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Explore tradeoffs between robustness and accuracy in machine learning, covering topics like spurious correlations, regularization, and model complexity with Percy Liang.
Explore modularity, attention, and credit assignment in computational models, focusing on complex generalization, independent mechanisms, and selective activation for improved learning and problem-solving.
Explore simpler, interpretable machine learning models and their advantages in criminal justice and beyond. Learn about the Rashomon set, decision trees, and strategies to combat overfitting.
Explore quantum entanglement and its computational applications in this advanced seminar, covering extremal surfaces, CFTs, and non-local effects in various dimensions and temperatures.
Explore the singular set in fully nonlinear obstacle problems, focusing on free boundaries, monotonicity, and parametric inequalities with expert Ovidiu Savin.
Explore classical and modern theories of vector field flows, covering key concepts, examples, and recent developments in analysis and differential equations.
Explore the Kudla-Rapoport conjecture, Hurwitz class numbers, and their connections to Eisenstein series and L-functions in this advanced number theory seminar by Chao Li.
Explore meta-learning challenges and solutions with Ke Li, covering optimization, model selection, and program induction in this theoretical machine learning seminar.
Explore overparameterization in machine learning, its benefits, potential harm, and the double descent phenomenon. Gain insights into interpolation regimes and matrix intuition.
Explore advanced matrix models, focusing on positivity constraints and loop equations. Learn techniques for solving complex mathematical problems in high-energy physics.
Explore covariant phase space with boundaries in field theory, covering key concepts like configuration space, prephase space, and Hamiltonian construction.
Explore energy-based models in machine learning, covering applications, scaling techniques, and their impact on classification, calibration, and robustness in AI systems.
Explore high dimensional expanders with Irit Dinur, covering random walks, spectral expansion, and hypergraphs. Gain insights into this advanced topic in discrete mathematics and computer science.
Explore mathematical approaches to modeling brain dynamics, from local neural populations to complex cognitive functions, with insights on synchronization, memory, and visual processing.
Explore the intersection of AI and social sciences through expert talks on mate selection decision-making and human behavior as AI's next frontier, followed by a moderated discussion and audience Q&A.
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