Machine Learning and Computational Imaging - IPAM at UCLA
Explore AI's role in computational imaging, focusing on deep learning for inverse problems in image recovery and restoration, from denoising to nano-scale imaging.
Economic Fairness - IPAM at UCLA
Explore economic fairness concepts with Sendhil Mullainathan in this UCLA summer school lecture, covering key principles and their applications in algorithmic decision-making.
Cynthia Dwork - Group Fairness and Individual Fairness - IPAM at UCLA
Explore group and individual fairness in algorithmic decision-making, examining their definitions, advantages, and limitations in the context of ethical AI and machine learning applications.
Efficient Tensor Representation for Deep Learning with TensorLy and PyTorch
Explore tensor methods for deep learning, improving performance, speed, and robustness. Learn practical implementation using TensorLy-Torch for video and image classification tasks.
Tensor-Train Decomposition and Its Applications in Machine Learning
Explore tensor-train decomposition and its applications in machine learning, including neural network compression, high-dimensional distribution approximation, and deep neural network analysis.
On Sparse Linear Programming and Simple Neural Networks
Explores connections between sparse linear programming and neural networks, focusing on overparametrized learning and sparse inference from computational perspectives.
A Mathematical Perspective on Machine Learning
Explore deep learning's impact on AI and science, its theoretical foundations, and its potential for solving high-dimensional problems in scientific computing and beyond.
David Silver: Simulation-Based Search
Explore simulation-based search algorithms with DeepMind's David Silver, covering Monte Carlo tree search and its applications in AI and game-playing systems.
LaMini-LM - Mini Models Maxi Data
Explore LaMini-LM: mini language models trained on diverse, large-scale instruction data. Learn about dataset creation, model training, and practical applications through paper analysis and code demonstrations.
Deep Learning and Shape Modelling for Medical Image Reconstruction, Segmentation and Analysis
Explores deep learning techniques for MR image processing, incorporating anatomical shape information as prior knowledge to enhance reconstruction, super-resolution, and segmentation in medical imaging applications.
Counting Divisorial Contractions with Centre a $cA_n$-Singularity
Explore 3D divisorial contractions with $cA_n$-singularity centers, simplifying existing classifications and examining high-discrepancy contractions to fixed varieties.
Logarithmic Resolution of Singularities
Explore a novel approach to resolving singularities in algebraic geometry using logarithmic techniques, offering faster and more functorial results with applications to log schemes and morphisms.
GPPV Invariants and the Spectrum of Singularities
Explore complex surface singularities, their 3-manifold links, and GPPV invariants. Discover new connections between modular forms and singularity spectra for Brieskorn homology spheres and ADE singularity links.
Steenbrink Spectra in Singularity Categories
Explore Steenbrink spectra's role in singularity categories, examining how spectral numbers manifest in triangulated singularity categories associated with singularities.
Composing Graphical Models with Neural Networks for Structured Representations and Fast Inference
Explore structured representations and fast inference by combining graphical models with neural networks, enhancing machine learning capabilities.
Geometry, Optimization and Generalization in Multilayer Networks
Explore the interplay of geometry, optimization, and generalization in multilayer networks, focusing on representation learning and its applications in deep learning architectures.
Tutorial on Deep Learning IV
Explore advanced deep learning concepts with Carnegie Mellon expert Ruslan Salakhutdinov, covering cutting-edge techniques and applications in machine learning.
Tutorial on Deep Learning I
Explore deep learning foundations, from neural networks to advanced techniques, with insights on feature representation, optimization, and practical applications in computer vision and audio processing.
Statistical Learning Theory and Neural Networks II
Explores advanced concepts in statistical learning theory and neural networks, focusing on optimization perspectives, gradient descent analysis, and the neural tangent kernel regime.
Statistical Learning Theory and Neural Networks
Explore statistical learning theory for deep neural networks, covering uniform laws, Rademacher complexity, and optimization perspectives including gradient descent and neural tangent kernel regime.
Adversarial Examples in Deep Learning
Exploring adversarial examples in deep learning: design, defense strategies, and the relationship between model size and robustness against attacks. Insights from experts on implications and challenges.