Yannic Kilcher Courses

HyperTransformer - Model Generation for Supervised and Semi-Supervised Few-Shot Learning

Explore HyperTransformer, a novel approach to few-shot learning using transformers to generate CNN weights. Learn about its architecture, advantages, and potential applications in machine learning.

• YouTube
• 1 hour 18 minutes
• On-Demand
• Free Video

Language Models as Zero-Shot Planners - Extracting Actionable Knowledge for Embodied Agents

Exploring how large language models can translate high-level tasks into actionable steps for virtual environments, with techniques to improve executability and potential real-world applications.

• YouTube
• 1 hour 17 minutes
• On-Demand
• Free Video

Unsupervised Brain Models - How Does Deep Learning Inform Neuroscience?

Explore connections between deep learning and neuroscience, focusing on unsupervised brain models and their potential to explain visual processing streams and representation learning in the brain.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Video

Predicting the Rules Behind - Deep Symbolic Regression for Recurrent Sequences

Explore deep symbolic regression for predicting rules behind number sequences using transformers. Learn about data encoding, training processes, and applications beyond mathematics in this in-depth interview.

• YouTube
• 1 hour 11 minutes
• On-Demand
• Free Video

Dynamic Inference with Neural Interpreters - Interview with the Authors

Explore Neural Interpreters: a modular deep learning architecture for systematic generalization. Learn about its structure, function routing, and applications in image classification and abstract reasoning.

• YouTube
• 1 hour 23 minutes
• On-Demand
• Free Video

Noether Networks - Meta-Learning Useful Conserved Quantities

Explore Noether Networks: a novel approach to meta-learning conserved quantities in sequential prediction problems, inspired by Noether's theorem and aimed at discovering useful symmetries and inductive biases.

• YouTube
• 1 hour 9 minutes
• On-Demand
• Free Video

Resolution-Robust Large Mask Inpainting with Fourier Convolutions - With Author Interview

Explore LaMa, an advanced image inpainting system using Fourier convolutions for large mask removal. Learn about its architecture, loss function, and impressive results in reconstructing complex structures.

• YouTube
• 54 minutes
• On-Demand
• Free Video

NÜWA - Visual Synthesis Pre-training for Neural Visual World Creation

Explore NÜWA, a unified multimodal model for visual synthesis tasks. Learn about its 3D transformer framework, 3D Nearby Attention mechanism, and applications in text-to-image/video generation and manipulation.

• YouTube
• 53 minutes
• On-Demand
• Free Video

Sparse Is Enough in Scaling Transformers - ML Research Paper Explained

Detailed explanation of Scaling Transformers and Terraformer architecture, focusing on leveraging sparsity to improve efficiency and speed in large language models while maintaining accuracy.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Towards Extreme Multi-Task Scaling for Transfer Learning

Explores ExT5, an advanced NLP model pre-trained on 107 diverse tasks. Analyzes multi-task scaling benefits, task co-training effects, and demonstrates ExT5's superior performance across various NLP benchmarks.

• YouTube
• 41 minutes
• On-Demand
• Free Video

Parameter Prediction for Unseen Deep Architectures - With First Author Boris Knyazev

Explore parameter prediction for neural networks using graph hypernetworks. Learn about DeepNets-1M dataset, training techniques, and experimental results for efficient network training paradigms.

• YouTube
• 48 minutes
• On-Demand
• Free Video

Learning Rate Grafting: Transferability of Optimizer Tuning - Machine Learning Research Paper Review

Explore grafting technique for transferring learning rate schedules between optimizers, improving deep learning model performance and reducing computational costs in hyperparameter tuning.

• YouTube
• 39 minutes
• On-Demand
• Free Video

Gradients Are Not All You Need - Machine Learning Research Paper Explained

Explores limitations of differentiable programming in machine learning, focusing on chaos-based failures in various systems. Discusses alternatives to backpropagation for gradient estimation in complex, stochastic environments.

• YouTube
• 49 minutes
• On-Demand
• Free Video

Autoregressive Diffusion Models - Machine Learning Research Paper Explained

Explore Autoregressive Diffusion Models, a novel approach combining autoregressive and diffusion models for efficient, order-agnostic generation and compression of text and image data.

• YouTube
• 34 minutes
• On-Demand
• Free Video

EfficientZero - Mastering Atari Games with Limited Data

Explore EfficientZero, a groundbreaking reinforcement learning algorithm achieving human-level performance on Atari games with minimal data, outperforming previous methods in sample efficiency and performance.

• YouTube
• 29 minutes
• On-Demand
• Free Video