MIML library is a Java software tool to develop, test, and compare classification algorithms for multi-instance multi-label (MIML) learning. The library includes 43 algorithms and provides a specific format and facilities for data managing and partitioning, holdout and cross-validation methods, standard metrics for performance evaluation, and generation of reports. In addition, algorithms can be executed through $xml$ configuration files without needing to program. It is platform-independent, extensible, free, open-source, and available on GitHub under the GNU General Public License.
In this paper, we present Tianshou, a highly modularized Python library for deep reinforcement learning (DRL) that uses PyTorch as its backend. Tianshou intends to be research-friendly by providing a flexible and reliable infrastructure of DRL algorithms. It supports online and offline training with more than 20 classic algorithms through a unified interface. To facilitate related research and prove Tianshou's reliability, we have released Tianshou's benchmark of MuJoCo environments, covering eight classic algorithms with state-of-the-art performance. We open-sourced Tianshou at https://github…
skrl is an open-source modular library for reinforcement learning written in Python and designed with a focus on readability, simplicity, and transparency of algorithm implementations. In addition to supporting environments that use the traditional interfaces from OpenAI Gym and DeepMind, it provides the facility to load, configure, and operate NVIDIA Isaac Gym and NVIDIA Omniverse Isaac Gym environments. Furthermore, it enables the simultaneous training of several agents with customizable scopes (subsets of environments among all available ones), which may or may not share resources, in the s…
Towards Modular LLMs by Building and Reusing a Library of LoRAs
Microsoft Research（微软研究院）；Mila — Quebec AI Institute（米拉魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；University of Copenhagen（哥本哈根大学）；University of Edinburgh（爱丁堡大学）；HEC Montréal（蒙特利尔高等商学院）
University of Toronto（多伦多大学）；Carnegie Mellon University（卡内基梅隆大学）
Flexible implementations of Monte Carlo Tree Search (MCTS), combined with domain specific knowledge and hybridization with other search algorithms, can be powerful for finding the solutions to problems in complex planning. We introduce mctreesearch4j, an MCTS implementation written as a standard JVM library following key design principles of object oriented programming. We define key class abstractions allowing the MCTS library to flexibly adapt to any well defined Markov Decision Process or turn-based adversarial game. Furthermore, our library is designed to be modular and extensible, utilizi…
There are several bias mitigators that can reduce algorithmic bias in machine learning models but, unfortunately, the effect of mitigators on fairness is often not stable when measured across different data splits. A popular approach to train more stable models is ensemble learning. Ensembles, such as bagging, boosting, voting, or stacking, have been successful at making predictive performance more stable. One might therefore ask whether we can combine the advantages of bias mitigators and ensembles? To explore this question, we first need bias mitigators and ensembles to work together. We bui…
CleanDiffuser: An Easy-to-use Modularized Library for Diffusion Models in Decision Making
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；School of Computer and Information Technology, Shanxi University（山西大学计算机与信息技术学院）
Southern University of Science and Technology（南方科技大学）；Peng Cheng Laboratory（鹏城实验室）
This paper presents LibMTL, an open-source Python library built on PyTorch, which provides a unified, comprehensive, reproducible, and extensible implementation framework for Multi-Task Learning (MTL). LibMTL considers different settings and approaches in MTL, and it supports a large number of state-of-the-art MTL methods, including 12 loss weighting strategies, 7 architectures, and 84 combinations of different architectures and loss weighting methods. Moreover, the modular design in LibMTL makes it easy-to-use and well extensible, thus users can easily and fast develop new MTL methods, compar…
Indian Institute of Science（印度科学研究院）；University of Illinois Urbana-Champaign, Department of Computer Science（伊利诺伊大学厄巴纳-香槟分校计算机科学系）；University of Wisconsin（威斯康星大学）
TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs
Mila - Quebec AI Institute（魁北克AI研究所）；School of Computer Science, McGill University（麦吉尔大学计算机科学学院）；University of Oxford（牛津大学）；Department of computer science, University of Manitoba（曼尼托瓦大学计算机科学系）；AITHYRA；Stanford University（斯坦福大学）；DIRO, Université de Montréal（蒙特利尔大学DIRO）；CIFAR AI Chair（CIFAR人工智能主席）
This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization. With reproducibility and modularity in mind, this open-source library provides algorithms from the literature and a set of tools to design custom anomaly detection algorithms via a plug-and-play approach. Anomalib comprises state-of-the-art anomaly detection algorithms that achieve top performance on the benchmarks and that can be used off-the-shelf. In addition, the library provides components to design custom algorithms that could be tailored towards specific needs. Additional tools, includin…
Cornell Tech（康奈尔科技校区）；University of Copenhagen（哥本哈根大学）；Meta AI
PyTorch Adapt is a library for domain adaptation, a type of machine learning algorithm that re-purposes existing models to work in new domains. It is a fully-featured toolkit, allowing users to create a complete train/test pipeline in a few lines of code. It is also modular, so users can import just the parts they need, and not worry about being locked into a framework. One defining feature of this library is its customizability. In particular, complex training algorithms can be easily modified and combined, thanks to a system of composable, lazily-evaluated hooks. In this technical report, we…
torchmil: A PyTorch-based library for deep Multiple Instance Learning
a Department of Computer Science and Artificial Intelligence, University of Granada, Spain（计算机科学与人工智能系，格拉纳达大学）；b Research Centre for Information and Communications Technologies, University of Granada, Spain（信息与通信技术研究中心，格拉纳达大学）；c Department of Statistics and Operations Research, University of Granada, Spain（统计与运筹学系，格拉纳达大学）；d Institute of Mathematics (IMAG), University of Granada, Spain（数学研究所（IMAG），格拉纳达大学）
We present AdversariaLib, an open-source python library for the security evaluation of machine learning (ML) against carefully-targeted attacks. It supports the implementation of several attacks proposed thus far in the literature of adversarial learning, allows for the evaluation of a wide range of ML algorithms, runs on multiple platforms, and has multi-processing enabled. The library has a modular architecture that makes it easy to use and to extend by implementing novel attacks and countermeasures. It relies on other widely-used open-source ML libraries, including scikit-learn and FANN. Cl…
University College London（伦敦大学学院）；Great Ormond Street Hospital for Children（大奥蒙德街儿童医院）；Microsoft UK（微软英国）
Touchless computer interaction has become an important consideration during the COVID-19 pandemic period. Despite progress in machine learning and computer vision that allows for advanced gesture recognition, an integrated collection of such open-source methods and a user-customisable approach to utilising them in a low-cost solution for touchless interaction in existing software is still missing. In this paper, we introduce the MotionInput v2.0 application. This application utilises published open-source libraries and additional gesture definitions developed to take the video stream from a st…
Massachusetts Institute of Technology（麻省理工学院）；Istituto Italiano di Tecnologia（意大利理工学院）
We present GURLS, a least squares, modular, easy-to-extend software library for efficient supervised learning. GURLS is targeted to machine learning practitioners, as well as non-specialists. It offers a number state-of-the-art training strategies for medium and large-scale learning, and routines for efficient model selection. The library is particularly well suited for multi-output problems (multi-category/multi-label). GURLS is currently available in two independent implementations: Matlab and C++. It takes advantage of the favorable properties of regularized least squares algorithm to explo…
Unsupervised learning of disentangled representations is an open problem in machine learning. The Disentanglement-PyTorch library is developed to facilitate research, implementation, and testing of new variational algorithms. In this modular library, neural architectures, dimensionality of the latent space, and the training algorithms are fully decoupled, allowing for independent and consistent experiments across variational methods. The library handles the training scheduling, logging, and visualizations of reconstructions and latent space traversals. It also evaluates the encodings based on …
University of California-Irvine（加州大学欧文分校）；Siemens Corporate Technology（西门子企业技术公司）；University of Southern California（南加州大学）
Pykg2vec is an open-source Python library for learning the representations of the entities and relations in knowledge graphs. Pykg2vec's flexible and modular software architecture currently implements 16 state-of-the-art knowledge graph embedding algorithms, and is designed to easily incorporate new algorithms. The goal of pykg2vec is to provide a practical and educational platform to accelerate research in knowledge graph representation learning. Pykg2vec is built on top of TensorFlow and Python's multiprocessing framework and provides modules for batch generation, Bayesian hyperparameter opt…
University of Chicago（芝加哥大学）；Virginia Tech（弗吉尼亚理工大学）；Argonne National Laboratory（阿贡国家实验室）
The field of neuromorphic computing is in a period of active exploration. While many tools have been developed to simulate neuronal dynamics or convert deep networks to spiking models, general software libraries for learning rules remain underexplored. This is partly due to the diverse, challenging nature of efforts to design new learning rules, which range from encoding methods to gradient approximations, from population approaches that mimic the Bayesian brain to constrained learning algorithms deployed on memristor crossbars. To address this gap, we present Neko, a modular, extensible libra…
Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators
The Hartree Centre, STFC, Sci-Tech Daresbury, Warrington, WA4 4AD, United Kingdom（哈特里中心，英国科学与技术研究院，达尔斯伯里科技，沃林顿，WA4 4AD，英国）；IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA（IBM量子，IBM沃森研究中心，Yorktown Heights，纽约10598，美国）；IBM Quantum, IBM Research Europe – Dublin, Ireland（IBM量子，IBM欧洲研究院–都柏林，爱尔兰）；IBM Research – UK（IBM英国研究院）；IBM Quantum, IBM Research – Tokyo, Tokyo 103-8510, Japan（IBM量子，IBM东京研究院，东京103-8510，日本）
