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1:14:55
ThunderKittens goes live: AMA and library walkthrough
0:52:27
AI Systems in Government: Challenges & Opportunities - Jared Dunnmon | Stanford MLSys#100
1:08:55
Automating Enterprises With Foundation Models - Avanika Narayan & Michael Wornow | Stanford MLSys#99
1:06:34
Teaching LLMs to Use Tools at Scale - Shishir Patil | Stanford MLSys #98
0:55:01
Scaling Up “Vibe Checks” for LLMs - Shreya Shankar | Stanford MLSys #97
1:04:36
EVO: DNA Foundation Models - Eric Nguyen | Stanford MLSys #96
0:54:36
Towards Conversational Diagnostic AI - Khaled Saab | Stanford MLSys #95
0:58:13
How Fine-tuning Open Source LLMs Solves GenAI Productionization - Piero Molino | Stanford MLSys #94
0:44:52
Online A/B Testing of Real-Time Event Detection Systems - David Tagliamonti | Stanford MLSys #93
0:59:21
The Next 100x - Gavin Uberti | Stanford MLSys #92
1:11:17
Large Language Models for Program Optimization - Osbert Bastani | Stanford MLSys #91
0:52:56
Multimodal Reasoning: PaLM-E & Gemini - Aakanksha Chowdhery | Stanford MLSys #90
0:57:05
Text2SQL: The Dream versus Reality - Laurel Orr | Stanford MLSys #89
1:16:48
Notes on AI Hardware - Benjamin Spector | Stanford MLSys #88
1:19:06
Hardware-aware Algorithms for Sequence Modeling - Tri Dao | Stanford MLSys #87
0:56:32
Monarch Mixer: Making Foundation Models More Efficient - Dan Fu | Stanford MLSys #86
0:47:35
Foundation Models on Consumer Devices - Tianqi Chen | Stanford MLSys #85
0:59:17
Serving 100s of LLMs on 1 GPU with LoRAX - Travis Addair | Stanford MLSys #84
0:55:59
Training LLMs at Scale - Deepak Narayanan | Stanford MLSys #83
0:58:25
Democratizing Foundation Models via k-bit Quantization - Tim Dettmers | Stanford MLSys #82
0:58:29
A Taxonomy of ML for Systems Problems - Martin Maas | Stanford MLSys #81
0:58:07
ML for ML Compilers - Mangpo Phothilimthana | Stanford MLSys #80
1:02:38
AI Safety, RLHF, and Self-Supervision - Jared Kaplan | Stanford MLSys #79
1:00:29
Common Sense as Dark Matter - Yejin Choi | Stanford MLSys #78
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