Mechanical Dreams

Mechanical Dreams

Por: Mechanical Dirk
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.
97 episodios disponibles

Latest episodes of the podcast Mechanical Dreams

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NExtLong - Toward Effective Long-Context Training without Long Documents 30/01/2025
Over-Tokenized Transformer 29/01/2025
Optimizing Pretraining Data Mixtures with LLM-Estimated Utility 29/01/2025
HashAttention: Semantic Sparsity for Faster Inference 16/01/2025
From Tokens to Words 14/01/2025
DeepSeek V3 07/01/2025
Optimal Linear Decay Learning Rate Schedules and Further Refinements 05/01/2025
Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers 20/12/2024
Efficient and Approximate Per-Example Gradient Norms for Gradient Noise Scale 19/12/2024
Phi-4 14/12/2024
Rephrasing natural text data with different languages and quality levels 13/12/2024
Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs 12/12/2024
EXAONE 3.5 10/12/2024
Model soups - averaging weights of multiple fine-tuned models improves accuracy without increasing inference time 09/12/2024
Model-Aware Data Selection for Efficient Pretraining with Data Influence Models 06/12/2024
Nemotron-CC 05/12/2024
Tülu 3 02/12/2024
The Zamba2 Suite 29/11/2024
Small-scale proxies for large-scale Transformer training instabilities 26/11/2024
Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training 25/11/2024
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