Test-Time Training Agents to Solve Challenging Problems | Jonas Huebotter (ETH Zurich)
heidelberg.ai
Test-Time Training Agents to Solve Challenging Problems | Jonas Huebotter (ETH Zurich)
46:13
Deploying General AI in the Private World
heidelberg.ai
Deploying General AI in the Private World
44:47
Pushing Boundaries of Structure-from-Motion with Machine Learning | Eric Brachmann (Niantic Spatial)
heidelberg.ai
Pushing Boundaries of Structure-from-Motion with Machine Learning | Eric Brachmann (Niantic Spatial)
47:27
Post-Pretraining in Vision, and Language Foundation Models | Yuki M. Asano (UTN)
heidelberg.ai
Post-Pretraining in Vision, and Language Foundation Models | Yuki M. Asano (UTN)
43:42
Virtual Cells and Digital Twins: AI in Personalized Oncology | Charlotte Bunne (EPFL)
heidelberg.ai
Virtual Cells and Digital Twins: AI in Personalized Oncology | Charlotte Bunne (EPFL)
48:50
Conformal Prediction under Ambiguous Ground Truth | David Stutz (Google Deepmind)
heidelberg.ai
Conformal Prediction under Ambiguous Ground Truth | David Stutz (Google Deepmind)
35:11
Interpretable Vision and Language Models | Zeynep Akata (TUM)
heidelberg.ai
Interpretable Vision and Language Models | Zeynep Akata (TUM)
58:01
Building Foundation Models in Ophthamology | Pearse Keane (University College London)
heidelberg.ai
Building Foundation Models in Ophthamology | Pearse Keane (University College London)
50:26
Learning Dynamical Laws from Data | Niki Kilbertus (TUM & Helmholtz AI)
heidelberg.ai
Learning Dynamical Laws from Data | Niki Kilbertus (TUM & Helmholtz AI)
1:07:35
Stable Diffusion and Friends - Generative Modeling in Latent Space | Robin Rombach (Stability AI)
heidelberg.ai
Stable Diffusion and Friends - Generative Modeling in Latent Space | Robin Rombach (Stability AI)
57:58
Human compatible World Models across Sizes, Languages and Modalities | Jonas Andrulis (Aleph Alpha)
heidelberg.ai
Human compatible World Models across Sizes, Languages and Modalities | Jonas Andrulis (Aleph Alpha)
56:45
Highly Accurate Protein Structure Prediction with AlphaFold | SimonKohl
heidelberg.ai
Highly Accurate Protein Structure Prediction with AlphaFold | SimonKohl
52:34
Why Domain Knowledge is Crucial for Machine Learning-based Medical Image Analysis | Lena Maier-Hein
heidelberg.ai
Why Domain Knowledge is Crucial for Machine Learning-based Medical Image Analysis | Lena Maier-Hein
1:08:22
A Brief Overview of the Success Story of Large Language Models | Timo Denk
heidelberg.ai
A Brief Overview of the Success Story of Large Language Models | Timo Denk
57:53
Signed Graph Partitioning: An Important Primitive in Computer Vision | Fred Hamprecht
heidelberg.ai
Signed Graph Partitioning: An Important Primitive in Computer Vision | Fred Hamprecht
1:02:17
From Development to a Certified Medical Product: Bringing AI solutions to the Patient | Philipp Mann
heidelberg.ai
From Development to a Certified Medical Product: Bringing AI solutions to the Patient | Philipp Mann
46:38
Deep Learning on Graphs: Successes, Challenges, and Next Steps | Michael Bronstein
heidelberg.ai
Deep Learning on Graphs: Successes, Challenges, and Next Steps | Michael Bronstein
1:24:00
Uncertainty, Causality and Generalization | Ben Glocker
heidelberg.ai
Uncertainty, Causality and Generalization | Ben Glocker
1:07:27
Learning Equivariant and Hybrid Message Passing on Graphs | Max Welling
heidelberg.ai
Learning Equivariant and Hybrid Message Passing on Graphs | Max Welling
1:11:40
Cross-Lingual Transfer Learning | Sebastian Ruder
heidelberg.ai
Cross-Lingual Transfer Learning | Sebastian Ruder
1:17:22
Analyzing Inverse Problems in Natural Science using Invertible Neural Networks | Ullrich Köthe
heidelberg.ai
Analyzing Inverse Problems in Natural Science using Invertible Neural Networks | Ullrich Köthe
59:40
Leveraging Bayesian Uncertainty Information | Christian Leibig | heidelberg.ai
heidelberg.ai
Leveraging Bayesian Uncertainty Information | Christian Leibig | heidelberg.ai
1:02:38
Self Supervision - Learning to Learn | Björn Ommer | heidelberg.ai
heidelberg.ai
Self Supervision - Learning to Learn | Björn Ommer | heidelberg.ai
1:14:22
Learning the Structure of Graph Neural Networks | Mathias Niepert | heidelberg.ai
heidelberg.ai
Learning the Structure of Graph Neural Networks | Mathias Niepert | heidelberg.ai
1:28:00
Towards Motor Skill Learning | Jan Peters | heidelberg.ai
heidelberg.ai
Towards Motor Skill Learning | Jan Peters | heidelberg.ai
1:12:42
Generative Query Networks & Neural Processes | Marta Garnelo | heidelberg.ai
heidelberg.ai
Generative Query Networks & Neural Processes | Marta Garnelo | heidelberg.ai
1:00:07
How Probabilistic Thinking and ML are Disrupting Retail | Christian Scherrer | heidelberg.ai
heidelberg.ai
How Probabilistic Thinking and ML are Disrupting Retail | Christian Scherrer | heidelberg.ai
1:12:35
Modelling Probability Distributions using Neural Networks | Christian Baumgartner | heidelberg.ai
heidelberg.ai
Modelling Probability Distributions using Neural Networks | Christian Baumgartner | heidelberg.ai
1:07:17
Deep Learning to Solve Challenging Problems | Jeff Dean | heidelberg.ai
heidelberg.ai
Deep Learning to Solve Challenging Problems | Jeff Dean | heidelberg.ai
1:50:31
heidelberg.ai - Deep Generative Models (Tutorial)
heidelberg.ai
heidelberg.ai - Deep Generative Models (Tutorial)
30:00