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Computer Science
Artificial Intelligence
OpenAI
Divide and Conquer, Sorting and Searching, and Randomized Algorithms
Introduction to Graphic Illustration
The Science of Gastronomy
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Explore the biophysics of neural computation, focusing on dendritic spikes and their role in information processing within the brain's complex structures.
Explore large-scale datasets and modeling tools for brain research, covering transcriptomics, connectome mapping, visual coding, and human neuron studies to advance our understanding of brain function.
Explore cutting-edge techniques in connectomics, from crowdsourcing to machine learning, for mapping neural connections and understanding brain function at scale.
Explore key developments in theoretical computer science through an engaging conversation between Russell Impagliazzo and Dick Karp, covering topics from NP-completeness to computational biology.
Exploration of self-concordant barriers over L_p balls, discussing Michael Cohen's contributions and proof ideas in the context of high-dimensional optimization and convex geometry.
Explore low-rank matrix recovery, from matrix completion to recent trends, covering theoretical foundations and applications in signal processing and machine learning.
Explore algorithmic decision-making in pre-trial settings, examining bias identification, fairness concerns, and the trade-offs between accuracy and equity in automated systems.
Explore nonnegative polynomials and nonconvex optimization, focusing on shape-constrained regression and DC programming. Learn powerful relaxation techniques and their applications in machine learning.
Explore symmetric matrix signings, their spectral aspects, and applications in graph theory, polynomial identity testing, and algorithmic problems.
Explore advanced techniques for constructing extended formulations in combinatorial optimization, focusing on polytopes, matchings, cycles, and spanning trees in various graph types.
Explore computational complexity, linear programming, and extended formulations in this lecture on lower bounds for linear program sizes, featuring geometric views and advanced topics.
Explore advanced algorithms and iterative methods for computing stationary distributions, with applications in optimization and graph theory.
Explore linear constraint optimization using random permutation techniques, focusing on ADMM variants, spectral analysis, and convergence rates for large-scale problems.
Explore advanced block coordinate descent techniques for large-scale optimization, including Gauss-Southwell rules, Newton steps, and bound constraints, with applications in machine learning.
Explore advanced interior point methods for optimization, covering key ideas, path following analysis, and specialized algorithms like Renegar's and regularized John ellipse barrier.
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