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Explore the intricacies of tensionless string theory, focusing on partition functions, backgrounds, and conical defects in this advanced seminar on high energy physics.
Explore exact WKB methods and their applications in supersymmetric field theory, focusing on key concepts, challenges, and recent developments in this advanced physics topic.
Explore latent state recovery in reinforcement learning, covering core problems, alternative approaches, and state abstraction. Gain insights from John Langford's expertise in theoretical machine learning.
Explore statistical learning theory's role in modern machine learning, covering PAC-Bayes framework, risk measures, and deep network training experiments with John Shawe-Taylor.
Explore nonlinear independent component analysis, its applications in AI and unsupervised learning, and recent advancements in deep latent variable models with Aapo Hyvärinen.
Explore implicit generative models' critic function, divergence measures, and energy-based models with Arthur Gretton. Gain insights into topological properties, neural net divergences, and multimodality in machine learning.
Explore efficient robot skill learning through grounded simulation, imitation learning, and off-policy reinforcement learning. Discover innovative approaches for transferring skills from simulation to real-world robots.
Explore quantum many-body scars as group-invariant Hilbert space sectors, examining their properties, energy gaps, and implications for high-temperature quantum systems and long-range correlations.
Explore semantic variables, learning theory, and cognitive processes with Yoshua Bengio. Delve into attention mechanisms, causality, and modular recurrent networks for advanced machine learning insights.
Explore non-perturbative aspects of JT gravity and supergravity through minimal strings, covering topics like double scaling limit, Dyson gas, and complex matrix models.
Explore advanced symplectic geometry concepts, focusing on distinguishing monotone Lagrangians through holomorphic annuli, with insights on geometric mutation and moduli spaces.
Explore private distributed learning techniques, including instance-hiding schemes, cryptographic methods, and data augmentation, with insights on privacy laws and practical applications.
Explore challenges in model-based reinforcement learning, including modeling errors and efficient planning, and discover strategies to overcome them in complex environments.
Explore Frobenius conjugacy classes in abelian varieties, focusing on independence of â„“, Mumford-Tate groups, and connections to Shimura varieties and CM liftings.
Explore Floer cohomology, arc spaces, and symplectic geometry with Mark McLean. Delve into contact structures, jet spaces, and mapping moduli spaces in this advanced mathematical lecture.
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