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Clement Bonnet - Can Latent Program Networks Solve Abstract Reasoning?
Manage episode 467488048 series 2803422
Clement Bonnet discusses his novel approach to the ARC (Abstraction and Reasoning Corpus) challenge. Unlike approaches that rely on fine-tuning LLMs or generating samples at inference time, Clement's method encodes input-output pairs into a latent space, optimizes this representation with a search algorithm, and decodes outputs for new inputs. This end-to-end architecture uses a VAE loss, including reconstruction and prior losses.
SPONSOR MESSAGES:
***
CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Check out their super fast DeepSeek R1 hosting!
https://centml.ai/pricing/
Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich.
Goto https://tufalabs.ai/
***
TRANSCRIPT + RESEARCH OVERVIEW:
https://www.dropbox.com/scl/fi/j7m0gaz1126y594gswtma/CLEMMLST.pdf?rlkey=y5qvwq2er5nchbcibm07rcfpq&dl=0
Clem and Matthew-
https://www.linkedin.com/in/clement-bonnet16/
https://github.com/clement-bonnet
https://mvmacfarlane.github.io/
TOC
1. LPN Fundamentals
[00:00:00] 1.1 Introduction to ARC Benchmark and LPN Overview
[00:05:05] 1.2 Neural Networks' Challenges with ARC and Program Synthesis
[00:06:55] 1.3 Induction vs Transduction in Machine Learning
2. LPN Architecture and Latent Space
[00:11:50] 2.1 LPN Architecture and Latent Space Implementation
[00:16:25] 2.2 LPN Latent Space Encoding and VAE Architecture
[00:20:25] 2.3 Gradient-Based Search Training Strategy
[00:23:39] 2.4 LPN Model Architecture and Implementation Details
3. Implementation and Scaling
[00:27:34] 3.1 Training Data Generation and re-ARC Framework
[00:31:28] 3.2 Limitations of Latent Space and Multi-Thread Search
[00:34:43] 3.3 Program Composition and Computational Graph Architecture
4. Advanced Concepts and Future Directions
[00:45:09] 4.1 AI Creativity and Program Synthesis Approaches
[00:49:47] 4.2 Scaling and Interpretability in Latent Space Models
REFS
[00:00:05] ARC benchmark, Chollet
https://arxiv.org/abs/2412.04604
[00:02:10] Latent Program Spaces, Bonnet, Macfarlane
https://arxiv.org/abs/2411.08706
[00:07:45] Kevin Ellis work on program generation
https://www.cs.cornell.edu/~ellisk/
[00:08:45] Induction vs transduction in abstract reasoning, Li et al.
https://arxiv.org/abs/2411.02272
[00:17:40] VAEs, Kingma, Welling
https://arxiv.org/abs/1312.6114
[00:27:50] re-ARC, Hodel
https://github.com/michaelhodel/re-arc
[00:29:40] Grid size in ARC tasks, Chollet
https://github.com/fchollet/ARC-AGI
[00:33:00] Critique of deep learning, Marcus
https://arxiv.org/vc/arxiv/papers/2002/2002.06177v1.pdf
232 에피소드
Manage episode 467488048 series 2803422
Clement Bonnet discusses his novel approach to the ARC (Abstraction and Reasoning Corpus) challenge. Unlike approaches that rely on fine-tuning LLMs or generating samples at inference time, Clement's method encodes input-output pairs into a latent space, optimizes this representation with a search algorithm, and decodes outputs for new inputs. This end-to-end architecture uses a VAE loss, including reconstruction and prior losses.
SPONSOR MESSAGES:
***
CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Check out their super fast DeepSeek R1 hosting!
https://centml.ai/pricing/
Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich.
Goto https://tufalabs.ai/
***
TRANSCRIPT + RESEARCH OVERVIEW:
https://www.dropbox.com/scl/fi/j7m0gaz1126y594gswtma/CLEMMLST.pdf?rlkey=y5qvwq2er5nchbcibm07rcfpq&dl=0
Clem and Matthew-
https://www.linkedin.com/in/clement-bonnet16/
https://github.com/clement-bonnet
https://mvmacfarlane.github.io/
TOC
1. LPN Fundamentals
[00:00:00] 1.1 Introduction to ARC Benchmark and LPN Overview
[00:05:05] 1.2 Neural Networks' Challenges with ARC and Program Synthesis
[00:06:55] 1.3 Induction vs Transduction in Machine Learning
2. LPN Architecture and Latent Space
[00:11:50] 2.1 LPN Architecture and Latent Space Implementation
[00:16:25] 2.2 LPN Latent Space Encoding and VAE Architecture
[00:20:25] 2.3 Gradient-Based Search Training Strategy
[00:23:39] 2.4 LPN Model Architecture and Implementation Details
3. Implementation and Scaling
[00:27:34] 3.1 Training Data Generation and re-ARC Framework
[00:31:28] 3.2 Limitations of Latent Space and Multi-Thread Search
[00:34:43] 3.3 Program Composition and Computational Graph Architecture
4. Advanced Concepts and Future Directions
[00:45:09] 4.1 AI Creativity and Program Synthesis Approaches
[00:49:47] 4.2 Scaling and Interpretability in Latent Space Models
REFS
[00:00:05] ARC benchmark, Chollet
https://arxiv.org/abs/2412.04604
[00:02:10] Latent Program Spaces, Bonnet, Macfarlane
https://arxiv.org/abs/2411.08706
[00:07:45] Kevin Ellis work on program generation
https://www.cs.cornell.edu/~ellisk/
[00:08:45] Induction vs transduction in abstract reasoning, Li et al.
https://arxiv.org/abs/2411.02272
[00:17:40] VAEs, Kingma, Welling
https://arxiv.org/abs/1312.6114
[00:27:50] re-ARC, Hodel
https://github.com/michaelhodel/re-arc
[00:29:40] Grid size in ARC tasks, Chollet
https://github.com/fchollet/ARC-AGI
[00:33:00] Critique of deep learning, Marcus
https://arxiv.org/vc/arxiv/papers/2002/2002.06177v1.pdf
232 에피소드
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