Maximum Margin Separations in Finite Closure Systems
Florian Seiffarth, Tamás Horváth, and Stefan Wrobel
Discovering outstanding subgroup lists for numeric targets using MDL
Hugo M. Proença, Peter Grünwald, Thomas Bäck, and Matthijs van Leeuwen
A Relaxation-based Approach for Mining Diverse Closed Patterns
Arnold Hien, Samir Loudni, Noureddine Aribi, Yahia Lebbah, Mohammed El Amine Laghzaoui, Abdelkader Ouali, and Albrecht Zimmermann
OMBA: User-Guided Product Representations for Online Market Basket Analysis
Amila Silva, Ling Luo, Shanika Karunasekera, and Christopher Leckie
Online Binary Incomplete Multi-view Clustering
Longqi Yang, Liangliang Zhang, and Yuhua Tang
Utilizing Structure-rich Features to improve Clustering
Benjamin Schelling, Lena Greta Marie Bauer, Sahar Behzadi, and Claudia Plant
Simple, Scalable, and Stable Variational Deep Clustering
Lele Cao, Sahar Asadi, Wenfei Zhu, Christian Schmidli, and Michael Sjöberg
Gauss Shift: Density Attractor Clustering Faster than Mean Shift
Richard Leibrandt and Stephan Gunnemann
Privacy-Preserving Decision Trees — Training and Prediction
Adi Akavia, Max Leibovich, Yehezkel S. Resheff, Roey Ron, Moni Shahar, and Margarita Vald
Poisoning Attacks on Algorithmic Fairness
David Solans, Battista Biggio, and Carlos Castillo
SpecGreedy: Unified Dense Subgraph Detection
Wenjie Feng, Shenghua Liu, Danai Koutra, Huawei Shen, and Xueqi Cheng
Networked Point Process Models Under the Lens of Scrutiny
Guilherme Borges, Flavio Figueiredo, Renato Assunção, and Pedro O.S. Vaz-de-Melo
FB2vec: a Novel Representation Learning Model for Forwarding Behaviors on Online Social Networks
Li Ma, Mingding Liao, Xiaofeng Gao, Guoze Zhang, Qiang Yan, and Guihai Chen
A Framework for Deep Quantification Learning
Lei Qi, Mohammed Khaleel, Wallapak Tavanapong, Adisak Sukul, and David Peterson
PROMO for Interpretable Personalized Social Emotion Mining
Jason (Jiasheng) Zhang and Dongwon Lee
Progressive Supervision for Node Classification
Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi
Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN
Hansheng Xue, Luwei Yang, Wen Jiang, Yi Wei, Yi Hu, and Yu Lin
GIKT: A Graph-based Interaction Model for Knowledge Tracing
Yang Yang, Jian Shen, Yanru Qu, Yunfei Liu, Kerong Wang, Yaoming Zhu, Weinan Zhang, and Yong Yu
Simple and Effective Graph Autoencoders with One-Hop Linear Models
Guillaume Salha, Romain Hennequin, and Michalis Vazirgiannis
Sparse Separable Nonnegative Matrix Factorization
Nicolas Nadisic, Arnaud Vandaele, Jeremy E. Cohen, and Nicolas Gillis
Robust Domain Adaptation: Representations, Weights and Inductive Bias
Victor Bouvier, Philippe Very, Cl'ement Chastagnol, Myriam Tami, and C'eline Hudelot
Target to Source Coordinate-wise Adaptation of Pre-trained Models
Luxin Zhang, Pascal Germain, Yacine Kessaci, and Christophe Biernacki
Unsupervised Multi-Source Domain Adaptation for Regression
Guillaume Richard, Antoine de Mathelin, Georges H'ebrail, Mathilde Mougeot, and Nicolas Vayatis
Open Set Domain Adaptation using Optimal Transport
Marwa Kechaou, Romain Herault, Mokhtar Z. Alaya, and Gilles Gasso
Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search
Stephan S. Lorenzen and Ninh Pham
LOAD: LSH-Based ℓ₀-Sampling over Stream Data with Near-Duplicates
Dingzhu Lurong, Yanlong Wen, Jiangwei Zhang, and Xiaojie Yuan
Spatio-Temporal Tensor Sketching via Adaptive Sampling
Jing Ma, Qiuchen Zhang, Joyce C. Ho, and Li Xiong
Negar Safinianaini, Camila P. E. de Souza, Henrik Boström, and Jens Lagergren
Orthogonal Mixture of Hidden Markov Models
Poisson Graphical Granger Causality by Minimum Message Length
Katev rina Hlav'av ckov'a-Schindler and Claudia Plant
Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimation
Shonosuke Harada and Hisashi Kashima
Real-time Fine-grained Freeway Traffic State Estimation under Sparse Observation
Yangxin Lin, Yang Zhou, Shengyue Yao, Fan Ding, and Ping Wang
Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics
Yuxuan Liang, Kun Ouyang, Yiwei Wang, Ye Liu, Junbo Zhang, Yu Zheng, and David S. Rosenblum
RLTS: Robust Learning Time-series Shapelets
Akihiro Yamaguchi, Shigeru Maya, and Ken Ueno
Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model
Ding Zhou, Yuanjun Gao, and Liam Paninski
Predicting Future Classifiers for Evolving Non-linear Decision Boundaries
Kanishka Khandelwal, Devendra Dhaka, and Vivek Barsopia
Parameterless Semi-Supervised Anomaly Detection in Univariate Time Series
Oleg Iegorov and Sebastian Fischmeister
The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification
Matthew Middlehurst, James Large, Gavin Cawley, and Anthony Bagnall
Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory
Antonio Carta, Alessandro Sperduti, and Davide Bacciu
Flexible Recurrent Neural Networks
Anne Lambert, Françoise Le Bolzer, and François Schnitzler
Z-Embedding: A Spectral Representation of Event Intervals for Efficient Clustering and Classification
Zed Lee, Šarūnas Girdzijauskas, and Panagiotis Papapetrou
Neural Cross-Domain Collaborative Filtering with Shared Entities
Vijaikumar M, Shirish Shevade, and M N Murty
NoisyCUR: An algorithm for two-cost budgeted matrix completion
Dong Hu, Alex Gittens, and Malik Magdon-Ismail
ADMMiRNN: Training RNN with Stable Convergence via An Efficient ADMM Approach
Yu Tang, Zhigang Kan, Dequan Sun, Linbo Qiao, Jingjing Xiao, Zhiquan Lai, and Dongsheng Li
Exponential Convergence of Gradient Methods in Concave Network Zero-sum Games
Amit Kadan and Hu Fu
Adaptive Momentum Coefficient for Neural Network Optimization
Zana Rashidi, Kasra Ahmadi K. A., Aijun An, and Xiaogang Wang
Squeezing Correlated Neurons for Resource-Efficient Deep Neural Networks
Elbruz Ozen and Alex Orailoglu
Activation Anomaly Analysis
Philip Sperl, Jan-Philipp Schulze, and Konstantin Bottinger
Effective Version Space Reduction for Convolutional Neural Networks
Jiayu Liu, Ioannis Chiotellis, Rudolph Triebel, and Daniel Cremers
A Principle of Least Action for the Training of Neural Networks
Skander Karkar, Ibrahim Ayed, Emmanuel de B'ezenac, and Patrick Gallinari
Tackling Noise in Active Semi-Supervised Clustering
Jonas Soenen, Sebastijan Dumanv ci'c, Toon Van Craenendonck, amd Hendrik Blockeel
A Taxonomy of Interactive Online Machine Learning Strategies
Agnes Tegen, Paul Davidsson, and Jan A. Persson
Knowledge Elicitation using Deep Metric Learning and Psychometric Testing
Lu Yin, Vlado Menkovski, and Mykola Pechenizkiy
Adversarial Learned Molecular Graph Inference and Generation
Sebastian Pölsterl and Christian Wachinger
A Generic and Model-Agnostic Exemplar Synthetization Framework for Explainable AI
Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu, and Marius Popescu
Quality Guarantees for Autoencoders via Unsupervised Adversarial Attacks
Benedikt Böing, Rajarshi Roy, Emmanuel Müller, and Daniel Neider
Metric Learning for Categorical and Ambiguous Features: An Adversarial Method
Xiaochen Yang, Mingzhi Dong, Yiwen Guo, and Jing-Hao Xue
Learning Implicit Generative Models By Teaching Density Estimators
Kun Xu, Chao Du, Chongxuan Li, Jun Zhu, and Bo Zhang
Reprogramming GANs via Input Noise Design
Kangwook Lee, Changho Suh, and Kannan Ramchandran
On Saliency Maps and Adversarial Robustness
Puneet Mangla, Vedant Singh, and Vineeth N Balasubramanian
Scalable Backdoor Detection in Neural Networks
Haripriya Harikumar, Vuong Le, Santu Rana, Sourangshu Bhattacharya, Sunil Gupta, and Svetha Venkatesh
An Algorithmic Framework for Decentralised Matrix Factorisation
Erika Duriakova, Wěipéng Huáng, Elias Tragos, Aonghus Lawlor, Barry Smyth, James Geraci, and Neil Hurley
Federated Multi-view Matrix Factorization for Personalized Recommendations
Adrian Flanagan, Were Oyomno, Alexander Grigorievskiy, Kuan E. Tan, Suleiman A. Khan, and Muhammad Ammad-Ud-Din
FedMAX: Mitigating Activation Divergence for Effective Federated Learning
Wei Chen, Kartikeya Bhardwaj, and Radu Marculescu
Model-based Clustering with HDBSCAN*
Michael Strobl, Jorg Sander, Ricardo J. G. B. Campello, and Osmar Zaiane
Incremental Sensitivity Analysis for Kernelized Models
Hadar Sivan, Moshe Gabel, and Assaf Schuster
Off-the-grid: Fast and Effective Hyperparameter Search for Kernel Clustering
Bruno Ordozgoiti and Lluís Belanche Muñoz
Low-regret algorithms for strategic buyers with unknown valuations in repeated posted-price auctions
Jason Rhuggenaath, Paulo Roberto de Oliveira da Costa, Yingqian Zhang, Alp Akcay, and Uzay Kaymak
Partial Label Learning via Subspace Representation and Global Disambiguation
Yue Sun, Gengyu Lyu, and Songhe Feng
Online Partial Label Learning
Haobo Wang, Yuzhou Qiang, Chen Chen, Weiwei Liu, Tianlei Hu, Zhao Li, and Gang Chen
Network Cooperation with Progressive Disambiguation for Partial Label Learning
Yao Yao, Chen Gong, Jiehui Deng, and Jian Yang
Partial Label Learning via Self-Paced Curriculum Strategy
Gengyu Lyu, Songhe Feng, Yi Jin, and Yidong Li
Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning
Arjun Manoharan, Rahul Ramesh and Balaraman Ravindran
EgoMap: Projective mapping and structured egocentric memory
Edward Beeching, Jilles Dibangoye, Olivier Simonin, and Christian Wolf
ELSIM: End-to-end learning of reusable skills through intrinsic motivation
Arthur Aubret, Laetitia Matignon, and Salima Hassas
Graph Diffusion Wasserstein Distances
Amélie Barbe, Pierre Borgnat, Paulo Gonçalves, Marc Sebban, and Rémi Gribonval
Towards Interpretable Multi-Task Learning Using Bilevel Programming
Francesco Alesiani, Shujian Yu, Ammar Shaker, and Wenzhe Yin
Deep Learning, Grammar Transfer, and Transportation Theory
Kaixuan Zhang, Qinglong Wang, and C. Lee Giles
Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering
Xin Cong, Bowen Yu, Tingwen Liu, Shiyao Cui, Hengzhu Tang, and Bin Wang
Unsupervised Domain Adaptation with Joint Domain-Adversarial Reconstruction Networks
Qian Chen, Yuntao Du, Zhiwen Tan, Yi Zhang, and Chongjun Wang
Diversity-Based Generalization for Unsupervised Text Classification under Domain Shift
Jitin Krishnan, Hemant Purohit, and Huzefa Rangwala
On Local Optimizers of Acquisition Functions in Bayesian Optimization
Jungtaek Kim and Seungjin Choi
Bayesian Optimization with Missing Inputs
Phuc Luong, Dang Nguyen, Sunil Gupta, Santu Rana, and Svetha Venkatesh
Confusable Learning for Large-class Few-Shot Classification
Bingcong Li, Bo Han, Zhuowei Wang, Jing Jiang, and Guodong Long
Inductive Generalized Zero-shot Learning with Adversarial Relation Network
Guanyu Yang, Kaizhu Huang, Rui Zhang, John Y. Goulermas, and Amir Hussain
Algorithms for Optimizing the Ratio of Monotone k-Submodular Functions
Hau Chan, Grigorios Loukides, and Zhenghui Su
Mining Dense Subgraphs with Similar Edges
Polina Rozenshtein, Giulia Preti, Aristides Gionis, and Yannis Velegrakis
Orthant Based Proximal Stochastic Gradient Method for ℓ₁-Regularized Optimization
Tianyi Chen, Tianyu Ding, Bo Ji, Guanyi Wang, Yixin Shi, Jing Tian, Sheng Yi, Xiao Tu, and Zhihui Zhu
Efficiency of Coordinate Descent Methods For Structured Nonconvex Optimization
Qi Deng and Chenghao Lan
Escaping Saddle Points of Empirical Risk Privately and Scalably via DP-Trust Region Method
Di Wang and Jinhui Xu
To Ensemble or Not Ensemble: When does End-To-End Training Fail?
Andrew Webb, Charles Reynolds, Wenlin Chen, Henry Reeve, Dan Iliescu, Mikel Luj'an, and Gavin Brown
Learning Gradient Boosted Multi-label Classification Rules
Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Vu-Linh Nguyen, and Eyke Hüllermeier
Landmark-based Ensemble Learning with Random Fourier Features and Gradient Boosting
L'eo Gautheron, Pascal Germain, Amaury Habrard, Guillaume Metzler, Emilie Morvant, Marc Sebban, and Valentina Zantedeschi
A General Machine Learning Framework for Survival Analysis
Andreas Bender, David Rugamer, Fabian Scheipl, and Bernd Bischl
Fairness by Explicability and Adversarial SHAP Learning
James M. Hickey, Pietro G. Di Stefano, and Vlasios Vasileiou
End-to-End Learning for Prediction and Optimization with Gradient Boosting
Takuya Konishi and Takuro Fukunaga
Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule
Giorgio Corani, Dario Azzimonti, João P. S. C. Augusto, and Marco Zaffalon
Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions
Lorenzo Perini, Vincent Vercruyssen, and Jesse Davis
Finding the Optimal Network Depth in Classification Tasks
Bartosz Wójcik, Maciej Wołczyk, Klaudia Bałazy, and Jacek Tabor
Topological Insights into Sparse Neural Networks
Shiwei Liu, Tim Van der Lee, Anil Yaman, Zahra Atashgahi, Davide Ferraro, Ghada Sokar, Mykola Pechenizkiy, and Decebal Constantin Mocanu
GRAM-SMOT: Top-N Personalized Bundle Recommendation via Graph Attention Mechanism and Submodular Optimization
Vijaikumar M, Shirish Shevade, and M N Murty
Temporal Heterogeneous Interaction Graph Embedding For Next-Item Recommendation
Yugang Ji, MingYang Yin, Yuan Fang, Hongxia Yang, Xiangwei Wang, Tianrui Jia, and Chuan Shi
Node Classification in Temporal Graphs through Stochastic Sparsification and Temporal Structural Convolution
Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, and Wei Wang
DyHGCN: A Dynamic Heterogeneous Graph Convolutional Network to Learn Users' Dynamic Preferences for Information Diffusion Prediction
Chunyuan Yuan, Jiacheng Li, Wei Zhou, Yijun Lu, Xiaodan Zhang, and Songlin Hu
A Self-Attention Network based Node Embedding Model
Dai Quoc Nguyen, Tu Dinh Nguyen, and Dinh Phung
Graph-Revised Convolutional Network
Donghan Yu, Ruohong Zhang, Zhengbao Jiang, Yuexin Wu, and Yiming Yang
Robust Training of Graph Convolutional Networks via Latent Perturbation
Hongwei Jin and Xinhua Zhang
Enhancing Robustness of Graph Convolutional Networks via Dropping Graph Connections
Lingwei Chen, Xiaoting Li, and Dinghao Wu
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation
Victor Picheny, Vincent Dutordoir, Artem Artemev, and Nicolas Durrande
MUMBO: MUlti-task Max-value Bayesian Optimization
Henry B. Moss, David S. Leslie, and Paul Rayson
Interactive Multi-Objective Reinforcement Learning in Multi-Armed Bandits with Gaussian Process Utility Models
Diederik M. Roijers, Luisa M. Zintgraf, Pieter Libin, Mathieu Reymond, Eugenio Bargiacchi, and Ann Now'e
Deep Gaussian Processes using Expectation Propagation and Monte Carlo Methods
Gonzalo Hernández-Muñoz, Carlos Villacampa-Calvo, and Daniel Hernández-Lobato
Companion Guided Soft Margin for Face Recognition
Yingcheng Su, Yichao Wu, Zhenmao Li, Qiushan Guo, Ken Chen, Junjie Yan, Ding Liang, and Xiaolin Hu
Soft Labels Transfer with Discriminative Representations Learning for Unsupervised Domain Adaptation
Manliang Cao, Xiangdong Zhou, and Lan Lin
Information-Bottleneck Approach to Salient Region Discovery
Andrey Zhmoginov, Ian Fischer, and Mark Sandler
FAWA: Fast Adversarial Watermark Attack on Optical Character Recognition (OCR) Systems
Lu Chen, Jiao Sun, and Wei Xu
Less is More: Rejecting Unreliable Reviews for Product Question Answering
Shiwei Zhang, Xiuzhen Zhang, Jey Han Lau, Jeffrey Chan, and Cecile Paris
AMQAN: Adaptive Multi-Attention Question-Answer Networks for Answer Selection
Haitian Yang, Weiqing Huang, Xuan Zhao, Yan Wang, Yuyan Chen, Bin Lv, Rui Mao, and Ning Li
Inductive Document Representation Learning for Short Text Clustering
Junyang Chen, Zhiguo Gong, Wei Wang, Xiao Dong, Wei Wang, Weiwen Liu, Cong Wang, and Xian Chen
FUSE: Multi-Faceted Set Expansion by Coherent Clustering of Skip-grams
Wanzheng Zhu, Hongyu Gong, Jiaming Shen, Chao Zhang, Jingbo Shang, Suma Bhat, and Jiawei Han
Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis
Lingwei Wei, Dou Hu, Wei Zhou, Xuehai Tang, Xiaodan Zhang, Xin Wang, Jizhong Han, and Songlin Hu
Early Detection of Fake News with Multi-source Weak Social Supervision
Kai Shu, Guoqing Zheng, Yichuan Li, Subhabrata Mukherjee, Ahmed Hassan Awadallah, Scott Ruston, and Huan Liu
Generating Financial Reports from Macro News via Multiple edits Neural Networks
Wenxin Hu, Xiaofeng Zhang, and Yunpeng Ren
Continual Learning with Knowledge Transfer for Sentiment Classification
Zixuan Ke, Bing Liu, Hao Wang, and Lei Shu
Predictive Bi-Clustering Trees for Hierarchical Multi-label Classification
Bruna Z. Santos, Felipe K. Nakano, Ricardo Cerri, and Celine Vens
Self-Attention Enhanced Patient Journey Understanding in Healthcare System
Xueping Peng, Guodong Long, Tao Shen, Sen Wang, and Jing Jiang
MMCNN: A Multi-branch Multi-scale Convolutional Neural Network for Motor Imagery Classification
Ziyu Jia, Youfang Lin, Jing Wang, Kaixin Yang, Tianhang Liu, and Xinwang Zhang
Social Influence Attentive Neural Network for Friend-Enhanced Recommendation
Yuanfu Lu, Ruobing Xie, Chuan Shi, Yuan Fang, Wei Wang, Xu Zhang, and Leyu Lin
Feedback-guided Attributed Graph Embedding for Relevant Video Recommendation
Taofeng Xue, Xinzhou Dong, Wei Zhuo, Beihong Jin, He Chen, Wenhai Pan, Beibei Li, and Xuejian Zhang
Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach
Bowen Hao, Jing Zhang, Cuiping Li, Hong Chen, and Hongzhi Yin
Learning a Contextual and Topological Representation of Areas-of-Interest for On-Demand Delivery Application
Mingxuan Yue, Tianshu Sun, Fan Wu, Lixia Wu, Yinghui Xu, and Cyrus Shahabi
Strategic and Crowd-Aware Itinerary Recommendation
Junhua Liu, Kristin L. Wood, and Kwan Hui Lim
A Context-aware Approach to Detect Abnormal Human Behaviors
Roghayeh Mojarad, Ferhat Attal, Abdelghani Chibani, and Yacine Amirat
RADAR: Recurrent Autoencoder Based Detector for Adversarial Examples on Temporal EHR
Wenjie Wang, Pengfei Tang, Li Xiong, and Xiaoqian Jiang
Self-Supervised Log Parsing
Sasho Nedelkoski, Jasmin Bogatinovski, Alexander Acker, Jorge Cardoso, and Odej Kao
Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysis
Dilusha Weeraddana, Sudaraka MallawaArachchi, Tharindu Warnakula, Zhidong Li, and Yang Wang
Forecasting Error Pattern-based Anomaly Detection in Multivariate Time Series
Seoyoung Park, Siho Han, and Simon S. Woo
6VecLM: Language Modeling in Vector Space for IPv6 Target Generation
Tianyu Cui, Gang Xiong, Gaopeng Gou, Junzheng Shi, and Wei Xia
Calibrating User Response Predictions in Online Advertising
Chao Deng, Hao Wang, Qing Tan, Jian Xu, and Kun Gai
An Advert Creation System for 3D Product Placements
Ivan Bacher, Hossein Javidnia, Soumyabrata Dev, Rahul Agrahari, Murhaf Hossari, Matthew Nicholson, Clare Conran, Jian Tang, Peng Song, David Corrigan, and Francc
Estimating Precisions for Multiple Binary Classifiers Under Limited Samples
Rahul Tripathi, Srinivasan Jagannathan, and Balaji Dhamodharaswamy
Automation of Leasing Vehicle Return Assessment Using Deep Learning Models
Mohsan Jameel, Mofassir ul Islam Arif, Andre Hintsches, and Lars Schmidt-Thieme
A Route-Affecting Region Based Approach for Feature Extraction in Transportation Route Planning
Fandel Lin, Hsun-Ping Hsieh, and Jie-Yu Fang
Real-time Lane Configuration with Coordinated Reinforcement Learning
Udesh Gunarathna, Hairuo Xie, Egemen Tanin, Shanika Karunasekara, and Renata Borovica-Gajic
A Multi-Criteria System for Recommending Taxi Routes with an Advance Reservation
Jie-Yu Fang, Fandel Lin, and Hsun-Ping Hsieh
Autonomous Driving Validation with Model-Based Dictionary Clustering
Etienne Goffinet, Mustapha Lebbah, Hanane Azzag, and Loic Giraldi
Recognizing Complex Activities by a Temporal Causal Network-Based Model
Jun Liao, Junfeng Hu, and Li Liu
Unsupervised Human Pose Estimation on Depth Images
Thibault Blanc-Beyne, Axel Carlier, Sandrine Mouysset, and Vincent Charvillat
Data Generation Process Modeling for Activity Recognition
Massinissa Hamidi and Aomar Osmani
Learning Disentangled Representations of Satellite Image Time Series
Eduardo Hugo Sanchez, Mathieu Serrurier and Mathias Ortner
FlowFrontNet: Improving Carbon Composite Manufacturing with CNNs
Simon Stieber, Niklas Schroter, Alexander Schiendorfer, Alwin Hoffmann, and Wolfgang Reif
Learning I/O Access Patterns to Improve Prefetching in SSDs
Chandranil Chakraborttii and Heiner Litz
Interpretable Dimensionally-Consistent Feature Extraction from Electrical Network Sensors
Laure Crochepierre, Lydia Boudjeloud-Assala, and Vincent Barbesant
Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone
Yexu Zhou, Michael Hefenbrock, Yiran Huang, Till Riedel, and Michael Beigl
On-Site Gamma-Hadron Separation with Deep Learning on FPGAs
Sebastian Buschjager, Lukas Pfahler, Jens Buss, Katharina Morik, and Wolfgang Rhode
Benchmarking Tropical Cyclone Rapid Intensification with Satellite Images and Attention-based Deep Models
Ching-Yuan Bai, Buo-Fu Chen, and Hsuan-Tien Lin
Model Monitoring and Dynamic Model Selection in Travel Time-series Forecasting
Rosa Candela, Pietro Michiardi, Maurizio Filippone, and Maria A. Zuluaga
Learning to Simulate on Sparse Trajectory Data
Hua Wei, Chacha Chen, Chang Liu, Guanjie Zheng, and Zhenhui Li
Learning from Crowds via Joint Probabilistic Matrix Factorization and Clustering in Latent Space
Wuguannan Yao, Wonjung Lee, and Junhui Wang
Prediction of Global Navigation Satellite System Positioning Errors with Guarantees
Alejandro Kuratomi, Tony Lindgren, and Panagiotis Papapetrou
Confound Removal and Normalization in Practice: A Neuroimaging Based Sex Prediction Case Study
Shammi More, Simon B. Eichkoff, Julian Caspers, and Kaustubh R. Patil
Energy consumption forecasting using a stacked nonparametric Bayesian approach
Dilusha Weeraddana, Nguyen Lu Dang Khoa, Lachlan O'Neil, Weihong Wang, and Chen Cai
Reconstructing the Past: Applying Deep Learning to Reconstruct Pottery from Thousands Shards
Keeyoung Kim, JinSeok Hong, Sang-Hoon Rhee, and Simon S. Woo
CrimeForecaster: Crime Prediction by Exploiting the Geographical Neighborhoods' Spatiotemporal Dependencies
Jiao Sun, Mingxuan Yue, Zongyu Lin, Xiaochen Yang, Luciano Nocera, Gabriel Kahn, and Cyrus Shahabi
PS3: Partition-based Skew-Specialized Sampling for Batch Mode Active Learning in Imbalanced Text Data
Ricky Maulana Fajri, Samaneh Khoshrou, Robert Peharz, and Mykola Pechenizkiy
An Uncertainty-based Human-in-the-loop System for Industrial Tool Wear Analysis
Alexander Treiss, Jannis Walk, and Niklas Kuhl
Filling Gaps in Micro-Meteorological Data
Antoine Richard, Lior Fine, Offer Rozenstein, Josef Tanny, Matthieu Geist, and Cedric Pradalier
Lagrangian Duality for Constrained Deep Learning
Ferdinando Fioretto, Pascal Van Hentenryck, Terrence W.K. Mak, Cuong Tran, Federico Baldo, and Michele Lombardi
Few-Shot Microscopy Image Cell Segmentation
Youssef Dawoud, Julia Hornauer, Gustavo Carneiro, and Vasileios Belagiannis
Deep reinforcement learning for large-scale epidemic control
Pieter J.K. Libin, Arno Moonens, Timothy Verstraeten, Fabian Perez-Sanjines, Niel Hens, Philippe Lemey, and Ann Now'e
GLUECK: Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics
Cristian Axenie and Daria Kurz
Automated Integration of Genomic Metadata with Sequence-to-Sequence Models
Giuseppe Cannizzaro, Michele Leone, Anna Bernasconi, Arif Canakoglu, and Mark J. Carman
Explaining end-to-end ECG automated diagnosis using contextual features
Derick M. Oliveira, Antônio H. Ribeiro, João A.O. Pedrosa, Gabriela M.M. Paixão, Antonio Luiz P. Ribeiro, and Wagner Meira Jr.
A Deep Reinforcement Learning Framework for Optimal Trade Execution
Siyu Lin and Peter A. Beling
Detecting and predicting evidences of insider trading in the Brazilian market
Filipe Lauar and Cristiano Arbex Valle
Mend the Learning Approach, Not the Data: Insights for Ranking E-Commerce Products
Muhammad Umer Anwaar, Dmytro Rybalko, and Martin Kleinsteuber
Multi-future Merchant Transaction Prediction
Chin-Chia Michael Yeh, Zhongfang Zhuang, Wei Zhang, and Liang Wang
Think out of the package: Recommending package types for e-commerce shipments
Karthik S. Gurumoorthy, Subhajit Sanyal, and Vineet Chaoji
Topics in Financial Filings and Bankruptcy Prediction with Distributed Representations of Textual Data
Ba-Hung Nguyen, Shirai Kiyoaki, and Van-Nam Huynh
Why did my Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data
Yorick Spenrath, Marwan Hassani, Boudewijn van Dongen, and Haseeb Tariq
Fashion Outfit Generation for E-commerce
Elaine M. Bettaney, Stephen R. Hardwick, Odysseas Zisimopoulos, and Benjamin Paul Chamberlain
Improved Identification of Imbalanced Multiple Annotation Intent Labels with a Hybrid BLSTM and CNN model and Hybrid Loss Function
Supawit Vatathanavaro, Kitsuchart Pasupa, Sorratat Sirirattanajakarin, and Boontawee Suntisrivaraporn
Measuring Immigrants Adoption of Natives Shopping Consumption with Machine Learning
Riccardo Guidotti, Mirco Nanni, Fosca Giannotti, Dino Pedreschi, Simone Bertoli, Biagio Speciale, and Hillel Rapoport
Model Bridging: Connection between Simulation Model and Neural Network
Keiichi Kisamori, Keisuke Yamazaki, Yuto Komori, and Hiroshi Tokieda
Semi-Supervised Multi-aspect Detection of Misinformation using Hierarchical Joint Decomposition
Sara Abdali, Neil Shah, and Evangelos E. Papalexakis
A Deep Dive into Multilingual Hate Speech Classification
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha, and Animesh Mukherjee
Spatial Community-Informed Evolving Graphs for Demand Prediction
Qianru Wang, Bin Guo, Yi Ouyang, Kai Shu, Zhiwen Yu, and Huan Liu
SoccerMix: Representing Soccer Actions with Mixture Models
Tom Decroos, Maaike Van Roy, and Jesse Davis
Automatic Pass Annotation from Soccer Video Streams Based on Object Detection and LSTM
Danilo Sorano, Fabio Carrara, Paolo Cintia, Fabrizio Falchi, and Luca Pappalardo
SoccerMap: A Deep Learning Architecture for Visually-Interpretable Analysis in Soccer
Javier Fernández and Luke Bornn
Stop the Clock: Are Timeout Effects Real?
Niander Assis, Renato Assunção, and Pedro O.S. Vaz-de-Melo
Deep Reinforcement Learning (DRL) for portfolio allocation
Eric Benhamou, David Saltiel, Jean Jacques Ohana, Jamal Atif, and Rida Laraki
Automated Quality Assurance for Hand-held Tools via Embedded Classification and AutoML
Christoffer Loffler, Christian Nickel, Christopher Sobel, Daniel Dzibela, Jonathan Braat, Benjamin Gruhler, Philipp Woller, Nicolas Witt, and Christopher Mutschler
Massively Distributed Clustering via Dirichlet Process Mixture
Khadidja Meguelati, Benedicte Fontez, Nadine Hilgert, Florent Masseglia, and Isabelle Sanchez
AutoRec: A Comprehensive Platform for Building Effective and Explainable Recommender Models
Qing Cui, Qitao Shi, Hao Qian, Caizhi Tang, Xixi Li, Yiming Zhao, Tao Jiang, Longfei Li, and Jun Zhou
multi-imbalance: open source Python toolbox for multi-class imbalanced classification
Jacek Grycza, Damian Horna, Hanna Klimczak, Mateusz Lango, Kamil Pluci'nski, and Jerzy Stefanowski
VisualSynth: Democratizing Data Science in Spreadsheets
Cl'ement Gautrais, Yann Dauxais, Samuel Kolb, Arcchit Jain, Mohit Kumar, Stefano Teso, Elia Van Wolputte, Gust Verbruggen, and Luc De Raedt
FireAnt: Claim-based Medical Misinformation Detection and Monitoring
Branislav Pecher, Ivan Srba, Robert Moro, Matus Tomlein, and Maria Bielikova
GAMA: a General Automated Machine learning Assistant
Pieter Gijsbers and Joaquin Vanschoren
Instructional Video Summarization using Attentive Knowledge Grounding
Kyungho Kim, Kyungjae Lee, and Seung-won Hwang
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