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ORCIDPKDD / ECML 2022: Grenoble, France - Part III
- Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part III. Lecture Notes in Computer Science 13715, Springer 2023, ISBN 978-3-031-26408-5
Deep Learning
- Zhenhe Wu, Liangqing Wu, Shuangyong Song, Jiahao Ji, Bo Zou, Zhoujun Li, Xiaodong He:
DialCSP: A Two-Stage Attention-Based Model for Customer Satisfaction Prediction in E-commerce Customer Service. 3-18 - Matteo Tiezzi, Simone Marullo, Alessandro Betti, Enrico Meloni, Lapo Faggi, Marco Gori, Stefano Melacci:
Foveated Neural Computation. 19-35 - Marcus de Carvalho, Mahardhika Pratama
, Jie Zhang, Yajuan Sun:
Class-Incremental Learning via Knowledge Amalgamation. 36-50 - Tingting Xuan, Giorgian Borca-Tasciuc, Yimin Zhu, Yu Sun, Cameron Dean, Zhaozhong Shi, Dantong Yu:
Trigger Detection for the sPHENIX Experiment via Bipartite Graph Networks with Set Transformer. 51-67 - Xiaoling Zhou, Ou Wu, Weiyao Zhu, Ziyang Liang:
Understanding Difficulty-Based Sample Weighting with a Universal Difficulty Measure. 68-84 - Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy:
Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks. 85-101 - Shaopu Wang, Xiaojun Chen, Mengzhen Kou, Jinqiao Shi:
PrUE: Distilling Knowledge from Sparse Teacher Networks. 102-117
Robust and Adversarial Machine Learning
- Hubert Baniecki, Wojciech Kretowicz, Przemyslaw Biecek:
Fooling Partial Dependence via Data Poisoning. 121-136 - Nikolaos Dionelis, Sotirios A. Tsaftaris, Mehrdad Yaghoobi:
FROB: Few-Shot ROBust Model for Joint Classification and Out-of-Distribution Detection. 137-153 - Tianle Zhang, Wenjie Ruan, Jonathan E. Fieldsend:
PRoA: A Probabilistic Robustness Assessment Against Functional Perturbations. 154-170 - Rafael Poyiadzi, Weisong Yang, Niall Twomey, Raúl Santos-Rodríguez:
Hypothesis Testing for Class-Conditional Label Noise. 171-186 - Max Klabunde, Florian Lemmerich:
On the Prediction Instability of Graph Neural Networks. 187-202 - Daniël Vos, Sicco Verwer:
Adversarially Robust Decision Tree Relabeling. 203-218 - Wenye Li, Fangchen Yu:
Calibrating Distance Metrics Under Uncertainty. 219-234 - Zikang Xiong, Joe Eappen, He Zhu, Suresh Jagannathan:
Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising. 235-250 - Zhihao Zhu, Chenwang Wu, Min Zhou, Hao Liao, Defu Lian, Enhong Chen:
Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation. 251-268 - Guoxin Sun, Tansu Alpcan, Benjamin I. P. Rubinstein, Seyit Camtepe:
Securing Cyber-Physical Systems: Physics-Enhanced Adversarial Learning for Autonomous Platoons. 269-285 - Federica Granese, Marine Picot, Marco Romanelli, Francesco Messina, Pablo Piantanida:
MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors. 286-303 - Alon Zolfi, Shai Avidan, Yuval Elovici, Asaf Shabtai:
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Models. 304-320
Generative Models
- Zhaobin Mo, Yongjie Fu, Daran Xu, Xuan Di:
TrafficFlowGAN: Physics-Informed Flow Based Generative Adversarial Network for Uncertainty Quantification. 323-339 - Chen Ling, Hengning Cao, Liang Zhao:
STGEN: Deep Continuous-Time Spatiotemporal Graph Generation. 340-356 - Jakob Drefs, Enrico Guiraud, Filippos Panagiotou, Jörg Lücke:
Direct Evolutionary Optimization of Variational Autoencoders with Binary Latents. 357-372 - Tanmoy Dam, Mahardhika Pratama, Md Meftahul Ferdaus, Sreenatha G. Anavatti, Hussein A. Abbass:
Scalable Adversarial Online Continual Learning. 373-389 - Zhixin Li, Jianwei Zhu, Jiahui Wei, Yufei Zeng:
Fine-Grained Bidirectional Attention-Based Generative Networks for Image-Text Matching. 390-406
Computer Vision
- Hao Hu, Federico Baldassarre, Hossein Azizpour:
Learnable Masked Tokens for Improved Transferability of Self-supervised Vision Transformers. 409-426 - Yang Yang, Min Li, Bo Meng, Zihao Huang, Junxing Ren, Degang Sun:
Rethinking the Misalignment Problem in Dense Object Detection. 427-442 - Raja Sunkara, Tie Luo:
No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects. 443-459 - Pritish Sahu, Kalliopi Basioti, Vladimir Pavlovic:
SAViR-T: Spatially Attentive Visual Reasoning with Transformers. 460-476 - Hiroaki Mikami, Kenji Fukumizu, Shogo Murai, Shuji Suzuki, Yuta Kikuchi, Taiji Suzuki, Shin-ichi Maeda, Kohei Hayashi:
A Scaling Law for Syn2real Transfer: How Much Is Your Pre-training Effective? 477-492 - Fengguang Su, Yu Zhu, Ou Wu, Yingjun Deng:
Submodular Meta Data Compiling for Meta Optimization. 493-511 - Hongfeng Han, Nanyi Fei, Zhiwu Lu, Ji-Rong Wen:
Supervised Contrastive Learning for Few-Shot Action Classification. 512-528 - Ramya Hebbalaguppe, Soumya Suvra Ghosal, Jatin Prakash, Harshad Khadilkar, Chetan Arora:
A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection Using Compounded Corruptions. 529-545 - Yuhui Guo, Xun Liang, Hui Tang, Xiangping Zheng, Bo Wu, Xuan Zhang:
Charge Own Job: Saliency Map and Visual Word Encoder for Image-Level Semantic Segmentation. 546-561 - Zheng Wang, Wenjie Ruan:
Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem. 562-577
Meta-learning, Neural Architecture Search
- Yiran Huang, Yexu Zhou, Michael Hefenbrock, Till Riedel, Likun Fang, Michael Beigl:
Automatic Feature Engineering Through Monte Carlo Tree Search. 581-598 - Zifu Wang, Matthew B. Blaschko:
MRF-UNets: Searching UNet with Markov Random Fields. 599-614 - Aroof Aimen, Bharat Ladrecha, Narayanan C. Krishnan:
Adversarial Projections to Tackle Support-Query Shifts in Few-Shot Meta-Learning. 615-630 - Aleksandra Nowak, Romuald A. Janik:
Discovering Wiring Patterns Influencing Neural Network Performance. 631-646 - Massinissa Hamidi, Aomar Osmani:
Context Abstraction to Improve Decentralized Machine Learning in Structured Sensing Environments. 647-663 - Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer:
Efficient Automated Deep Learning for Time Series Forecasting. 664-680
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