Machine-learning models help discover a material for film capacitors with record-breaking performance

The Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) and several collaborating institutions have successfully demonstrated a machine-learning technique to accelerate the discovery of materials for film capacitors—crucial components in electrification and renewable energy technologies. The technique was used to screen a library of nearly 50,000 chemical structures to identify a compound with record-breaking …

Classifier-Free Guidance Is a Predictor-Corrector

This paper was accepted at the Mathematics of Modern Machine Learning (M3L) Workshop at NeurIPS 2024. We investigate the unreasonable effectiveness of classifier-free guidance (CFG). CFG is the dominant method of conditional sampling for text-to-image diffusion models, yet unlike other aspects of diffusion, it remains on shaky theoretical footing. In this paper, we disprove common …

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Amazon Bedrock Marketplace now includes NVIDIA models: Introducing NVIDIA Nemotron-4 NIM microservices

This post is co-written with Abhishek Sawarkar, Eliuth Triana, Jiahong Liu and Kshitiz Gupta from NVIDIA.  At AWS re:Invent 2024, we are excited to introduce Amazon Bedrock Marketplace. This a revolutionary new capability within Amazon Bedrock that serves as a centralized hub for discovering, testing, and implementing foundation models (FMs). It provides developers and organizations …

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Build agentic RAG on Google Cloud databases with LlamaIndex

AI agents are revolutionizing the landscape of gen AI application development. Retrieval augmented generation (RAG) has significantly enhanced the capabilities of large language models (LLMs), enabling them to access and leverage external data sources such as databases. This empowers LLMs to generate more informed and contextually relevant responses. Agentic RAG represents a significant leap forward, …