Rational Ignorance - Optimal Learning from Complex Mechanistic Models
Explore optimal Bayesian learning from complex models, challenging traditional approaches and proposing a new method to maximize expected information while avoiding bias in multiparameter systems.
Geometric Representations of Far Future Predictions in Deep Neural Networks - Next-Token Training
Explore universal geometric representations of far-future beliefs in deep neural networks trained on next-token prediction, uncovering fractal and quantum-like structures embedded in network activations.
Parameter Estimation in Mathematical Oncology - Lecture
Dive into advanced techniques for parameter estimation in mathematical oncology, exploring key concepts and practical applications.
Predictive Inference and Material World in Cognitive Processes
Explore the interplay between predictive inference and the material world in cognitive processes, delving into complex systems and interdisciplinary physics perspectives.
Celeste.jl - Petascale Computing in Julia
Explore Celeste.jl, a Julia-based project for petascale astronomical image analysis using Bayesian inference, showcasing Julia's power in high-performance computing and data science on supercomputers.
Steps Towards More Human-Like Learning in Machines - Josh Tenenbaum
Explore how machines can achieve more human-like learning through probabilistic programming, intuitive psychology, and simulation engines. Discover insights on core knowledge and one-shot learning in AI.
Using Bayesian Inference to Detect Cyber Attacks
Explore Bayesian inference techniques for detecting cyber attacks, enhancing your ability to identify and respond to security threats in real-time.
Is Reality a Controlled Hallucination? - Exploring Consciousness and Perception
Explore consciousness as a controlled hallucination through innovative theories and experiments. Delve into perception, self-awareness, and the nature of reality with neuroscientist Anil Seth.
Iterative Stochastic Numerical Methods for Statistical Sampling
Explore stochastic algorithms for Bayesian inference, blending molecular dynamics with data analytics. Learn innovative approaches to merge physical laws and data science in engineering applications.
Physics-Assisted Multi-Robot Exploration
Explore cutting-edge techniques for multi-robot systems using physics-assisted approaches to enhance exploration and coordination in complex environments.
Statistics in Cyber-Security - Dr Nick Heard, Imperial College London
Explore statistical approaches for cyber-security, from micro-level models to full network graph representations, to identify illegitimate network activity using enterprise data sources.
Statistical Inference I
Explore statistical inference fundamentals, including models, estimation, minimax risk, and Bayesian approaches, with UC Berkeley's Will Fithian.
Geometric and Topological Data Analysis of Enzyme Kinetics
Explore geometric and topological data analysis techniques applied to enzyme kinetics, focusing on ERK molecular dynamics and its implications for cancer and developmental defects.
The Promises and Pitfalls of Stochastic Gradient Langevin Dynamics - Eric Moulines
Explore Stochastic Gradient Langevin Dynamics: its potential and challenges in optimization, data processing, and machine learning. Gain insights from expert Eric Moulines on this powerful algorithm.
Digital Twins Workshop: Foundations and Challenges - Day 1
Advancing digital twins research: Experts explore Bayesian inference, irregular posteriors, and geophysical applications in this foundational workshop for data-driven engineering.
Statistical Rethinking 2023 - Markov Chain Monte Carlo
Explore Markov Chain Monte Carlo methods, from basic concepts to advanced techniques like Hamiltonian Monte Carlo, with practical applications and diagnostics for statistical modeling.
Statistical Rethinking 2022 - Bayesian Inference
Explore Bayesian inference through practical examples like globe tossing. Learn updating, sampling, and computing distributions using grid approximation and posterior predictive methods.
Science Before Statistics - Causal Inference
Comprehensive workshop on causal inference, covering key concepts like causal design, graph analysis, and Bayesian inference. Equips researchers with tools to navigate complex causal relationships in data analysis.
Scaling Up Bayesian Inference for Big and Complex Data
Explore advanced Bayesian inference techniques for large-scale datasets, focusing on computational challenges and innovative solutions in machine learning and deep learning applications.
Perceptual Organization From a Bayesian Point of View
Explore Bayesian approach to perceptual organization, examining how the visual system groups images into distinct units through inverse inference and mixture estimation techniques.
Development of Intelligence - Josh Tenenbaum: Bayesian Inference
Explore Bayesian inference and cognitive neuroscience with Josh Tenenbaum, delving into AI challenges, scene understanding, and advanced mathematical concepts for intelligence development.