
Institute for Pure & Applied Mathematics (IPAM)
The Institute for Pure and Applied Mathematics (IPAM) is a National Science Foundation Math Institute at the University of California, Los Angeles (UCLA) that fosters the interaction of mathematics with a broad range of science and technology, builds new interdisciplinary research communities, promotes mathematical innovation, and engages and transforms the world through mathematics. IPAM fulfills its mission through workshops and programs that connect mathematics and other disciplines or multiple areas of mathematics. These activities bring in thousands of visitors annually from academia, government, and industry. The NSF has encouraged IPAM to make the scientific presentations of our speakers available to a wider audience. While this YouTube channel contains many of our most popular videos, our full video archive is available at www.ipam.ucla.edu/videos.
Episodes


Maxwell Hutchinson: "Boltzmann Trees: a physically inspired randomization for robust modeling of..."

Maria Schuld: "Innovating machine learning with near-term quantum computing"

Carlo Baldassi: "On the existence of wide flat minima in neural network landscapes: analytic and..."

Daniel Roberts: "Deep learning as a toy model of the 1/N-expansion and renormalization"

Riccardo Zecchina: "Evidence for local entropy optimization in machine learning, physics and neu..."

Haim Sompolinsky: "Statistical Mechanics of Deep Manifolds: Mean Field Geometry in High Dimension"

Masayuki Ohzeki: "Quantum annealing and machine learning - new directions of quantum"

Chiara Cammarota: "High-dimensional cost landscape and gradient descent in Tensor PCA and its ge..."

Gérard Ben Arous: "Which is worse: a weak signal lost in entropy or trapping by topological comp..."

Yann LeCun: "Energy-Based Self-Supervised Learning"

Michele Ceriotti: "Machine learning for atomic and molecular simulations"

Risi Kondor: "Fourier space neural networks"

Eric Vanden-Eijnden: "Trainability and accuracy of artificial neural networks"

Rémi Monasson: "Capacity-resolution trade-off in the optimal learning of multiple low-dimensiona..."

Lenka Zdeborová: "Understanding machine learning via exactly solvable statistical physics models"

Lukáš Pospíšil: "Towards a low-cost scalable data-driven modeling of coupled systems and non-sta..."

Joan Bruna: "Geometric Insights for Nonlinear TD Convergence"

Stefan Klus: "Data-driven transfer operator approximation, model reduction, and system identific..."

Bing Brunton: "Fields and flows in neural dynamics"
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