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Matbench: Benchmarks for materials science property prediction
a library to measure the python energy consumption of python code
A HuggingFace compatible Small Language Model trainer.
A large scale benchmark of materials design methods: https://www.nature.com/articles/s41524-024-01259-w
The PyExperimenter is a tool for the automatic execution of experiments, e.g. for machine learning (ML), capturing corresponding results in a unified manner in a database.
Critical difference diagrams with Python and Tikz
AI Energy Score: Initiative to establish comparable energy efficiency ratings for AI models.
Code and data used to create and evaluate LLM4Mat-Bench
Monash Scalable Time Series Evaluation Repository
A framework for few-shot evaluation of autoregressive language models.
Initiating a paradigm shift in reporting and helping with making ML advances more considerate of sustainability and trustworthiness.
Highly Scalable Time Series Classification for Very Large Datasets
Towards Verifying the Geometric Robustness of Large-scale Neural Networks - AAAI 2023
Automatically select DNNs for time series forecasting under consideration of complexity and resource consumption.
Minimalist optimization suite using Evolutionary Algorithms
Easily seed frameworks used for machine learning like Numpy and PyTorch using context managers.
Use your Python modules (code split up over mutiple files) for Kaggle submissions
MetaQuRe - code and data for rethinking and performing AutoML and meta-learning in a resource-aware way
Proximal Stochastic Gradient Descent (Tree) Ensembles
Simple function to plot multiple barplots in the same figure.