University Laboratory Partnerships in the AI Era - Lecture 13
Explore university-laboratory collaborations in the AI era, examining their impact on research, innovation, and education in scientific computing.
Foundational Methods for Foundation Models in Scientific Machine Learning - Lecture 6
Explore foundational methods for scientific machine learning models, addressing challenges in integrating data-driven ML with domain-driven scientific computing methodologies.
Programming Novel AI Accelerators for Scientific Computing - Part 2 - Tutorial 5
Explore advanced techniques for programming AI accelerators in scientific computing, focusing on optimization and performance enhancement.
LLMs on Polaris - Tutorial 5
Explore large language model deployment on Polaris supercomputer, focusing on efficient scaling and performance optimization techniques.
SambaNova DataScale SN30 and Platform Architecture - Tutorial 5
Explore SambaNova DataScale SN30 architecture and platform features for advanced AI computing solutions.
Programming Novel AI Accelerators for Scientific Computing - Part 1 - Tutorial 5
Explore programming techniques for AI accelerators in scientific computing, focusing on novel approaches and optimization strategies.
Federated Learning Tutorial: Concepts, Applications, Challenges, and Framework
Explore federated learning concepts, applications, challenges, and frameworks. Gain insights into collaborative machine learning while preserving data privacy and security.
Cerebras Systems - Tutorial 5
Explore cutting-edge AI hardware with Cerebras Systems, focusing on their innovative wafer-scale engine for accelerated deep learning and high-performance computing.
Groq Tutorial - Part 5
Explore Groq's innovative AI hardware and software solutions for accelerating machine learning workloads and enhancing computational efficiency.
GenSLM - Infrastructure and Downstream Utilization - Tutorial 3
Explore GenSLM infrastructure and downstream applications for advanced language modeling techniques in this comprehensive tutorial.
Foundation Models for Materials Discovery
Explore foundation models for materials discovery, focusing on their applications and potential in advancing scientific research and innovation.
Foundation Models for a Sustainable Planet
Explore how foundation models can address sustainability challenges and drive positive environmental impact through AI-powered solutions.
Robust Design Discovery and Exploration in Bayesian Optimization
Explore data-driven methods for robust design discovery and optimization, focusing on Bayesian techniques that efficiently handle uncertainties and adversarial scenarios in various scientific domains.
Federated Learning and Analytics at Google - Workshop Introduction
Explore federated learning and analytics at Google, focusing on privacy principles, data minimization, and innovative techniques for decentralized machine learning.
Heterogeneity-Aware Algorithms for Federated Optimization
Explore heterogeneity-aware algorithms for federated optimization, addressing data, communication, and computational challenges in distributed learning environments.
Federated Learning with Formal User-Level Differential Privacy Guarantees
Explore federated learning with user-level differential privacy guarantees. Discover scalable algorithms for industrial workloads in data privacy and machine learning.
Privacy-Aware Compression for Federated Learning
Explore privacy-aware compression techniques for federated learning, addressing challenges and implementing local differential privacy mechanisms to enhance data protection in collaborative AI.
Secure Self-supervised Learning: Challenges and Solutions
Explore secure self-supervised learning techniques, focusing on backdoor attacks, data auditing, and membership inference for pre-trained encoders in machine learning.
Mapping from Training Data to Predictions with Datamodels - Differential Privacy in Machine Learning
Explore datamodels in machine learning, from training data to predictions. Learn about model brittleness, data counterfactuals, and applications in ML pipelines.
Example Memorization in Learning: Batch and Streaming - Differential Privacy for ML
Explore example memorization in machine learning, covering batch and streaming scenarios, space requirements, and implications for natural models and future research directions.
Privacy-Preserving Machine Learning with Fully Homomorphic Encryption
Explore Fully Homomorphic Encryption for privacy-preserving machine learning, enabling zero-trust interactions and secure model deployment on untrusted servers.