Evaluating Large-Scale Learning Systems
Explore challenges and solutions in evaluating large-scale machine learning systems, including federated networks and large language models. Gain insights into reliable assessment methods.
HammingMesh: A Network Topology for Large-Scale Deep Learning
Explore a novel network topology for large-scale deep learning systems, enhancing data movement efficiency and job scheduling flexibility for future AI training infrastructure.
AI-Driven Performance Metaprogramming - Embedding Spaces for Program Analysis and Optimization
Explore AI-driven performance metaprogramming using embedding spaces for programs. Learn to assess, analyze, and improve program performance through advanced AI methods.
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.
Structure Learning Using Benchpress - UAI 2023 Tutorial
Explore Benchpress, a scalable workflow for structure learning in probabilistic graphical models. Learn to run, develop, and benchmark algorithms for causal discovery and data-generating mechanisms.
Improving Pareto Front Learning via Multi-Head HyperNetwork
Explore multi-objective optimization and Pareto front learning through a novel Multi-head HyperNetwork architecture, improving solution quality for machine learning applications.
Toward Building a Cost-Effective and Robust Machine Learning System
Explore cost-effective ML system development through active learning, continual learning, and transfer learning. Learn efficient model selection techniques for optimal source model transfer to target tasks.
Backdoor Attacks and Defenses in AI Systems - Seminar Series
Explore backdoor attacks in AI systems, their impact on deep learning, and defense strategies against this emerging security threat.
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.
Faster Saddle-Point Optimization for Solving Large-Scale Markov Decision Processes
Explore advanced techniques for optimizing large-scale Markov decision processes using saddle-point methods. Gain insights into theoretical reinforcement learning and linear programming formulations.
Image Gradients for Understanding and Improving Neural Networks
Explore image gradients in neural networks to synthesize adversarial examples, visualize neurons, enhance robustness, interpret decisions, and improve image generators.
Adversarially Robust Stochastic Multi-Armed Bandits
Explore adversarially robust stochastic multi-armed bandits for optimal experimental design. Learn about algorithms balancing fast and robust learning in real-world applications like digital advertising.
Mehler's Formula, Branching Processes, and Compositional Kernels of Deep Neural Networks
Explore deep neural networks through compositional kernels and branching processes. Gain insights into activation functions, network limits, and memorization capacity. Learn about eigenvalues and a new random features algorithm.
Quantum Computing in Computer Vision: A Case Study in Robust Geometric Optimisation
Explore quantum computing's impact on computer vision, focusing on robust geometric optimization. Gain insights into NISQ experimentation and the potential of quantum algorithms in vision research.