TRIPROBE is a multi-level probing framework designed to diagnose task separability in machine learning pipelines. It evaluates separability across input spaces, learned feature representations, and classifier outputs by decomposing multi-task problems into binary subtasks and applying Foundational, Latent, and Final probes. Using Maximum Fisher's Discriminant Ratio, TRIPROBE identifies bottlenecks and task pairs that affect performance, as demonstrated on the Roshambo sEMG benchmark.
By Amirhossein Sadough, Freek Hens, Aleksa Bok\v{s}an, Mohammad Mahdi Dehshibi, Mahyar Shahsavari
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By Tanay Varshney, Annie Surla, Michelle Xu, Gomathy Venkata Krishnan, Maximilian Jeblick, David Austin, Neal Vaidya, Davide Onofrio
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arXiv:2609.36679v1 Announce Type: new
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By Xin Yu, Lizhu Zhang, Jiamu Bai, Yanhong Wu, Zellux Wang, Serena Li, Weiwei Li, Lingzhou Xue, Xiangjun Fan, Bo Peng
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By Zeyu Chen, Huanjin Yao, Ziwang Zhao, Min Yang
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By Konstantinos Kontras, Teodora Gagaleska, Thomas Strypsteen, Christos Chatzichristos, Matthew Blaschko, Maarten De Vos, Paul Pu Liang
arXiv:2507. 09471v4 Announce Type: replace Abstract: Continual Learning (CL) empowers AI models to continuously learn from sequential task streams.
By Lingfeng He, De Cheng, Zhiheng Ma, Huaijie Wang, Dingwen Zhang, Nannan Wang, Xinbo Gao
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By Tianyi Men, Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao
arXiv:2509.24367v2 Announce Type: replace
Abstract: Deepfake generators evolve rapidly, making exhaustive data collection and repeated retraining impractical. Unlike generic multi-task settings, deep...
By Jinhee Park, Guisik Kim, Choongsang Cho, Junseok Kwon
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By Julian Skirzynski, Harry Cheon, Shreyas Kadekodi, Meredith Stewart, Berk Ustun
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By Anqi Li, Jie Zhang, Zhongqi Wang, Songkai Xue, Jiahao Wang, Shiguang Shan, Xilin Chen
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By Yucheng Jiang, Zora Zhiruo Wang, Ruishi Chen, Diyi Yang