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#Manifold Learning

Showing: 13 courses
Applied Algebraic Topology Network

Demystifying Latschev's Theorem for Manifold Reconstruction

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Explores quantitative approaches to Latschev's theorem for topological manifold reconstruction, advancing finite sample analysis in data science and manifold learning applications.
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1 Lesons
49 minutes
On-Demand
Free-Video
Data Science Courses

Deep Learning - Regularization Techniques - Lecture 4

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Dive into advanced regularization techniques for deep learning, covering weight decay, early stopping, and noise injection methods to enhance model performance and prevent overfitting.
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1 Lesons
1 hour 18 minutes
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Free-Video
Applied Algebraic Topology Network

Reconstructing Manifolds by Weighted L_1-Norm Minimization

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Innovative approach to shape reconstruction using weighted l_1-norm minimization, transforming triangulation into a convex optimization problem for smooth orientable manifolds in high-dimensional spaces.
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1 Lesons
51 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Quantifying the Topology of Coma

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Explore a novel graph distance measure based on non-backtracking cycles, its relation to network features, and its application in distinguishing brain networks of healthy and comatose patients.
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1 Lesons
48 minutes
On-Demand
Free-Video
APS Physics

From Reinforcement Learning to Spin Glasses - The Many Surprises in Quantum State Preparation

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Explore quantum state preparation, reinforcement learning, and spin glasses, uncovering surprising connections and insights in this cutting-edge physics research presentation.
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11 Lesons
29 minutes
On-Demand
Free-Video
Joint Mathematics Meetings

Mathematical Frameworks for Signal and Image Analysis - Diffusion Methods in Manifold and Fibre Bundle Learning

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Explore advanced mathematical techniques for signal and image analysis, focusing on diffusion methods in manifold and fiber bundle learning with renowned mathematician Ingrid Daubechies.
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15 Lesons
53 minutes
On-Demand
Free-Video
HUJI Machine Learning Club

Manifold Denoising with Application to Electron Microscopy

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Discover advanced manifold denoising techniques for electron microscopy image processing, focusing on Fourier-like basis construction and filtering methods to enhance two-dimensional image collections through mathematical analysis.
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1 Lesons
56 minutes
On-Demand
Free-Video
Inside Livermore Lab

Leveraging Latent Representations for Predictive Physics-Based Modeling

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Explore advanced techniques for leveraging latent representations in predictive physics-based modeling, focusing on surrogate models, inverse problems, and transfer learning for complex PDEs.
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1 Lesons
56 minutes
On-Demand
Free-Video
Institut des Hautes Etudes Scientifiques (IHES)

Geometry, Topology and Discrete Symmetries Revealed by Deep Neural Networks

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Explore deep neural networks' ability to reveal geometry, topology, and discrete symmetries in manifolds. Learn about universal approximators and their applications in inverse problems and group actions.
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1 Lesons
36 minutes
On-Demand
Free-Video
IEEE Signal Processing Society

Low Distortion Embeddings With Bottom-up Manifold Learning

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Explore bottom-up manifold learning techniques for creating low distortion embeddings, enhancing data visualization and dimensionality reduction in signal processing applications.
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1 Lesons
57 minutes
On-Demand
Free-Video
Simons Institute

Understanding Generalization from Pre-training Loss to Downstream Tasks

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Explore the mystery of pre-trained models' effectiveness, examining contrastive loss, embedding spaces, and the roles of inductive bias and optimizers in generalization to downstream tasks.
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1 Lesons
1 hour 18 minutes
On-Demand
Free-Video
Inside Livermore Lab

Networks that Adapt to Intrinsic Dimensionality Beyond the Domain

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Explore neural networks' ability to adapt to intrinsic dimensionality, focusing on ReLU networks approximating functions with dimensionality-reducing feature maps. Gain insights into manifold learning and data analysis.
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21 Lesons
1 hour
On-Demand
Free-Video
BIMSA

Learning Manifold-Structured Data Using Deep Neural Networks - Theory and Applications

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Explore deep neural networks' ability to learn geometric data structures using multi-chart latent spaces and Chart Auto-Encoders, with theoretical foundations and practical applications.
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1 Lesons
1 hour 6 minutes
On-Demand
Free-Video