Principal Components along Quiver Representations
Explore principal components in quiver representations, a generalization of multi-indexed data tensors, and their applications in optimization and data analysis.
Beyond Lazy Training for Over-parameterized Tensor Decomposition
Explore tensor decomposition in over-parameterized settings, analyzing gradient descent variants and their performance beyond lazy training regimes for improved optimization in neural networks.
Strictly-Correlated Electron Functional and Multimarginal Optimal Transport
Explore convex optimization methods for multi-marginal transport problems in density functional theory, including relaxation techniques and rounding schemes for energy bounds.
Hyper-Optimized Tensor Network Contraction - Simplifications, Applications and Approximations
Explore tensor network contraction optimization through hyper-graph partitioning, simplifications, and approximations, with applications in quantum circuit simulation and beyond.
Low Rank Tensor Completion
Explore low-rank tensor completion methods for recovering tensors from partial observations, including matrix composition, multilinear ranks, and approaches for initialization and recovery.
Multigrid in Hierarchical Low Rank Tensor Formats
Explore hierarchical low rank tensor formats and multigrid methods for solving high-dimensional systems, with focus on convergence analysis and practical applications in scientific computing.
Tensor Decompositions and Their Applications
Explore advanced tensor decomposition techniques and their applications in latent variable models, phylogenetics, mixture models, and orbit retrieval with MIT's Ankur Moitra.
Tensor Decompositions and Their Applications
Explore tensor decompositions and their applications in latent variable models, focusing on Jennrich's algorithm and its use in various fields like phylogenetics and mixture models.
Learning Diagonal Gaussian Mixture Models and Incomplete Tensor Decompositions
Explore parameter learning in diagonal Gaussian mixture models through incomplete symmetric tensor decompositions, with applications in tensor approximation and stability analysis.
HypoSVI- Earthquake Hypocentre Inversion With Stein Variational Inference and Physics Informed Neural Networks
Explore physics-informed deep learning for environmental sciences, covering PINNs for Eikonal equations, probabilistic inversion for earthquake location, and neural operators for in-ice navigation.
From Bits to Atoms - Turning Information into Things and Things into Information
Explore the convergence of computer science and physical science, examining how information becomes tangible and objects transform into data, revolutionizing manufacturing and design.