机器学习开源软件 (MLOSS)

为了支持开源软件运动，JMLR MLOSS 发表与非平凡机器学习算法、工具箱甚至科学计算语言实现相关的贡献。投稿说明请参阅此处。

TorchCP：一个用于保形预测的 Python 库: Jianguo Huang, Jianqing Song, Xuanning Zhou, Bingyi Jing, Hongxin Wei; (266):1−25, 2025.
[abs][pdf][bib] [code]

Talent：一个表格数据分析和学习工具箱: Si-Yang Liu, Hao-Run Cai, Qi-Le Zhou, Huai-Hong Yin, Tao Zhou, Jun-Peng Jiang, Han-Jia Ye; (226):1−16, 2025.
[abs][pdf][bib] [code]

BoFire：面向实际实验的贝叶斯优化框架: Johannes P. Dürholt, Thomas S. Asche, Johanna Kleinekorte, Gabriel Mancino-Ball, Benjamin Schiller, Simon Sung, Julian Keupp, Aaron Osburg, Toby Boyne, Ruth Misener, Rosona Eldred, Chrysoula Kappatou, Robert M. Lee, Dominik Linzner, Wagner Steuer Costa, David Walz, Niklas Wulkow, Behrang Shafei; (204):1−7, 2025.
[abs][pdf][bib] [code]

WEFE：一个用于衡量和减轻词嵌入中偏差的 Python 库: Pablo Badilla, Felipe Bravo-Marquez, María José Zambrano, Jorge Pérez; (156):1−6, 2025.
[abs][pdf][bib] [code]

skglm：改进 scikit-learn 用于正则化广义线性模型: Badr Moufad, Pierre-Antoine Bannier, Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias; (149):1−6, 2025.
[abs][pdf][bib] [code]

GraphNeuralNetworks.jl：使用 Julia 进行图深度学习: Carlo Lucibello, Aurora Rossi; (80):1−6, 2025.
[abs][pdf][bib] [code]

Ontolearn---一个用于在 Python 中进行大规模 OWL 类表达式学习的框架: Caglar Demir, Alkid Baci, N'Dah Jean Kouagou, Leonie Nora Sieger, Stefan Heindorf, Simon Bin, Lukas Blübaum, Alexander Bigerl, Axel-Cyrille Ngonga Ngomo; (63):1−6, 2025.
[abs][pdf][bib] [code]

Lightning UQ Box：神经网络的不确定性量化: Nils Lehmann, Nina Maria Gottschling, Jakob Gawlikowski, Adam J. Stewart, Stefan Depeweg, Eric Nalisnick; (54):1−7, 2025.
[abs][pdf][bib] [code]

PFLlib：一个易于上手且全面的个性化联邦学习库和基准: Jianqing Zhang, Yang Liu, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Jian Cao; (50):1−10, 2025.
[abs][pdf][bib] [code]

gsplat：用于高斯溅射的开源库: Vickie Ye, Ruilong Li, Justin Kerr, Matias Turkulainen, Brent Yi, Zhuoyang Pan, Otto Seiskari, Jianbo Ye, Jeffrey Hu, Matthew Tancik, Angjoo Kanazawa; (34):1−17, 2025.
[abs][pdf][bib] [code]

depyf：为机器学习研究人员打开 PyTorch 编译器的黑盒: Kaichao You, Runsheng Bai, Meng Cao, Jianmin Wang, Ion Stoica, Mingsheng Long; (25):1−18, 2025.
[abs][pdf][bib] [code]

PyDMD：一个用于鲁棒动态模式分解的 Python 包: Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo, Marco Tezzele, Karl Lapo, Gianluigi Rozza, Steven L. Brunton, J. Nathan Kutz; (417):1−9, 2024.
[摘要][pdf][bib] [代码]

KerasCV 和 KerasNLP：多框架模型: Matthew Watson, Divyashree Shivakumar Sreepathihalli, François Chollet, Martin Górner, Kiranbir Sodhia, Ramesh Sampath, Tirth Patel, Haifeng Jin, Neel Kovelamudi, Gabriel Rasskin, Samaneh Saadat, Luke Wood, Chen Qian, Jonathan Bischof, Ian Stenbit, Abheesht Sharma, Anshuman Mishra; (375):1−10, 2024.
[摘要][pdf][bib] [代码]

TopoX：用于拓扑域机器学习的 Python 包套件: Mustafa Hajij, Mathilde Papillon, Florian Frantzen, Jens Agerberg, Ibrahem AlJabea, Rubén Ballester, Claudio Battiloro, Guillermo Bernárdez, Tolga Birdal, Aiden Brent, Peter Chin, Sergio Escalera, Simone Fiorellino, Odin Hoff Gardaa, Gurusankar Gopalakrishnan, Devendra Govil, Josef Hoppe, Maneel Reddy Karri, Jude Khouja, Manuel Lecha, Neal Livesay, Jan Meißner, Soham Mukherjee, Alexander Nikitin, Theodore Papamarkou, Jaro Prílepok, Karthikeyan Natesan Ramamurthy, Paul Rosen, Aldo Guzmán-Sáenz, Alessandro Salatiello, Shreyas N. Samaga, Simone Scardapane, Michael T. Schaub, Luca Scofano, Indro Spinelli, Lev Telyatnikov, Quang Truong, Robin Walters, Maosheng Yang, Olga Zaghen, Ghada Zamzmi, Ali Zia, Nina Miolane; (374):1−8, 2024.
[摘要][pdf][bib] [代码]

PGMax：用于离散概率图模型的因子图和 JAX 中的循环信念传播: Guangyao Zhou, Antoine Dedieu, Nishanth Kumar, Wolfgang Lehrach, Shrinu Kushagra, Dileep George, Miguel Lázaro-Gredilla; (371):1−25, 2024.
[摘要][pdf][bib] [代码]

Aequitas Flow：简化公平机器学习实验: Sérgio Jesus, Pedro Saleiro, Inês Oliveira e Silva, Beatriz M. Jorge, Rita P. Ribeiro, João Gama, Pedro Bizarro, Rayid Ghani; (354):1−7, 2024.
[摘要][pdf][bib] [代码]

使用 SpeechBrain 1.0 的开源对话式 AI: Mirco Ravanelli, Titouan Parcollet, Adel Moumen, Sylvain de Langen, Cem Subakan, Peter Plantinga, Yingzhi Wang, Pooneh Mousavi, Luca Della Libera, Artem Ploujnikov, Francesco Paissan, Davide Borra, Salah Zaiem, Zeyu Zhao, Shucong Zhang, Georgios Karakasidis, Sung-Lin Yeh, Pierre Champion, Aku Rouhe, Rudolf Braun, Florian Mai, Juan Zuluaga-Gomez, Seyed Mahed Mousavi, Andreas Nautsch, Ha Nguyen, Xuechen Liu, Sangeet Sagar, Jarod Duret, Salima Mdhaffar, Gaëlle Laperrière, Mickael Rouvier, Renato De Mori, Yannick Estève; (333):1−11, 2024.
[摘要][pdf][bib] [代码]

RLtools：一个用于连续控制的快速、可移植的深度强化学习库: Jonas Eschmann, Dario Albani, Giuseppe Loianno; (301):1−19, 2024.
[摘要][pdf][bib] [代码]

PyPop7：一个纯 Python 实现的基于种群的黑盒优化库: Qiqi Duan, Guochen Zhou, Chang Shao, Zhuowei Wang, Mingyang Feng, Yuwei Huang, Yajing Tan, Yijun Yang, Qi Zhao, Yuhui Shi; (296):1−28, 2024.
[摘要][pdf][bib] [代码]

skscope：用于 Python 中的快速稀疏约束优化: Zezhi Wang, Junxian Zhu, Xueqin Wang, Jin Zhu, Huiyang Pen, Peng Chen, Anran Wang, Xiaoke Zhang; (290):1−9, 2024.
[摘要][pdf][bib] [代码]

aeon：一个用于时间序列学习的 Python 工具包: Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume, Christopher Holder, David Guijo-Rubio, Guzal Bulatova, Leonidas Tsaprounis, Lukasz Mentel, Martin Walter, Patrick Schäfer, Anthony Bagnall; (289):1−10, 2024.
[摘要][pdf][bib] [代码]

OmniSafe：一个用于加速安全强化学习研究的基础设施: Jiaming Ji, Jiayi Zhou, Borong Zhang, Juntao Dai, Xuehai Pan, Ruiyang Sun, Weidong Huang, Yiran Geng, Mickel Liu, Yaodong Yang; (285):1−6, 2024.
[摘要][pdf][bib] [代码]

Pearl: 一个生产就绪的强化学习智能体: Zheqing Zhu, Rodrigo de Salvo Braz, Jalaj Bhandari, Daniel Jiang, Yi Wan, Yonathan Efroni, Liyuan Wang, Ruiyang Xu, Hongbo Guo, Alex Nikulkov, Dmytro Korenkevych, Urun Dogan, Frank Cheng, Zheng Wu, Wanqiao Xu; (273):1−30, 2024.
[摘要][pdf][bib] [代码]

pgmpy: 用于贝叶斯网络的 Python 工具包: Ankur Ankan, Johannes Textor; (265):1−8, 2024.
[摘要][pdf][bib] [代码]

PromptBench：大型语言模型评估的统一库: Kaijie Zhu, Qinlin Zhao, Hao Chen, Jindong Wang, Xing Xie; (254):1−22, 2024.
[摘要][pdf][bib] [代码]

Fortuna：深度学习中不确定性量化的库: Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias Seeger, Andrew Gordon Wilson, Cedric Archambeau; (238):1−7, 2024.
[摘要][pdf][bib] [代码]

BenchMARL：多智能体强化学习基准测试: Matteo Bettini, Amanda Prorok, Vincent Moens; (217):1−10, 2024.
[摘要][pdf][bib] [代码]

PAMI：一个用于模式挖掘的 Python 库: Uday Kiran Rage, Veena Pamalla, Masashi Toyoda, Masaru Kitsuregawa; (209):1−6, 2024.
[摘要][pdf][bib] [代码]

DoWhy-GCM：DoWhy 用于图形因果模型的因果推断的扩展: Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing; (147):1−7, 2024.
[摘要][pdf][bib] [代码]

PyGOD：一个用于图异常检测的 Python 库: Kay Liu, Yingtong Dou, Xueying Ding, Xiyang Hu, Ruitong Zhang, Hao Peng, Lichao Sun, Philip S. Yu; (141):1−9, 2024.
[摘要][pdf][bib] [代码]

OpenBox：一个用于广义黑盒优化的 Python 工具包: Huaijun Jiang, Yu Shen, Yang Li, Beicheng Xu, Sixian Du, Wentao Zhang, Ce Zhang, Bin Cui; (120):1−11, 2024.
[摘要][pdf][bib] [代码]

QDax：一个用于质量多样性和基于种群的算法的具有硬件加速功能的库: Felix Chalumeau, Bryan Lim, Raphaël Boige, Maxime Allard, Luca Grillotti, Manon Flageat, Valentin Macé, Guillaume Richard, Arthur Flajolet, Thomas Pierrot, Antoine Cully; (108):1−16, 2024.
[摘要][pdf][bib] [代码]

ptwt - PyTorch 小波工具箱: Moritz Wolter, Felix Blanke, Jochen Garcke, Charles Tapley Hoyt; (80):1−7, 2024.
[摘要][pdf][bib] [代码]

关于部分观测扩散过程的无偏估计: Jeremy Heng, Jeremie Houssineau, Ajay Jasra; (66):1−66, 2024.
[摘要][pdf][bib] [代码]

Causal-learn: Python 中的因果发现: Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang; (60):1−8, 2024.
[摘要][pdf][bib] [代码]

不变和协变雷诺网络: Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai; (42):1−36, 2024.
[摘要][pdf][bib] [代码]

Pygmtools: 一个 Python 图匹配工具包: Runzhong Wang, Ziao Guo, Wenzheng Pan, Jiale Ma, Yikai Zhang, Nan Yang, Qi Liu, Longxuan Wei, Hanxue Zhang, Chang Liu, Zetian Jiang, Xiaokang Yang, Junchi Yan; (33):1−7, 2024.
[摘要][pdf][bib] [代码]

使用 t5x 和 seqio 扩展模型和数据: 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; (377):1−8, 2023.
[摘要][pdf][bib] [代码]

TorchOpt：一种高效的可微优化库: Jie Ren*, Xidong Feng*, Bo Liu*, Xuehai Pan*, Yao Fu, Luo Mai, Yaodong Yang; (367):1−14, 2023.
[摘要][pdf][bib] [代码]

Avalanche：用于深度持续学习的 PyTorch 库: Antonio Carta, Lorenzo Pellegrini, Andrea Cossu, Hamed Hemati, Vincenzo Lomonaco; (363):1−6, 2023.
[摘要][pdf][bib] [代码]

MARLlib：一个可扩展且高效的多智能体强化学习库: Siyi Hu, Yifan Zhong, Minquan Gao, Weixun Wang, Hao Dong, Xiaodan Liang, Zhihui Li, Xiaojun Chang, Yaodong Yang; (315):1−23, 2023.
[摘要][pdf][bib] [代码]

Fairlearn：评估和改进人工智能系统的公平性: Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio; (257):1−8, 2023.
[摘要][pdf][bib] [代码]

Torchhd：一个开源Python库，用于支持对超维计算和向量符号架构的研究: Mike Heddes, Igor Nunes, Pere Vergés, Denis Kleyko, Danny Abraham, Tony Givargis, Alexandru Nicolau, Alexander Veidenbaum; (255):1−10, 2023.
[摘要][pdf][bib] [代码]

skrl：用于强化学习的模块化和灵活的库: Antonio Serrano-Muñoz, Dimitrios Chrysostomou, Simon Bøgh, Nestor Arana-Arexolaleiba; (254):1−9, 2023.
[摘要][pdf][bib] [代码]

MultiZoo和MultiBench：用于多模态深度学习的标准工具包: Paul Pu Liang, Yiwei Lyu, Xiang Fan, Arav Agarwal, Yun Cheng, Louis-Philippe Morency, Ruslan Salakhutdinov; (234):1−7, 2023.
[摘要][pdf][bib] [代码]

Merlion：用于时间序列的端到端机器学习: 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; (226):1−6, 2023.
[摘要][pdf][bib] [代码]

LibMTL：用于深度多任务学习的 Python 库: Baijiong Lin, Yu Zhang; (209):1−7, 2023.
[摘要][pdf][bib] [代码]

L0Learn：用于使用 L0 正则化的稀疏学习的可扩展包: Hussein Hazimeh, Rahul Mazumder, Tim Nonet; (205):1−8, 2023.
[摘要][pdf][bib] [代码]

CodeLab 竞赛：组织科学挑战的开源平台: Adrien Pavao, Isabelle Guyon, Anne-Catherine Letournel, Dinh-Tuan Tran, Xavier Baro, Hugo Jair Escalante, Sergio Escalera, Tyler Thomas, Zhen Xu; (198):1−6, 2023.
[摘要][pdf][bib] [代码]

MALib: 基于种群的多智能体强化学习的并行框架: Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Yong Yu, Jun Wang, Weinan Zhang; (150):1−12, 2023.
[摘要][pdf][bib] [代码]

SQLFlow: 集成数据库和人工智能的可扩展工具包: Jun Zhou, Ke Zhang, Lin Wang, Hua Wu, Yi Wang, ChaoChao Chen; (116):1−9, 2023.
[摘要][pdf][bib] [代码]

FedLab：一个灵活的联邦学习框架: Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu; (100):1−7, 2023.
[摘要][pdf][bib] [代码]

Quantus：用于负责任地评估神经网络解释及其他内容的解释性 AI 工具包: Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne; (34):1−11, 2023.
[摘要][pdf][bib] [代码]

HiClass：与 Scikit-learn 兼容的本地层次分类的 Python 库: Fábio M. Miranda, Niklas Köhnecke, Bernhard Y. Renard; (29):1−17, 2023.
[摘要][pdf][bib] [代码]

分类难度对深度学习中权重矩阵谱的影响及在提前停止中的应用: Xuran Meng, Jeff Yao; (28):1−40, 2023.
[摘要][pdf][bib] [代码]

基于 LiNGAM 的因果发现的 Python 包: Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu; (14):1−8, 2023.
[摘要][pdf][bib] [代码]

AutoKeras：用于深度学习的 AutoML 库: Haifeng Jin, François Chollet, Qingquan Song, Xia Hu; (6):1−6, 2023.
[摘要][pdf][bib] [代码]

OMLT：优化与机器学习工具包: Francesco Ceccon, Jordan Jalving, Joshua Haddad, Alexander Thebelt, Calvin Tsay, Carl D Laird, Ruth Misener; (349):1−8, 2022.
[摘要][pdf][bib] [代码]

WarpDrive：GPU上的快速端到端深度多智能体强化学习: Tian Lan, Sunil Srinivasa, Huan Wang, Stephan Zheng; (316):1−6, 2022.
[摘要][pdf][bib] [代码]

d3rlpy：离线深度强化学习库: Takuma Seno, Michita Imai; (315):1−20, 2022.
[摘要][pdf][bib] [代码]

JsonGrinder.jl：自动可微神经网络，用于嵌入任意JSON数据: Šimon Mandlík, Matěj Račinský, Viliam Lisý, Tomáš Pevný; (298):1−5, 2022.
[摘要][pdf][bib] [代码]

ReservoirComputing.jl：用于储层计算模型的有效且模块化的库: Francesco Martinuzzi, Chris Rackauckas, Anas Abdelrehim, Miguel D. Mahecha, Karin Mora; (288):1−8, 2022.
[摘要][pdf][bib] [代码]

Deepchecks：用于测试和验证机器学习模型和数据的库: Shir Chorev, Philip Tannor, Dan Ben Israel, Noam Bressler, Itay Gabbay, Nir Hutnik, Jonatan Liberman, Matan Perlmutter, Yurii Romanyshyn, Lior Rokach; (285):1−6, 2022.
[摘要][pdf][bib] [代码]

CleanRL：高质量的深度强化学习算法单文件实现: Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye, Jeff Braga, Dipam Chakraborty, Kinal Mehta, João G.M. Araújo; (274):1−18, 2022.
[摘要][pdf][bib] [代码]

Tianshou：高度模块化的深度强化学习库: Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Yi Su, Hang Su, Jun Zhu; (267):1−6, 2022.
[摘要][pdf][bib] [代码]

abess：Python和R中的快速最佳子集选择库: Jin Zhu, Xueqin Wang, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, Junxian Zhu; (202):1−7, 2022.
[摘要][pdf][bib] [代码]

InterpretDL：PaddlePaddle中的深度模型解释: Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Zeyu Chen, Dejing Dou; (197):1−6, 2022.
[摘要][pdf][bib] [代码]

ktrain：用于增强机器学习的低代码库: Arun S. Maiya; (158):1−6, 2022.
[摘要][pdf][bib] [代码]

Darts：面向时间序列的友好型现代机器学习: Julien Herzen, Francesco Lässig, Samuele Giuliano Piazzetta, Thomas Neuer, Léo Tafti, Guillaume Raille, Tomas Van Pottelbergh, Marek Pasieka, Andrzej Skrodzki, Nicolas Huguenin, Maxime Dumonal, Jan Kościsz, Dennis Bader, Frédérick Gusset, Mounir Benheddi, Camila Williamson, Michal Kosinski, Matej Petrik, Gaël Grosch; (124):1−6, 2022.
[摘要][pdf][bib] [代码]

solo-learn：用于视觉表示学习的自监督方法的库: Victor Guilherme Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, Elisa Ricci; (56):1−6, 2022.
[摘要][pdf][bib] [代码]

SMAC3：用于超参数优化的多功能贝叶斯优化包: Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter; (54):1−9, 2022.
[摘要][pdf][bib] [代码]

DoubleML - Python中双重机器学习的面向对象实现: Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler; (53):1−6, 2022.
[摘要][pdf][bib] [代码]

用于多模态学习的工具箱 (scikit-multimodallearn): Dominique Benielli, Baptiste Bauvin, Sokol Koço, Riikka Huusari, Cécile Capponi, Hachem Kadri, François Laviolette; (51):1−7, 2022.
[摘要][pdf][bib] [代码]

Stable-Baselines3：可靠的强化学习实现: Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, Noah Dormann; (268):1−8, 2021.
[摘要][pdf][bib] [代码]

DIG：用于深入图深度学习研究的现成库: Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora M Oztekin, Xuan Zhang, Shuiwang Ji; (240):1−9, 2021.
[摘要][pdf][bib] [代码]

sklvq：Scikit Learning 向量量化: Rick van Veen, Michael Biehl, Gert-Jan de Vries; (231):1−6, 2021.
[摘要][pdf][bib] [代码]

FATE：一种工业级平台，用于具有数据保护功能的协同学习: Yang Liu, Tao Fan, Tianjian Chen, Qian Xu, Qiang Yang; (226):1−6, 2021.
[摘要][pdf][bib] [代码]

TensorHive：分布式机器学习工作负载的独占GPU访问管理: Paweł Rościszewski, Michał Martyniak, Filip Schodowski; (215):1−5, 2021.
[摘要][pdf][bib] [代码]

dalex: 使用交互式可解释性和公平性进行负责任的机器学习，Python 版: Hubert Baniecki, Wojciech Kretowicz, Piotr Piątyszek, Jakub Wiśniewski, Przemysław Biecek; (214):1−7, 2021.
[摘要][pdf][bib] [代码]

mlr3pipelines - R 中的灵活机器学习流水线: Martin Binder, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, Bernd Bischl; (184):1−7, 2021.
[摘要][pdf][bib] [代码]

Alibi Explain：解释机器学习模型的算法: Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti, Alexandru Coca; (181):1−7, 2021.
[摘要][pdf][bib] [代码]

用于灵活数值优化的ensmallen库: Ryan R. Curtin, Marcus Edel, Rahul Ganesh Prabhu, Suryoday Basak, Zhihao Lou, Conrad Sanderson; (166):1−6, 2021.
[摘要][pdf][bib] [代码]

MushroomRL: 简化强化学习研究: Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters; (131):1−5, 2021.
[摘要][pdf][bib] [代码]

River: Python 中流数据机器学习: Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphael Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem, Albert Bifet; (110):1−8, 2021.
[摘要][pdf][bib] [代码]

mvlearn: Python中的多视角机器学习: Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, Joshua T. Vogelstein; (109):1−7, 2021.
[摘要][pdf][bib] [代码]

OpenML-Python：一个用于 OpenML 的可扩展 Python API: Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter; (100):1−5, 2021.
[摘要][pdf][bib] [代码]

POT: Python 最优传输: Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer; (78):1−8, 2021.
[摘要][pdf][bib] [代码]

ChainerRL：一个深度强化学习库: Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, Takahiro Ishikawa; (77):1−14, 2021.
[摘要][pdf][bib] [代码]

GPU上的核操作，具有自动微分，无内存溢出: Benjamin Charlier, Jean Feydy, Joan Alexis Glaunès, François-David Collin, Ghislain Durif; (74):1−6, 2021.
[摘要][pdf][bib] [代码]

giotto-tda: : 用于机器学习和数据探索的拓扑数据分析工具包: Guillaume Tauzin, Umberto Lupo, Lewis Tunstall, Julian Burella Pérez, Matteo Caorsi, Anibal M. Medina-Mardones, Alberto Dassatti, Kathryn Hess; (39):1−6, 2021.
[摘要][pdf][bib] [代码]

Pykg2vec：知识图嵌入的Python库: Shih-Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque; (16):1−6, 2021.
[摘要][pdf][bib] [代码]

algcomparison: 使用 TETRAD 比较图形结构学习算法的性能: Joseph D. Ramsey, Daniel Malinsky, Kevin V. Bui; (238):1−6, 2020.
[摘要][pdf][bib] [代码]

Geomstats：用于机器学习中黎曼几何的 Python 包: Nina Miolane, Nicolas Guigui, Alice Le Brigant, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes, Thomas Gerald, Paul Chauchat, Christian Shewmake, Daniel Brooks, Bernhard Kainz, Claire Donnat, Susan Holmes, Xavier Pennec; (223):1−9, 2020.
[摘要][pdf][bib] [代码]

scikit-survival：一个基于 scikit-learn 构建的时间-事件分析库: Sebastian Pölsterl; (212):1−6, 2020.
[摘要][pdf][bib] [代码]

Scikit-network：Python 中的图分析: Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier; (185):1−6, 2020.
[摘要][pdf][bib] [代码]

apricot: 用于数据摘要的 Python 中的次模选择: Jacob Schreiber, Jeffrey Bilmes, William Stafford Noble; (161):1−6, 2020.
[摘要][pdf][bib] [代码]

metric-learn: Python 中的度量学习算法: William de Vazelhes, CJ Carey, Yuan Tang, Nathalie Vauquier, Aurélien Bellet; (138):1−6, 2020.
[摘要][pdf][bib] [代码]

通过上下文架构在图上进行概率学习: Davide Bacciu, Federico Errica, Alessio Micheli; (134):1−39, 2020.
[摘要][pdf][bib] [代码]

AI Explainability 360：理解数据和机器学习模型的扩展工具包: Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang; (130):1−6, 2020.
[摘要][pdf][bib] [代码]

Apache Mahout：分布式数据流系统上的机器学习: Robin Anil, Gokhan Capan, Isabel Drost-Fromm, Ted Dunning, Ellen Friedman, Trevor Grant, Shannon Quinn, Paritosh Ranjan, Sebastian Schelter, Özgür Yılmazel; (127):1−6, 2020.
[摘要][pdf][bib] [代码]

Tslearn，用于时间序列数据的机器学习工具包: Romain Tavenard, Johann Faouzi, Gilles Vandewiele, Felix Divo, Guillaume Androz, Chester Holtz, Marie Payne, Roman Yurchak, Marc Rußwurm, Kushal Kolar, Eli Woods; (118):1−6, 2020.
[摘要][pdf][bib] [代码]

GluonTS：Python 中的概率和神经网络时间序列建模: Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang; (116):1−6, 2020.
[摘要][pdf][bib] [代码]

MFE：迈向可重现的元特征提取: Edesio Alcobaça, Felipe Siqueira, Adriano Rivolli, Luís P. F. Garcia, Jefferson T. Oliva, André C. P. L. F. de Carvalho; (111):1−5, 2020.
[摘要][pdf][bib] [代码]

ThunderGBM：GPU 上的快速 GBDT 和随机森林: Zeyi Wen, Hanfeng Liu, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen; (108):1−5, 2020.
[摘要][pdf][bib] [代码]

AI-Toolbox：用于强化学习和规划的 C++ 库（带有 Python 绑定）: Eugenio Bargiacchi, Diederik M. Roijers, Ann Nowé; (102):1−12, 2020.
[摘要][pdf][bib] [代码]

pyDML：用于距离度量学习的 Python 库: Juan Luis Suárez, Salvador García, Francisco Herrera; (96):1−7, 2020.
[摘要][pdf][bib] [代码]

Cornac：多模态推荐系统的比较框架: Aghiles Salah, Quoc-Tuan Truong, Hady W. Lauw; (95):1−5, 2020.
[摘要][pdf][bib] [代码]

Kymatio: Python 中的散射变换: Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Muawiz Chaudhary, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine Cella, Michael Eickenberg; (60):1−6, 2020.
[摘要][pdf][bib] [代码]

GraKeL：Python 中的图核库: Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, Michalis Vazirgiannis; (54):1−5, 2020.
[摘要][pdf][bib] [代码]

pyts: 一个用于时间序列分类的 Python 包: Johann Faouzi, Hicham Janati; (46):1−6, 2020.
[摘要][pdf][bib] [代码]

Tensor Train 分解在 TensorFlow 上的实现 (T3F): Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan Oseledets; (30):1−7, 2020.
[摘要][pdf][bib] [代码]

ORCA：用于序数回归的Matlab/Octave工具箱: Javier Sánchez-Monedero, Pedro A. Gutiérrez, María Pérez-Ortiz; (125):1−5, 2019.
[摘要][pdf][bib] [代码]

PyOD：用于可扩展异常检测的 Python 工具箱: Yue Zhao, Zain Nasrullah, Zheng Li; (96):1−7, 2019.
[摘要][pdf][bib] [代码]

iNNvestigate 神经网络！: Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans; (93):1−8, 2019.
[摘要][pdf][bib] [代码]

AffectiveTweets：用于分析推文情感的 Weka 包: Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer, Saif M. Mohammad; (92):1−6, 2019.
[摘要][pdf][bib] [代码]

SMART：用于监督学习的开源数据标注平台: Rob Chew, Michael Wenger, Caroline Kery, Jason Nance, Keith Richards, Emily Hadley, Peter Baumgartner; (82):1−5, 2019.
[摘要][pdf][bib] [代码]

Picasso：用于R和Python中高维数据分析的稀疏学习库: Jason Ge, Xingguo Li, Haoming Jiang, Han Liu, Tong Zhang, Mengdi Wang, Tuo Zhao; (44):1−5, 2019.
[摘要][pdf][bib] [代码] [网页]

Pyro：深度通用概率编程: Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, Noah D. Goodman; (28):1−6, 2019.
[摘要][pdf][bib] [代码]

TensorLy: Python 中的张量学习: Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic; (26):1−6, 2019.
[摘要][pdf][bib] [代码]

spark-crowd：用于从众包大数据学习的 Spark 包: Enrique G. Rodrigo, Juan A. Aledo, José A. Gámez; (19):1−5, 2019.
[摘要][pdf][bib] [代码]

scikit-multilearn：用于多标签分类的 Python 库: Piotr Szymański, Tomasz Kajdanowicz; (6):1−22, 2019.
[摘要][pdf][bib] [代码]

Seglearn：一个用于学习序列和时间序列的 Python 包: David M. Burns, Cari M. Whyne; (83):1−7, 2018.
[摘要][pdf][bib] [代码] [网页]

Scikit-Multiflow：一种多输出流式学习框架: Jacob Montiel, Jesse Read, Albert Bifet, Talel Abdessalem; (72):1−5, 2018.
[摘要][pdf][bib] [代码]

OpenEnsembles：用于集成聚类的 Python 资源: Tom Ronan, Shawn Anastasio, Zhijie Qi, Pedro Henrique S. Vieira Tavares, Roman Sloutsky, Kristen M. Naegle; (26):1−6, 2018.
[摘要][pdf][bib] [网页] [代码]

ThunderSVM：GPU和CPU上的快速SVM库: Zeyi Wen, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen; (21):1−5, 2018.
[摘要][pdf][bib] [网页] [代码]

ELFI：无似然度推断引擎: Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski; (16):1−7, 2018.
[摘要][pdf][bib] [网页] [代码]

SGDLibrary：用于随机优化算法的 MATLAB 库: Hiroyuki Kasai; (215):1−5, 2018.
[摘要][pdf][bib] [代码]

tick: 一个用于统计学习的 Python 库，重点关注霍克斯过程和时变模型: Emmanuel Bacry, Martin Bompaire, Philip Deegan, Stéphane Gaïffas, Søren V. Poulsen; (214):1−5, 2018.
[摘要][pdf][bib] [代码] [网页]

KELP：基于内核的学习平台: Simone Filice, Giuseppe Castellucci, Giovanni Da San Martino, Alessandro Moschitti, Danilo Croce, Roberto Basili; (191):1−5, 2018.
[摘要][pdf][bib] [代码] [网页]

Pycobra：用于集成学习和可视化的 Python 工具箱: Benjamin Guedj, Bhargav Srinivasa Desikan; (190):1−5, 2018.
[摘要][pdf][bib] [代码] [网页]

HyperTools：用于洞察高维数据的 Python 工具箱: Andrew C. Heusser, Kirsten Ziman, Lucy L. W. Owen, Jeremy R. Manning; (152):1−6, 2018.
[摘要][pdf][bib] [代码] [网页]

openXBOW -- 介绍帕绍开源跨模态词袋模型工具包: Maximilian Schmitt, Björn Schuller; (96):1−5, 2017.
[摘要][pdf][bib] [代码]

MADP 工具箱：用于 (多) 智能体系统中的规划和学习的开源库: Frans A. Oliehoek, Matthijs T. J. Spaan, Bas Terwijn, Philipp Robbel, João V. Messias; (89):1−5, 2017.
[摘要][pdf][bib] [代码]

GPflow：一个基于 TensorFlow 的高斯过程库: Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman; (40):1−6, 2017.
[摘要][pdf][bib] [代码] [网页]

GFA：使用组因子分析对多个数据源进行探索性分析: Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski; (39):1−5, 2017.
[摘要][pdf][bib] [代码] [r-project.org]

POMDPs.jl：不确定性下的顺序决策框架: Maxim Egorov, Zachary N. Sunberg, Edward Balaban, Tim A. Wheeler, Jayesh K. Gupta, Mykel J. Kochenderfer; (26):1−5, 2017.
[摘要][pdf][bib] [代码] [网页]

Auto-WEKA 2.0：WEKA 中的自动模型选择和超参数优化: Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown; (25):1−5, 2017.
[摘要][pdf][bib] [代码] [网页]

JSAT：Java 统计分析工具，一个机器学习库: Edward Raff; (23):1−5, 2017.
[摘要][pdf][bib] [代码] [网页]

Imbalanced-learn：一个用于解决机器学习中不平衡数据集问题的 Python 工具箱: Guillaume Lemaître, Fernando Nogueira, Christos K. Aridas; (17):1−5, 2017.
[摘要][pdf][bib] [代码] [网页]

炼油厂：一个开源主题建模网络平台: Daeil Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth; (12):1−5, 2017.
[摘要][pdf][bib] [代码] [网页]

SnapVX：一种基于网络的凸优化求解器: David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosič, Stephen Boyd, Jure Leskovec; (4):1−5, 2017.
[摘要][pdf][bib] [代码] [stanford.edu]

fastFM: 一个用于因子分解机的库: Immanuel Bayer; (184):1−5, 2016.
[摘要][pdf][bib] [代码] [网页]

Megaman: Python 中的可扩展流形学习: James McQueen, Marina Meilă, Jacob VanderPlas, Zhongyue Zhang; (148):1−5, 2016.
[摘要][pdf][bib] [代码] [网页]

JCLAL：用于主动学习的Java框架: Oscar Reyes, Eduardo Pérez, María del Carmen Rodríguez-Hernández, Habib M. Fardoun, Sebastián Ventura; (95):1−5, 2016.
[摘要][pdf][bib] [代码]

LIBMF：共享内存系统中并行矩阵分解的库: Wei-Sheng Chin, Bo-Wen Yuan, Meng-Yuan Yang, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin; (86):1−5, 2016.
[摘要][pdf][bib] [代码]

CVXPY：嵌入 Python 的凸优化建模语言: Steven Diamond, Stephen Boyd; (83):1−5, 2016.
[摘要][pdf][bib] [代码] [网页]

通过训练卷积神经网络来比较图像块进行立体匹配: Jure Žbontar, Yann LeCun; (65):1−32, 2016.
[摘要][pdf][bib] [代码]

MLlib: Apache Spark 中的机器学习: Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar; (34):1−7, 2016.
[摘要][pdf][bib] [代码] [网页]

MEKA：WEKA的多标签/多目标扩展: Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes; (21):1−5, 2016.
[摘要][pdf][bib] [代码] [网页]

Harry：一种衡量字符串相似度的工具: Konrad Rieck, Christian Wressnegger; (9):1−5, 2016.
[摘要][pdf][bib] [代码] [网页]

partykit：R语言中递归分区模块化工具包: Torsten Hothorn, Achim Zeileis; (118):3905−3909, 2015.
[摘要][pdf][bib] [代码]

CEKA：用于挖掘集体智慧的工具: Jing Zhang, Victor S. Sheng, Bryce A. Nicholson, Xindong Wu; (88):2853−2858, 2015.
[摘要][pdf][bib] [代码]

pyGPs -- 一个用于高斯过程回归和分类的 Python 库: Marion Neumann, Shan Huang, Daniel E. Marthaler, Kristian Kersting; (80):2611−2616, 2015.
[摘要][pdf][bib] [代码]

Libra工具包，用于概率模型: Daniel Lowd, Amirmohammad Rooshenas; (75):2459−2463, 2015.
[摘要][pdf][bib] [代码]

RLPy：用于教育和研究的基于价值函数的强化学习框架: Alborz Geramifard, Christoph Dann, Robert H. Klein, William Dabney, Jonathan P. How; (46):1573−1578, 2015.
[摘要][pdf][bib] [代码]

Encog：Java和C#的互换机器学习模型库: Jeff Heaton; (36):1243−1247, 2015.
[摘要][pdf][bib] [代码] [网页]

flare包用于R中高维线性回归和精度矩阵估计: Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu; (18):553−557, 2015.
[摘要][pdf][bib] [代码] [网页]

介绍CURRENNT：慕尼黑开源CUDA循环神经网络工具包: Felix Weninger; (17):547−551, 2015.
[摘要][pdf][bib] [代码]

JCLEC中遗传编程算法的分类模块: Alberto Cano, José María Luna, Amelia Zafra, Sebastián Ventura; (15):491−494, 2015.
[摘要][pdf][bib] [代码]

SAMOA：可扩展的高级大规模在线分析: Gianmarco De Francisci Morales, Albert Bifet; (5):149−153, 2015.
[摘要][pdf][bib] [代码]

BayesOpt：用于非线性优化、实验设计和 Bandit 问题的贝叶斯优化库: Ruben Martinez-Cantin; (115):3915−3919, 2014.
[摘要][pdf][bib] [代码]

SPMF：一个 Java 开源模式挖掘库: Philippe Fournier-Viger, Antonio Gomariz, Ted Gueniche, Azadeh Soltani, Cheng-Wei Wu, Vincent S. Tseng; (104):3569−3573, 2014.
[摘要][pdf][bib] [代码]

手势识别工具包: Nicholas Gillian, Joseph A. Paradiso; (101):3483−3487, 2014.
[摘要][pdf][bib] [代码]

ooDACE 工具箱：一种灵活的面向对象克里金实现: Ivo Couckuyt, Tom Dhaene, Piet Demeester; (91):3183−3186, 2014.
[摘要][pdf][bib] [代码]

pystruct - 在 Python 中学习结构化预测: Andreas C. Müller, Sven Behnke; (59):2055−2060, 2014.
[摘要][pdf][bib] [代码]

Manopt，用于流形优化的Matlab工具箱: Nicolas Boumal, Bamdev Mishra, P.-A. Absil, Rodolphe Sepulchre; (42):1455−1459, 2014.
[摘要][pdf][bib] [代码]

带有高阶依赖性的条件随机场，用于序列标注和分割: Nguyen Viet Cuong, Nan Ye, Wee Sun Lee, Hai Leong Chieu; (28):981−1009, 2014.
[摘要][pdf][bib] [代码]

LIBOL：在线学习算法库: Steven C.H. Hoi, Jialei Wang, Peilin Zhao; (15):495−499, 2014.
[摘要][pdf][bib] [代码]

用于线性规划和大规模精度矩阵估计的FASTCLIME包: Haotian Pang, Han Liu, Robert Vanderbei; (14):489−493, 2014.
[摘要][pdf][bib] [代码]

信息理论估计器工具箱: Zoltán Szabó; (9):283−287, 2014.
[摘要][pdf][bib] [代码]

EnsembleSVM：使用支持向量机进行集成学习的库: Marc Claesen, Frank De Smet, Johan A.K. Suykens, Bart De Moor; (4):141−145, 2014.
[摘要][pdf][bib] [代码]

GURLS：用于监督学习的最小二乘法库: Andrea Tacchetti, Pavan K. Mallapragada, Matteo Santoro, Lorenzo Rosasco; (100):3201−3205, 2013.
[摘要][pdf][bib] [代码]

Divvy：快速且直观的探索性数据分析: Joshua M. Lewis, Virginia R. de Sa, Laurens van der Maaten; (98):3159−3163, 2013.
[摘要][pdf][bib] [代码] [网页]

用于挖掘定量关联规则的QuantMiner: Ansaf Salleb-Aouissi, Christel Vrain, Cyril Nortet, Xiangrong Kong, Vivek Rathod, Daniel Cassard; (97):3153−3157, 2013.
[摘要][pdf][bib] [代码]

用于 R-Java 的非负盲源分离 CAM 软件: Niya Wang, Fan Meng, Li Chen, Subha Madhavan, Robert Clarke, Eric P. Hoffman, Jianhua Xuan, Yue Wang; (88):2899−2903, 2013.
[摘要][pdf][bib] [代码]

BudgetedSVM：用于可扩展SVM近似的工具箱: Nemanja Djuric, Liang Lan, Slobodan Vucetic, Zhuang Wang; (84):3813−3817, 2013.
[摘要][pdf][bib] [代码]

Tapkee：高效的降维库: Sergey Lisitsyn, Christian Widmer, Fernando J. Iglesias Garcia; (72):2355−2359, 2013.
[摘要][pdf][bib] [代码]

Orange：Python中的数据挖掘工具箱: Janez Demšar, Tomaž Curk, Aleš Erjavec, Črt Gorup, Tomaž Hočevar, Mitar Milutinovič, Martin Možina, Matija Polajnar, Marko Toplak, Anže Starič, Miha Štajdohar, Lan Umek, Lan Žagar, Jure Žbontar, Marinka Žitnik, Blaž Zupan; (71):2349−2353, 2013.
[摘要][pdf][bib] [代码]

JKernelMachines：一个用于核机器的简单框架: David Picard, Nicolas Thome, Matthieu Cord; (43):1417−1421, 2013.
[摘要][pdf][bib] [代码]

GPstuff：使用高斯过程的贝叶斯建模: Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari; (35):1175−1179, 2013.
[摘要][pdf][bib] [代码]

MLPACK：一个可扩展的C++机器学习库: Ryan R. Curtin, James R. Cline, N. P. Slagle, William B. March, Parikshit Ram, Nishant A. Mehta, Alexander G. Gray; (24):801−805, 2013.
[摘要][pdf][bib] [代码]

基于C++模板的强化学习库：将代码与数学结合: Hervé Frezza-Buet, Matthieu Geist; (18):625−628, 2013.
[摘要][pdf][bib] [代码]

SVDFeature：基于特征的协同过滤工具包: Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, Yong Yu; (116):3619−3622, 2012.
[摘要][pdf][bib] [代码]

DARWIN：一个用于机器学习和计算机视觉研究与开发的框架: Stephen Gould; (113):3533−3537, 2012.
[摘要][pdf][bib] [代码]

Sally：将字符串嵌入向量空间的工具: Konrad Rieck, Christian Wressnegger, Alexander Bikadorov; (104):3247−3251, 2012.
[摘要][pdf][bib] [代码]

Oger：用于大规模序列处理的模块化学习架构: David Verstraeten, Benjamin Schrauwen, Sander Dieleman, Philemon Brakel, Pieter Buteneers, Dejan Pecevski; (96):2995−2998, 2012.
[摘要][pdf][bib] [代码]

PREA：个性化推荐算法工具包: Joonseok Lee, Mingxuan Sun, Guy Lebanon; (87):2699−2703, 2012.
[摘要][pdf][bib] [代码]

使用信念传播的主题建模工具箱: Jia Zeng; (73):2233−2236, 2012.
[摘要][pdf][bib] [代码]

DEAP：进化算法变得容易: Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, Christian Gagné; (70):2171−2175, 2012.
[摘要][pdf][bib] [代码]

Python的Pattern: Tom De Smedt, Walter Daelemans; (66):2063−2067, 2012.
[摘要][pdf][bib] [代码]

Jstacs：用于统计分析和生物序列分类的Java框架: Jan Grau, Jens Keilwagen, André Gohr, Berit Haldemann, Stefan Posch, Ivo Grosse; (62):1967−1971, 2012.
[摘要][pdf][bib] [代码]

glm-ie：广义线性模型推断和估计工具箱: Hannes Nickisch; (54):1699−1703, 2012.
[摘要][pdf][bib] [代码]

用于 R 的高维无向图估计大型包: Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman; (37):1059−1062, 2012.
[摘要][pdf][bib] [代码]

NIMFA：用于非负矩阵分解的 Python 库: Marinka Žitnik, Blaž Zupan; (30):849−853, 2012.
[摘要][pdf][bib] [代码]

GPLP：用于高斯过程回归的本地和并行计算工具箱: Chiwoo Park, Jianhua Z. Huang, Yu Ding; (26):775−779, 2012.
[摘要][pdf][bib] [代码]

ML-Flex：用于并行执行分类分析的灵活工具箱: Stephen R. Piccolo, Lewis J. Frey; (19):555−559, 2012.
[摘要][pdf][bib] [代码]

MULTIBOOST：一个多用途提升包: Djalel Benbouzid, Róbert Busa-Fekete, Norman Casagrande, François-David Collin, Balázs Kégl; (18):549−553, 2012.
[摘要][pdf][bib] [代码]

固定子空间分析工具箱: Jan Saputra Müller, Paul von Bünau, Frank C. Meinecke, Franz J. Király, Klaus-Robert Müller; (93):3065−3069, 2011.
[摘要][pdf][bib] [代码]

Scikit-learn: Python 中的机器学习: Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay; (85):2825−2830, 2011.
[摘要][pdf][bib] [代码]

LPmade：轻松进行链接预测: Ryan N. Lichtenwalter, Nitesh V. Chawla; (75):2489−2492, 2011.
[摘要][pdf][bib] [代码]

MULAN：一个用于多标签学习的Java库: Grigorios Tsoumakas, Eleftherios Spyromitros-Xioufis, Jozef Vilcek, Ioannis Vlahavas; (71):2411−2414, 2011.
[摘要][pdf][bib] [代码]

Waffles：一个机器学习工具包: Michael Gashler; (69):2383−2387, 2011.
[摘要][pdf][bib] [代码]

MSVMpack：一个多类支持向量机软件包: Fabien Lauer, Yann Guermeur; (66):2293−2296, 2011.
[摘要][pdf][bib] [代码]

arules R-Package 生态系统：分析大型交易数据集中的有趣模式: Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, Christian Buchta; (57):2021−2025, 2011.
[摘要][pdf][bib] [代码]

CARP：用于筛选优秀聚类算法的软件: Volodymyr Melnykov, Ranjan Maitra; (3):69−73, 2011.
[摘要][pdf][bib] [代码]

用于机器学习的高斯过程 (GPML) 工具箱: Carl Edward Rasmussen, Hannes Nickisch; (100):3011−3015, 2010.
[摘要][pdf][bib] [代码]

libDAI：用于图模型中离散近似推断的免费和开源C++库: Joris M. Mooij; (74):2169−2173, 2010.
[摘要][pdf][bib] [代码]

基于模型的 Boosting 2.0: Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner; (71):2109−2113, 2010.
[摘要][pdf][bib] [代码]

用于计算机辅助设计的代理建模和自适应采样工具箱: Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom Dhaene, Karel Crombecq; (68):2051−2055, 2010.
[摘要][pdf][bib] [代码]

SHOGUN机器学习工具箱: Sören Sonnenburg, Gunnar Rätsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien, Fabio de Bona, Alexander Binder, Christian Gehl, Vojtěch Franc; (60):1799−1802, 2010.
[摘要][pdf][bib] [代码]

FastInf：一个高效的近似推断库: Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elidan; (57):1733−1736, 2010.
[摘要][pdf][bib] [代码]

MOA：大规模在线分析: Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer; (52):1601−1604, 2010.
[摘要][pdf][bib] [代码]

SFO：次模函数优化的工具箱: Andreas Krause; (38):1141−1144, 2010.
[摘要][pdf][bib] [代码]

连续时间贝叶斯网络推理和学习引擎: Christian R. Shelton, Yu Fan, William Lam, Joon Lee, Jing Xu; (37):1137−1140, 2010.
[摘要][pdf][bib] [代码]

纠错输出码库: Sergio Escalera, Oriol Pujol, Petia Radeva; (20):661−664, 2010.
[摘要][pdf][bib] [代码]

DL-Learner：描述逻辑中的概念学习: Jens Lehmann; (91):2639−2642, 2009.
[摘要][pdf][bib] [代码]

RL-Glue：用于强化学习实验的语言无关软件: Brian Tanner, Adam White; (74):2133−2136, 2009.
[摘要][pdf][bib] [代码]

Dlib-ml：一个机器学习工具包: Davis E. King; (60):1755−1758, 2009.
[摘要][pdf][bib] [代码]

模型监控 (M2)：评估、比较和监控模型: Troy Raeder, Nitesh V. Chawla; (47):1387−1390, 2009.
[摘要][pdf][bib] [代码]

Java-ML：一个机器学习库: Thomas Abeel, Yves Van de Peer, Yvan Saeys; (34):931−934, 2009.
[摘要][pdf][bib] [代码]

Nieme：大规模能量模型: Francis Maes; (26):743−746, 2009.
[摘要][pdf][bib] [代码]

用于贝叶斯学习的 Python 环境：从知识和数据推断贝叶斯网络的结构: Abhik Shah, Peter Woolf; (6):159−162, 2009.
[摘要][pdf][bib] [代码]

JNCC2：朴素信度分类器2的Java实现: Giorgio Corani, Marco Zaffalon; (90):2695−2698, 2008.
[摘要][pdf][bib] [代码]

LIBLINEAR：大型线性分类库: Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin; (61):1871−1874, 2008.
[摘要][pdf][bib] [代码]

Shark: Christian Igel, Verena Heidrich-Meisner, Tobias Glasmachers; (33):993−996, 2008.
[摘要][pdf][bib] [代码]

用于局部加权投影回归的库: Stefan Klanke, Sethu Vijayakumar, Stefan Schaal; (21):623−626, 2008.
[摘要][pdf][bib] [代码]