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Deep Learning 9/26/24
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Classroom Contents
Mathematics and Machine Learning Program - September 3 to November 1, 2024
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- 1 Giorgi Butbaia | Machine learning smooth 4-genus of a knot
- 2 Petros Koumoutsakos| Learning the effective dynamics of complex systems
- 3 Tristan Buckmaster | Singularities in fluids
- 4 James Halverson | Learning the Topological Invariance of Knots
- 5 Kyu-Hwan Lee | Discovering New Mathematical Structures with Machine Learning
- 6 Eric Vanden Eijnden|Generative modeling w/flows & diffusions, w/applications to scientific computing
- 7 Bin Dong | AI for Mathematics: From Digitization to Intelligentization
- 8 Stephane Mallat | Image Generation by Score Diffusion and the Renormalisation Group
- 9 Wagner et al. | Sparse subgraphs of the d-cube with diameter d
- 10 Angelica Babei | Learning Euler factors of elliptic curves with transformers
- 11 Yang Hui He | AI assisted mathematics
- 12 Edgar Costa | Machine learning L-functions
- 13 Matej Balog | FunSearch: Mathematical discoveries from program search with large language models
- 14 Cengiz Pehlevan | Solvable Models of Scaling and Emergence in Deep Learning
- 15 Fabian Ruehle | Rigorous results from ML using RL
- 16 Ankit Anand and Abbas Mehrabian | From Theorem Proving to Disproving
- 17 Jürgen Jost | Data visualization with category theory and geometry
- 18 Deep Learning 10/22/24
- 19 Math and Machine Learning Program 10/21/24
- 20 Math and Machine Learning Program 10/23/24
- 21 Math and Machine Learning Program 10/17/24
- 22 Math and Machine Learning Program 10/15/24 | Tutorial on the Lean theorem prover
- 23 Math and Machine Learning Program Discussion 10/16/24
- 24 Deep Learning 10/15/24
- 25 Deep Learning 10/11/24
- 26 Deep Learning 9/19/24
- 27 Math and Machine Learning Program 10/7/24
- 28 Deep Learning 10/8/24
- 29 Math and Machine Learning Program 10/8/24
- 30 Math and Machine Learning Program 10/3/2024
- 31 Math and Machine Learning Program 10/2/2024
- 32 Math and Machine Learning Program | Panel discussion on Machine Learning in Science Education
- 33 Math and Machine Learning Program 9/30/2024
- 34 Deep Learning 10/1/2024
- 35 Math and Machine Learning Program 9/27/2024
- 36 Deep Learning 9/26/24
- 37 Deep Learning 9/24/2024
- 38 CMSA Math and Machine Learning Program 9/23/2024
- 39 Deep Learning 9/17/2024
- 40 Deep Learning 9/12/2024
- 41 Deep Learning 9/10/2024
- 42 Kun-Hsing Yu | Foundation Models for Real-Time Cancer Diagnosis
- 43 Boris Hanin | Scaling Limits of Neural Networks
- 44 Melanie Weber | Data and Model Geometry in Deep Learning
- 45 Bianca Dumitrascu|Statistical machine learning for learning representations of embryonic development
- 46 Tianxi Cai|Crowdsourcing with Multi-institutional EHR to Improve Reliability of Real World Evidence
- 47 Neil Thompson | How Algorithmic Progress is driving progress in Big Data and AI
- 48 Raj Chetty | The Science of Economic Opportunity: New Insights from Big Data
- 49 Kavita Ramanan | Understanding High-dimensional Stochastic Dynamics on Realistic Networks
- 50 Jamie Morgenstern | What governs predictive disparity in modern machine learning applications?
- 51 Peter Hull | Measuring Discrimination in Multi-Phase Systems with an Application to Child Protection
- 52 CMSA Math and Machine Learning Program 9/13/24
- 53 CMSA Math and Machine Learning Program 9/11/24
- 54 CMSA Math and Machine Learning Program 9/10/24
- 55 CMSA Math and Machine Learning Program 9/9/24
- 56 Geordie Williamson | Can AI help with hard mathematics?
- 57 Geordie Williamson | Using saliency analysis to discover structure
- 58 Mathematics and Machine Learning Program Opening Workshop | Panel Discussion
- 59 Leon Bottou | Conceptual challenges in modern AI
- 60 François Charton | Transformers meet Lyapunov
- 61 Adam Wagner | Case studies I: Reinforcement learning
- 62 Boris Hanin: Theory of Machine Learning
- 63 Panel Discussion: Automated mathematical discovery
- 64 David McAllester | Logic and formal methods
- 65 Mike Douglas | Overview of AI for mathematics
