Search Results for author:
Found 10 papers, 4 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)
Conditional Text Generation
Multiple Choice Question Answering (MCQA)
Distributed Extra-gradient with Optimal Complexity and Communication Guarantees
We consider monotone variational inequality (VI) problems in multi-GPU settings where multiple processors/workers/clients have access to local stochastic dual vectors.
DiGress: Discrete Denoising diffusion for graph generation
This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes.
Proximal Point Imitation Learning
Thanks to PPM, we avoid nested policy evaluation and cost updates for online IL appearing in the prior literature.
Filling gaps in trustworthy development of AI
The range of application of artificial intelligence (AI) is vast, as is the potential for harm.
On the benefits of deep RL in accelerated MRI sampling
Deep learning approaches have shown great promise in accelerating magnetic resonance imaging (MRI), by reconstructing high quality images from highly undersampled data.
GG-GAN: A Geometric Graph Generative Adversarial Network
We study the fundamental problem of graph generation.
Uncertainty-Driven Adaptive Sampling via GANs
We propose an adaptive sampling method for the linear model, driven by the uncertainty estimation with a generative adversarial network (GAN) model.
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development.
Computers and Society
Closed loop deep Bayesian inversion: Uncertainty driven acquisition for fast MRI
This work proposes a closed-loop, uncertainty-driven adaptive sampling frame- work (CLUDAS) for accelerating magnetic resonance imaging (MRI) via deep Bayesian inversion.
