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Test-Time Adaptation: the key to reasoning with DL (Mohamed Osman)

1:03:36
 
공유
 

Manage episode 472811068 series 2803422
Machine Learning Street Talk (MLST)에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Machine Learning Street Talk (MLST) 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

Mohamed Osman joins to discuss MindsAI's highest scoring entry to the ARC challenge 2024 and the paradigm of test-time fine-tuning. They explore how the team, now part of Tufa Labs in Zurich, achieved state-of-the-art results using a combination of pre-training techniques, a unique meta-learning strategy, and an ensemble voting mechanism. Mohamed emphasizes the importance of raw data input and flexibility of the network.

SPONSOR MESSAGES:

***

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 + REFS:

https://www.dropbox.com/scl/fi/jeavyqidsjzjgjgd7ns7h/MoFInal.pdf?rlkey=cjjmo7rgtenxrr3b46nk6yq2e&dl=0

Mohamed Osman (Tufa Labs)

https://x.com/MohamedOsmanML

Jack Cole (Tufa Labs)

https://x.com/MindsAI_Jack

How and why deep learning for ARC paper:

https://github.com/MohamedOsman1998/deep-learning-for-arc/blob/main/deep_learning_for_arc.pdf

TOC:

1. Abstract Reasoning Foundations

[00:00:00] 1.1 Test-Time Fine-Tuning and ARC Challenge Overview

[00:10:20] 1.2 Neural Networks vs Programmatic Approaches to Reasoning

[00:13:23] 1.3 Code-Based Learning and Meta-Model Architecture

[00:20:26] 1.4 Technical Implementation with Long T5 Model

2. ARC Solution Architectures

[00:24:10] 2.1 Test-Time Tuning and Voting Methods for ARC Solutions

[00:27:54] 2.2 Model Generalization and Function Generation Challenges

[00:32:53] 2.3 Input Representation and VLM Limitations

[00:36:21] 2.4 Architecture Innovation and Cross-Modal Integration

[00:40:05] 2.5 Future of ARC Challenge and Program Synthesis Approaches

3. Advanced Systems Integration

[00:43:00] 3.1 DreamCoder Evolution and LLM Integration

[00:50:07] 3.2 MindsAI Team Progress and Acquisition by Tufa Labs

[00:54:15] 3.3 ARC v2 Development and Performance Scaling

[00:58:22] 3.4 Intelligence Benchmarks and Transformer Limitations

[01:01:50] 3.5 Neural Architecture Optimization and Processing Distribution

REFS:

[00:01:32] Original ARC challenge paper, François Chollet

https://arxiv.org/abs/1911.01547

[00:06:55] DreamCoder, Kevin Ellis et al.

https://arxiv.org/abs/2006.08381

[00:12:50] Deep Learning with Python, François Chollet

https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438

[00:13:35] Deep Learning with Python, François Chollet

https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438

[00:13:35] Influence of pretraining data for reasoning, Laura Ruis

https://arxiv.org/abs/2411.12580

[00:17:50] Latent Program Networks, Clement Bonnet

https://arxiv.org/html/2411.08706v1

[00:20:50] T5, Colin Raffel et al.

https://arxiv.org/abs/1910.10683

[00:30:30] Combining Induction and Transduction for Abstract Reasoning, Wen-Ding Li, Kevin Ellis et al.

https://arxiv.org/abs/2411.02272

[00:34:15] Six finger problem, Chen et al.

https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_SpatialVLM_Endowing_Vision-Language_Models_with_Spatial_Reasoning_Capabilities_CVPR_2024_paper.pdf

[00:38:15] DeepSeek-R1-Distill-Llama, DeepSeek AI

https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B

[00:40:10] ARC Prize 2024 Technical Report, François Chollet et al.

https://arxiv.org/html/2412.04604v2

[00:45:20] LLM-Guided Compositional Program Synthesis, Wen-Ding Li and Kevin Ellis

https://arxiv.org/html/2503.15540

[00:54:25] Abstraction and Reasoning Corpus, François Chollet

https://github.com/fchollet/ARC-AGI

[00:57:10] O3 breakthrough on ARC-AGI, OpenAI

https://arcprize.org/

[00:59:35] ConceptARC Benchmark, Arseny Moskvichev, Melanie Mitchell

https://arxiv.org/abs/2305.07141

[01:02:05] Mixtape: Breaking the Softmax Bottleneck Efficiently, Yang, Zhilin and Dai, Zihang and Salakhutdinov, Ruslan and Cohen, William W.

http://papers.neurips.cc/paper/9723-mixtape-breaking-the-softmax-bottleneck-efficiently.pdf

  continue reading

233 에피소드

Artwork
icon공유
 
Manage episode 472811068 series 2803422
Machine Learning Street Talk (MLST)에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Machine Learning Street Talk (MLST) 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

Mohamed Osman joins to discuss MindsAI's highest scoring entry to the ARC challenge 2024 and the paradigm of test-time fine-tuning. They explore how the team, now part of Tufa Labs in Zurich, achieved state-of-the-art results using a combination of pre-training techniques, a unique meta-learning strategy, and an ensemble voting mechanism. Mohamed emphasizes the importance of raw data input and flexibility of the network.

SPONSOR MESSAGES:

***

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 + REFS:

https://www.dropbox.com/scl/fi/jeavyqidsjzjgjgd7ns7h/MoFInal.pdf?rlkey=cjjmo7rgtenxrr3b46nk6yq2e&dl=0

Mohamed Osman (Tufa Labs)

https://x.com/MohamedOsmanML

Jack Cole (Tufa Labs)

https://x.com/MindsAI_Jack

How and why deep learning for ARC paper:

https://github.com/MohamedOsman1998/deep-learning-for-arc/blob/main/deep_learning_for_arc.pdf

TOC:

1. Abstract Reasoning Foundations

[00:00:00] 1.1 Test-Time Fine-Tuning and ARC Challenge Overview

[00:10:20] 1.2 Neural Networks vs Programmatic Approaches to Reasoning

[00:13:23] 1.3 Code-Based Learning and Meta-Model Architecture

[00:20:26] 1.4 Technical Implementation with Long T5 Model

2. ARC Solution Architectures

[00:24:10] 2.1 Test-Time Tuning and Voting Methods for ARC Solutions

[00:27:54] 2.2 Model Generalization and Function Generation Challenges

[00:32:53] 2.3 Input Representation and VLM Limitations

[00:36:21] 2.4 Architecture Innovation and Cross-Modal Integration

[00:40:05] 2.5 Future of ARC Challenge and Program Synthesis Approaches

3. Advanced Systems Integration

[00:43:00] 3.1 DreamCoder Evolution and LLM Integration

[00:50:07] 3.2 MindsAI Team Progress and Acquisition by Tufa Labs

[00:54:15] 3.3 ARC v2 Development and Performance Scaling

[00:58:22] 3.4 Intelligence Benchmarks and Transformer Limitations

[01:01:50] 3.5 Neural Architecture Optimization and Processing Distribution

REFS:

[00:01:32] Original ARC challenge paper, François Chollet

https://arxiv.org/abs/1911.01547

[00:06:55] DreamCoder, Kevin Ellis et al.

https://arxiv.org/abs/2006.08381

[00:12:50] Deep Learning with Python, François Chollet

https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438

[00:13:35] Deep Learning with Python, François Chollet

https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438

[00:13:35] Influence of pretraining data for reasoning, Laura Ruis

https://arxiv.org/abs/2411.12580

[00:17:50] Latent Program Networks, Clement Bonnet

https://arxiv.org/html/2411.08706v1

[00:20:50] T5, Colin Raffel et al.

https://arxiv.org/abs/1910.10683

[00:30:30] Combining Induction and Transduction for Abstract Reasoning, Wen-Ding Li, Kevin Ellis et al.

https://arxiv.org/abs/2411.02272

[00:34:15] Six finger problem, Chen et al.

https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_SpatialVLM_Endowing_Vision-Language_Models_with_Spatial_Reasoning_Capabilities_CVPR_2024_paper.pdf

[00:38:15] DeepSeek-R1-Distill-Llama, DeepSeek AI

https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B

[00:40:10] ARC Prize 2024 Technical Report, François Chollet et al.

https://arxiv.org/html/2412.04604v2

[00:45:20] LLM-Guided Compositional Program Synthesis, Wen-Ding Li and Kevin Ellis

https://arxiv.org/html/2503.15540

[00:54:25] Abstraction and Reasoning Corpus, François Chollet

https://github.com/fchollet/ARC-AGI

[00:57:10] O3 breakthrough on ARC-AGI, OpenAI

https://arcprize.org/

[00:59:35] ConceptARC Benchmark, Arseny Moskvichev, Melanie Mitchell

https://arxiv.org/abs/2305.07141

[01:02:05] Mixtape: Breaking the Softmax Bottleneck Efficiently, Yang, Zhilin and Dai, Zihang and Salakhutdinov, Ruslan and Cohen, William W.

http://papers.neurips.cc/paper/9723-mixtape-breaking-the-softmax-bottleneck-efficiently.pdf

  continue reading

233 에피소드

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