches by a large margin.
Uncovering differential equations from data with hidden variables
Authors: Agustín Somacal, Yamila Barrera, Leonardo Boechi, Matthieu Jonckheere, Vincent Lefieux, Dominique Picard, Ezequiel Smucler
Date: 2020-02-06 00:00:00
Tasks: RTE, Time Series, Time Series Analysis

SINDy is a method for learning system of differential equations from data by solving a sparse linear regression optimization problem [Brunton et al., 2016]. In this article, we propose an extension of the SINDy method that learns systems of differential equations in cases where some of the variables are not observed. Our extension is based on regressing a higher order time derivative of a target variable onto a dictionary of functions that includes lower order time derivatives of the target variable. We evaluate our method by measuring the prediction accuracy of the learned dynamical systems on synthetic data and on a real data-set of temperature time series provided by the R\'eseau de Transport d'\'Electricit\'e (RTE). Our method provides high quality short-term forecasts and it is orders of magnitude faster than competing methods for learning differential equations with latent variables.
Towards Semantic Noise Cleansing of Categorical Data based on Semantic Infusion
Authors: Rishabh Gupta, Rajesh N Rao
Date: 2020-02-06 00:00:00
Tasks: Decision Making

Semantic Noise affects text analytics activities for the domain-specific industries significantly. It impedes the text understanding which holds prime importance in the critical decision making tasks. In this work, we formalize semantic noise as a sequence of terms that do not contribute to the narrative of the text. We look beyond the notion of standard statistically-based stop words and consider the semantics of terms to exclude the semantic noise. We present a novel Semantic Infusion technique to associate meta-data with the categorical corpus text and demonstrate its near-lossless nature. Based on this technique, we propose an unsupervised text-preprocessing framework to filter the semantic noise using the context of the terms. Later we present the evaluation results of the proposed framework using a web forum dataset from the automobile-domain.
Citation Data of Czech Apex Courts
Authors: Jakub Harašta, Tereza Novotná, Jaromír Šavelka
Date: 2020-02-06 00:00:00

In this paper, we introduce the citation data of the Czech apex courts (Supreme Court, Supreme Administrative Court and Constitutional Court). This dataset was automatically extracted from the corpus of texts of Czech court decisions - CzCDC 1.0. We obtained the citation data by building the natural language processing pipeline for extraction of the court decision identifiers. The pipeline included the (i) document segmentation model and the (ii) reference recognition model. Furthermore, the dataset was manually processed to achieve high-quality citation data as a base for subsequent qualitative and quantitative analyses. The dataset will be made available to the general public.
From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability
Authors: Ibai Lana, Javier J. Sanchez-Medina, Eleni I. Vlahogianni, Javier Del Ser
Date: 2020-02-06 00:00:00
Tasks: Decision Making

Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that {are} data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a ``story'' intensively producing and consuming large amounts of data. A~diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers' personal devices act as sources of data flows that are eventually fed {into} software running on automatic devices, actuators or control systems producing, in~turn, complex information flows {among} users, traffic managers, data analysts, traffic modeling scientists, etc. These~information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in~other words, for data-based models to fully become \emph{actionable}. Grounded in this described data modeling pipeline for ITS, we~define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We~deliberately generalize model learning to be adaptive, since, in~the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we~provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.
Global Convergence of Frank Wolfe on One Hidden Layer Networks
Authors: Alexandre d'Aspremont, Mert Pilanci
Date: 2020-02-06 00:00:00

We derive global convergence bounds for the Frank Wolfe algorithm when training one hidden layer neural networks. When using the ReLU activation function, and under tractable preconditioning assumptions on the sample data set, the linear minimization oracle used to incrementally form the solution can be solved explicitly as a second order cone program. The classical Frank Wolfe algorithm then converges with rate $O(1/T)$ where $T$ is both the number of neurons and the number of calls to the oracle.