Easily Scale to Production-Ready Generative AI with NVIDIA and Anyscale
Scale generative AI to production with NVIDIA and Anyscale's partnership. Learn how their combined technologies optimize performance, lower costs, and enable secure deployment of AI services at scale.
Enabling End-to-End LLMOps on Michelangelo with Ray
Explore Uber's extension of Michelangelo for end-to-end LLMOps, leveraging Ray for scalable development with hundreds of A100 GPUs and integrating open-source techniques for efficient custom LLM creation.
Ray Train: A Production-Ready Library for Distributed Deep Learning
Explore Ray Train's architecture for efficient, cost-effective distributed deep learning. Learn about resource scheduling, API simplicity, and exclusive features for LLM training, including Distributed Checkpointing.
Scalable and Cost-Efficient AI Workloads with AWS and Anyscale
Learn to scale and productionize GenAI and LLM workloads cost-effectively using AWS compute instances and Anyscale's capabilities for ambitious AI projects.
Optimizing LLM Inference with AWS Trainium, Ray, vLLM, and Anyscale
Explore cost-effective LLM inference scaling using AWS accelerators, Ray, vLLM, and Anyscale. Learn to build a complete stack on EKS and leverage advanced cluster management for enterprise-grade GenAI workloads.
New Frontiers for AI-assisted Design and Discovery in Molecular Generation
Explore AI-assisted design for generating molecules with desired properties. Learn about overcoming challenges in combinatorial search, modeling interactions, and segregating representations for improved drug and material discovery.
New Frontiers for AI-assisted Design and Discovery in Molecular Generation
Explore AI-assisted design and discovery for generating molecules with desirable properties. Learn about overcoming challenges in combinatorial space, modeling interactions, and segregating representations for improved drug and material development.
Hierarchical Imitation Learning with Vector Quantized Models
Learn hierarchical imitation learning techniques using vector quantized models for complex decision-making tasks, exploring subgoal identification and planning strategies to outperform expert demonstrations.
Elucidating the Design Space of Diffusion-Based Generative Models
Explore advancements in diffusion-based generative models, including improved sampling, training, and score network preconditioning. Learn about state-of-the-art results and modular design changes for enhanced efficiency and quality.
Diffusion Models for Inverse Problems
Explore advanced techniques for solving inverse problems using diffusion models, focusing on posterior sampling and manifold constraints.
Planning with Diffusion for Flexible Behavior Synthesis
Learn flexible behavior synthesis using diffusion models for planning, exploring compositional trajectories, goal-directed planning, and offline reinforcement learning techniques.
Hierarchically Branched Diffusion Models for Efficient and Interpretable Multi-Class Conditional Generation
Explore efficient and interpretable multi-class conditional generation using hierarchically branched diffusion models. Gain insights into advanced generative techniques.
Diffusion Models as Plug-and-Play Priors
Explore diffusion models as versatile priors for various tasks, enhancing image processing and generation capabilities.
Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces
Explore generative diffusion models in discrete-state spaces through the innovative Blackout Diffusion approach, presented by researcher Yen Ting Lin.
Spontaneous Symmetry Breaking in Generative Diffusion Models
Explore symmetry breaking in generative diffusion models, uncovering insights into their behavior and potential applications in AI and machine learning.
Bridging Associative Memory and Probabilistic Modeling
Explore the connection between associative memory and probabilistic modeling in this insightful presentation by Rylan Schaeffer.
Critical Windows: Non-Asymptotic Theory for Feature Emergence in Diffusion Models
Explore non-asymptotic theory for feature emergence in diffusion models, focusing on critical windows and their implications for generative AI and machine learning.
Generative AI and Large Language Models: Recent Advancements and Challenges
Explore generative AI advancements, challenges, and VinAI's contributions with Dr. Hung Bui. Gain insights into AI-generated content's impact and the pressing issues in this rapidly evolving field.
Explore the Sustainable Future of AI
Discover strategies for sustainable AI development, focusing on energy-efficient hardware, optimized algorithms, and renewable energy in data centers.
Harnessing Proprietary Data in the Era of Generative AI - Insights from AWS
Explore AWS's mission to democratize machine learning, offering customization capabilities for proprietary data. Learn about security, data governance, and scalable GenAI development for startups building innovative AI solutions.
Introduction to Language Models - LLMs, Prompt Engineering, and Architectures
Explore language models, covering generative AI, LLMs, encoders, prompt engineering, and transformer architecture. Gain insights into embeddings, attention mechanisms, and model limitations.