Mechanical Dirk

Mechanical Dreams

Science EN ↓ 153 episodes

An automatically generated podcast about machine learning and natural language processing. The two fictional hosts talk about papers that I want to learn more about on my way to work. It's not good, but it's useful.

Author

Mechanical Dirk

Episodes

Phi-4 14.12.2024 9:05

Rephrasing natural text data with different languages and quality levels 13.12.2024 11:02

Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs 12.12.2024 11:59

EXAONE 3.5 11.12.2024 8:59

Model soups - averaging weights of multiple fine-tuned models improves accuracy without increasing inference time 09.12.2024 6:15

Model-Aware Data Selection for Efficient Pretraining with Data Influence Models 06.12.2024 13:14

Nemotron-CC 05.12.2024 12:39

Tülu 3 02.12.2024 12:03

The Zamba2 Suite 29.11.2024 13:04

Small-scale proxies for large-scale Transformer training instabilities 26.11.2024 10:07

Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training 25.11.2024 10:29

I slightly tweaked the personality of the hosts.

Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations 19.11.2024 8:34

Understanding WSD Learning Rates 18.11.2024 9:10

Toward Understanding Why Adam Converges Faster Than SGD for Transformers 16.11.2024 7:30

New generation algorithm! Should make the episodes longer, more detailed, and more coherent.

Amuro & Char - Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models 08.11.2024 13:36

Evaluation data contamination in LLMs: How do we measure it and (when) does it matter? 07.11.2024 6:28

How Does Critical Batch Size Scale in Pre-training? 04.11.2024 7:41

The Road Less Scheduled 01.11.2024 8:54

Learning-Rate-Free Learning by D-Adaptation 31.10.2024 4:37

Scaling FP8 Training to Trillion Token LLMs 30.10.2024 9:55

Geometric Dynamics of Signal Propagation Predict Trainability of Transformers 29.10.2024 15:44

A Survey on Model MoErging 28.10.2024 8:58

Liquid Time-constant Networks 27.10.2024 8:11

Scaling Laws for Predicting Downstream Performance in LLMs 26.10.2024 10:07

A Spectral Condition for Feature Learning 25.10.2024 16:53

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