MLOSS 2023

20 papers

AutoKeras: An AutoML Library for Deep Learning Haifeng Jin, François Chollet, Qingquan Song, Xia Hu

Avalanche: A PyTorch Library for Deep Continual Learning Antonio Carta, Lorenzo Pellegrini, Andrea Cossu, Hamed Hemati, Vincenzo Lomonaco

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CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges Adrien Pavao, Isabelle Guyon, Anne-Catherine Letournel, Dinh-Tuan Tran, Xavier Baro, Hugo Jair Escalante, Sergio Escalera, Tyler Thomas, Zhen Xu

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Fairlearn: Assessing and Improving Fairness of AI Systems Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio

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FedLab: A Flexible Federated Learning Framework Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu

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HiClass: A Python Library for Local Hierarchical Classification Compatible with Scikit-Learn Fábio M. Miranda, Niklas Köhnecke, Bernhard Y. Renard

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Impact of Classification Difficulty on the Weight Matrices Spectra in Deep Learning and Application to Early-Stopping Xuran Meng, Jeff Yao

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L0Learn: A Scalable Package for Sparse Learning Using L0 Regularization Hussein Hazimeh, Rahul Mazumder, Tim Nonet

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LibMTL: A Python Library for Deep Multi-Task Learning Baijiong Lin, Yu Zhang

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MALib: A Parallel Framework for Population-Based Multi-Agent Reinforcement Learning Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Yong Yu, Jun Wang, Weinan Zhang

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MARLlib: A Scalable and Efficient Multi-Agent Reinforcement Learning Library Siyi Hu, Yifan Zhong, Minquan Gao, Weixun Wang, Hao Dong, Xiaodan Liang, Zhihui Li, Xiaojun Chang, Yaodong Yang

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Merlion: End-to-End Machine Learning for Time Series Aadyot Bhatnagar, Paul Kassianik, Chenghao Liu, Tian Lan, Wenzhuo Yang, Rowan Cassius, Doyen Sahoo, Devansh Arpit, Sri Subramanian, Gerald Woo, Amrita Saha, Arun Kumar Jagota, Gokulakrishnan Gopalakrishnan, Manpreet Singh, K C Krithika, Sukumar Maddineni, Daeki Cho, Bo Zong, Yingbo Zhou, Caiming Xiong, Silvio Savarese, Steven Hoi, Huan Wang

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MultiZoo and MultiBench: A Standardized Toolkit for Multimodal Deep Learning Paul Pu Liang, Yiwei Lyu, Xiang Fan, Arav Agarwal, Yun Cheng, Louis-Philippe Morency, Ruslan Salakhutdinov

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Python Package for Causal Discovery Based on LiNGAM Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu

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Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne

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Scaling up Models and Data with T5x and Seqio Adam Roberts, Hyung Won Chung, Gaurav Mishra, Anselm Levskaya, James Bradbury, Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, Curtis Hawthorne, Aitor Lewkowycz, Alex Salcianu, Marc van Zee, Jacob Austin, Sebastian Goodman, Livio Baldini Soares, Haitang Hu, Sasha Tsvyashchenko, Aakanksha Chowdhery, Jasmijn Bastings, Jannis Bulian, Xavier Garcia, Jianmo Ni, Andrew Chen, Kathleen Kenealy, Kehang Han, Michelle Casbon, Jonathan H. Clark, Stephan Lee, Dan Garrette, James Lee-Thorp, Colin Raffel, Noam Shazeer, Marvin Ritter, Maarten Bosma, Alexandre Passos, Jeremy Maitin-Shepard, Noah Fiedel, Mark Omernick, Brennan Saeta, Ryan Sepassi, Alexander Spiridonov, Joshua Newlan, Andrea Gesmundo

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Skrl: Modular and Flexible Library for Reinforcement Learning Antonio Serrano-Muñoz, Dimitrios Chrysostomou, Simon Bøgh, Nestor Arana-Arexolaleiba

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SQLFlow: An Extensible Toolkit Integrating DB and AI Jun Zhou, Ke Zhang, Lin Wang, Hua Wu, Yi Wang, ChaoChao Chen

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Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures Mike Heddes, Igor Nunes, Pere Vergés, Denis Kleyko, Danny Abraham, Tony Givargis, Alexandru Nicolau, Alexander Veidenbaum

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TorchOpt: An Efficient Library for Differentiable Optimization Jie Ren, Xidong Feng, Bo Liu, Xuehai Pan, Yao Fu, Luo Mai, Yaodong Yang

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