yannic kilcher

1:09:00

[GRPO Explained] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

0:00:32

Yannic Kilcher on PhD's for ML #shorts

0:53:02

Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters (Paper)

0:27:48

Were RNNs All We Needed? (Paper Explained)

0:01:01

Yannic Kilcher on superintelligence #machineleaning

0:28:23

TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters (Paper Explained)

0:10:49

My GitHub (Trash code I wrote during PhD)

0:29:47

Grokking: Generalization beyond Overfitting on small algorithmic datasets (Paper Explained)

0:27:07

Attention Is All You Need

1:11:58

Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools (Paper Explained)

0:56:16

Flow Matching for Generative Modeling (Paper Explained)

0:37:06

GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

0:59:38

JEPA - A Path Towards Autonomous Machine Intelligence (Paper Explained)

0:45:44

What is Q-Learning (back to basics)

0:48:53

Safety Alignment Should be Made More Than Just a Few Tokens Deep (Paper Explained)

0:36:15

Byte Latent Transformer: Patches Scale Better Than Tokens (Paper Explained)

0:40:40

Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Paper Explained)

0:29:56

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Paper Explained)

0:15:12

No, Anthropic's Claude 3 is NOT sentient

0:57:00

xLSTM: Extended Long Short-Term Memory

0:19:20

GPT-4chan: This is the worst AI ever

1:05:16

Hopfield Networks is All You Need (Paper Explained)

0:48:07

OpenAI CLIP: ConnectingText and Images (Paper Explained)