Particle Methods for Optimization Over Measures - Lecture 3
Explore particle methods for measure optimization in this advanced lecture, covering theoretical foundations and practical applications in data science and machine learning.
On the Limit Theory of Mean Field Optimal Stopping with Common Noise
Explore mean field optimal stopping with common noise, focusing on limit theory. Gain insights into mathematical finance concepts presented at the Eastern Conference.
Cell Shape Variability and Its Implications in Epithelial Monolayers
Explore cell shape variability in epithelial monolayers, its universal distribution, origins, and implications for tissue dynamics and biological functions.
From Centrality Measures to Nanowire Networks - Disordered Systems Methods to the Rescue
Explore advanced network analysis techniques, from centrality measures to nanowire networks, using disordered systems methods to understand complex network dynamics and neuromorphic computing applications.
Landau Mean Field Theory and Dimensional Analysis - Lecture 2
Delve into advanced concepts of phase transitions through Landau mean field theory and dimensional analysis, exploring fundamental principles of statistical mechanics and critical phenomena.
The Mean-Field Limit of Non-Exchangeable Integrate and Fire Systems
Explore mean-field limits in non-exchangeable integrate and fire systems with Pierre-Emmanuel Jabin. Delve into advanced mathematical concepts and their applications in this specialized conference talk.
Cost-Efficient Neural Dynamics: Reconciling Multilevel Spontaneous and Evoked Activity in E-I Balanced Neural Networks at Criticality
Explore cost-efficient neural dynamics in E-I balanced networks, examining irregular spiking, oscillations, and critical avalanches. Uncover how these properties achieve minimal energy cost and maximal information capacity.
Mean Field Limit in Neural Network Learning
Explore the mean field limit in neural network learning, examining optimization dynamics, convergence to global optima, and data representation in autoencoders. Gain insights from physics analogies.
Probabilistic Graphical Models and Mean Field Inference - Lecture 20
Dive into advanced data science concepts including local version inference, alternating updates, and mean field version updates for practical applications in graphic models and word distribution.
Mean Field Results in Fluid Mechanics - Lecture 2
Explore advanced mean field techniques in fluid mechanics, focusing on stochastic and deterministic analysis for irregular models in mathematics.
Mean Field Results in Fluid Mechanics - Lecture 3
Explore advanced mean field techniques in fluid mechanics, focusing on stochastic and deterministic analysis for irregular models in mathematics.
A First-Principles Approach to Understanding Deep Learning
Delve into the fundamental principles of deep learning through statistical physics, exploring connections between neural networks, Gaussian processes, and scaling laws with insights from a DeepMind researcher.