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Found 6 papers, 2 papers with code
MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
Large language models (LLMs) can potentially democratize access to medical knowledge.
Ranked #1 on
Multiple Choice Question Answering (MCQA)
on MedMCQA
(Dev Set (Acc-%) metric)
Inference from Real-World Sparse Measurements
Notably, our model reduces the root mean square error (RMSE) for wind nowcasting from 9. 24 to 7. 98 and for heat diffusion tasks from 0. 126 to 0. 084.
Efficiently Training Low-Curvature Neural Networks
To achieve this, we minimize a data-independent upper bound on the curvature of a neural network, which decomposes overall curvature in terms of curvatures and slopes of its constituent layers.
The Theoretical Expressiveness of Maxpooling
Over the decade since deep neural networks became state of the art image classifiers there has been a tendency towards less use of max pooling: the function that takes the largest of nearby pixels in an image.
Challenges for Using Impact Regularizers to Avoid Negative Side Effects
Finally, we explore promising directions to overcome the unsolved challenges in preventing negative side effects with impact regularizers.
Computing Preimages of Deep Neural Networks with Applications to Safety
To apply an algorithm in a sensitive domain it is important to understand the set of input values that result in specific decisions.
