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Meta AI Releases Meta’s Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

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Researchers from Meta Fundamental AI Research (FAIR) have introduced the Open Materials 2024 (OMat24) dataset, which contains over 110 million DFT calculations, making it one of the largest publicly available datasets in this domain. Don’t Forget to join our 50k+ ML SubReddit. If you like our work, you will love our newsletter.

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Katanemo Open Sources Arch-Function: A Set of Large Language Models (LLMs) Promising Ultra-Fast Speeds at Function-Calling Tasks for Agentic Workflows

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Meet Katanemo Arch-Function, a collection of state-of-the-art (SOTA) LLMs designed for function calling tasks – that meet/beat frontier LLM performance, but… pic.twitter.com/IajF8w3syz — Salman Paracha (Building Intelligent Infra) (@salman_paracha) October 15, 2024 Check out the Hugging Face Model Card.

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Differentiable Rendering of Robots (Dr. Robot): A Robot Self-Model Differentiable from Its Visual Appearance to Its Control Parameters

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Don’t Forget to join our 50k+ ML SubReddit. Upcoming Live Webinar- Oct 29, 2024] The Best Platform for Serving Fine-Tuned Models: Predibase Inference Engine (Promoted) The post Differentiable Rendering of Robots (Dr. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.

Robotics 108
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Graph-Constrained Reasoning (GCR): A Novel AI Framework that Bridges Structured Knowledge in Knowledge Graphs with Unstructured Reasoning in LLMs

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Don’t Forget to join our 50k+ ML SubReddit. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup. If you like our work, you will love our newsletter.

LLM 109
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MEGA-Bench: A Comprehensive AI Benchmark that Scales Multimodal Evaluation to Over 500 Real-World Tasks at a Manageable Inference Cost

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Don’t Forget to join our 50k+ ML SubReddit. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup. If you like our work, you will love our newsletter.

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JAMUN: A Walk-Jump Sampling Model for Generating Ensembles of Molecular Conformations

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By utilizing a SE(3)-equivariant denoising network, JAMUN can sample the Boltzmann distribution of arbitrary proteins at a speed significantly higher than traditional MD methods or current ML-based approaches. Don’t Forget to join our 50k+ ML SubReddit. If you like our work, you will love our newsletter.

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Rethinking Direct Alignment: Balancing Likelihood and Diversity for Better Model Performance

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Don’t Forget to join our 50k+ ML SubReddit. Upcoming Live Webinar- Oct 29, 2024] The Best Platform for Serving Fine-Tuned Models: Predibase Inference Engine (Promoted) The post Rethinking Direct Alignment: Balancing Likelihood and Diversity for Better Model Performance appeared first on MarkTechPost.