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Found 5 papers, 4 papers with code
SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients
In recent times, machine learning has joined the more established property prediction models.
A smile is all you need: Predicting limiting activity coefficients from SMILES with natural language processing
This training strategy enables SPT to accurately predict limiting activity coefficients even for unknown molecules, cutting the mean prediction error in half compared to state-of-the-art models for activity coefficient predictions such as COSMO-RS, UNIFAC, and improving on recent machine learning approaches.
Evaluating Large Language Models Trained on Code
We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities.
Ranked #1 on
Multi-task Language Understanding
on BBH-alg
A Generalizable Approach to Learning Optimizers
A core issue with learning to optimize neural networks has been the lack of generalization to real world problems.
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
Ranked #1 on
Zero-Shot Learning
on HellaSwag
