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#Energy-Based Models

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Stanford University

Deep Generative Models - Energy Based Models - Lecture 12

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Explore energy-based models in deep learning, their applications, and theoretical foundations. Gain insights into this powerful approach for modeling complex data distributions.
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1 Lesons
1 hour 23 minutes
On-Demand
Free-Video
Stanford University

Deep Generative Models - Energy Based Models - Lecture 14

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Explore energy-based models in deep learning, their applications, and theoretical foundations. Gain insights from Stanford's expert on advanced generative modeling techniques.
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1 Lesons
1 hour 25 minutes
On-Demand
Free-Video
Stanford University

Deep Generative Models - Energy Based Models - Lecture 11

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Explore energy-based models in deep learning, their applications, and theoretical foundations. Gain insights from Stanford's expert on advanced generative modeling techniques.
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1 Lesons
1 hour 25 minutes
On-Demand
Free-Video
Institute for Advanced Study

Your Brain on Energy-Based Models - Applying and Scaling EBMs to Problems

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Explore energy-based models in machine learning, covering applications, scaling techniques, and their impact on classification, calibration, and robustness in AI systems.
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10 Lesons
45 minutes
On-Demand
Free-Video
Institute for Advanced Study

On the Critic Function of Implicit Generative Models - Arthur Gretton

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Explore implicit generative models' critic function, divergence measures, and energy-based models with Arthur Gretton. Gain insights into topological properties, neural net divergences, and multimodality in machine learning.
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29 Lesons
1 hour 6 minutes
On-Demand
Free-Video
Institute for Advanced Study

Energy-Based Approaches to Representation Learning - Yann LeCun

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Explore energy-based approaches to representation learning with Yann LeCun, covering self-supervised learning, video prediction, and sparse modeling in high-dimensional spaces.
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19 Lesons
40 minutes
On-Demand
Free-Video
Institut des Hautes Etudes Scientifiques (IHES)

Sampling through Exploration Exploitation in Markov Chain Monte Carlo

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Explore an innovative Markov chain Monte Carlo algorithm combining global proposals and mobile moves for efficient, parallelizable sampling in various applications.
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1 Lesons
35 minutes
On-Demand
Free-Video
Simons Institute

Towards Understanding Generalization Properties of Score-Based Losses

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Explore score-based losses as alternatives to maximum likelihood in generative models, examining their statistical efficiency and connections to diffusion algorithms for improved model design.
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1 Lesons
1 hour 17 minutes
On-Demand
Free-Video
Yannic Kilcher

A Path Towards Autonomous Machine Intelligence - Paper Explained

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Explanation of Yann LeCun's paper on autonomous machine intelligence, combining self-supervised learning, energy-based models, and hierarchical predictive embedding for creating world models and planning.
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11 Lesons
1 hour
On-Demand
Free-Video
Yannic Kilcher

Author Interview - VOS- Learning What You Don't Know by Virtual Outlier Synthesis

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Interview exploring Virtual Outlier Synthesis for out-of-distribution detection in neural networks. Discusses motivations, methodology, and key findings of novel approach to improve model robustness using synthesized outlier data.
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11 Lesons
36 minutes
On-Demand
Free-Video
Yannic Kilcher

Self-Supervised Learning - The Dark Matter of Intelligence

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Explore self-supervised learning as a promising approach for AI to develop background knowledge and common sense, with insights from Yann LeCun and Ishan Misra of Facebook AI Research.
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9 Lesons
59 minutes
On-Demand
Free-Video
Yannic Kilcher

Concept Learning with Energy-Based Models

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Explore energy-based models for concept learning, focusing on their ability to generalize, reason abstractly, and solve problems creatively with limited data through inference-time optimization.
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1 Lesons
39 minutes
On-Demand
Free-Video
Alfredo Canziani

Structured Prediction with Energy Based Models

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Explore energy-based models for structured prediction, covering factor graphs, efficient inference, Graph Transformer Networks, loss functions, and variational inference in deep learning applications.
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8 Lesons
2 hours 8 minutes
On-Demand
Free-Video
Alfredo Canziani

PyTorch Activation and Loss Functions

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Explore activation and loss functions in PyTorch, including kink vs. smooth activations, margin-based losses, and designing effective loss functions for Energy-Based Models (EBMs).
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7 Lesons
1 hour 54 minutes
On-Demand
Free-Video
Alfredo Canziani

Group Sparsity, World Model, and Generative Adversarial Networks

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Explore advanced deep learning concepts: sparse auto-encoders, world models for autonomous control, and generative adversarial networks (GANs). Gain insights into cutting-edge AI techniques and their applications.
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7 Lesons
1 hour 58 minutes
On-Demand
Free-Video
Alfredo Canziani

Contrastive Methods and Regularised Latent Variable Models

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Explore contrastive methods and regularized latent variable models in energy-based models, covering self-supervised learning, autoencoders, sparse coding, and variational techniques for advanced machine learning applications.
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7 Lesons
1 hour 39 minutes
On-Demand
Free-Video
Alfredo Canziani

Energy Based Models and Self-Supervised Learning

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Explore energy-based models, latent variables, and self-supervised learning. Dive into EBM training, inference, and comparison with probabilistic models. Discover contrastive methods and applications like K-means and BERT.
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7 Lesons
1 hour 37 minutes
On-Demand
Free-Video
Simons Institute

Backpropagation and Deep Learning in the Brain

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Explore deep learning in the brain, backpropagation, and credit assignment algorithms. Discover neuroscience evidence and new models of neurons in this computational brain theory talk.
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23 Lesons
1 hour
On-Demand
Free-Video
Paul G. Allen School

Generalizing Outside the Training Distribution through Compositional Generation

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Explore compositional generative modeling for AI generalization beyond training data, enabling complex planning and decision-making in unseen tasks and environments.
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1 Lesons
59 minutes
On-Demand
Free-Video
Valence Labs

Protein Discovery with Discrete Walk-Jump Sampling

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Explore discrete generative modeling for protein discovery using Walk-Jump Sampling. Learn to train energy functions, sample with Langevin MCMC, and optimize for distributional conformity in antibody design.
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6 Lesons
55 minutes
On-Demand
Free-Video
Center for Language & Speech Processing(CLSP), JHU

Joint Energy-based Models for Speech

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Explore generative models, focusing on Energy-based Models and Joint Energy-based Models for speech, with potential applications in multiple tasks.
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1 Lesons
12 minutes
On-Demand
Free-Video
Center for Language & Speech Processing(CLSP), JHU

Learning Energy-Based Models of High-Dimensional Data - 2002

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Explore energy-based models for high-dimensional data analysis. Learn about deterministic networks, perceptual inference, and efficient learning techniques for large-scale models.
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16 Lesons
1 hour 5 minutes
On-Demand
Free-Video
USC Information Sciences Institute

On Robustness and Generative Modeling

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Explore adversarial training's impact on AI robustness and generative modeling. Gain insights into energy dynamics, robust overfitting, and the connection between robust classifiers and generative capabilities.
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1 Lesons
1 hour 4 minutes
On-Demand
Free-Video