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Found 6 papers, 2 papers with code
Towards Generalist Prompting for Large Language Models by Mental Models
MeMo distills the cores of various prompting methods into individual mental models and allows LLMs to autonomously select the most suitable mental models for the problem, achieving or being near to the state-of-the-art results on diverse tasks such as STEM, logical reasoning, and commonsense reasoning in zero-shot settings.
Control Risk for Potential Misuse of Artificial Intelligence in Science
In this study, we aim to raise awareness of the dangers of AI misuse in science, and call for responsible AI development and use in this domain.
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning
In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems.
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
To reduce the compute time overhead of private learning, we show that \emph{per-layer clipping}, where the gradient of each neural network layer is clipped separately, allows clipping to be performed in conjunction with backpropagation in differentially private optimization.
Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets
This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation.
An Empirical Study of Graphormer on Large-Scale Molecular Modeling Datasets
This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation.
