ThunderKittens goes live: AMA and library walkthrough

AI Systems in Government: Challenges & Opportunities - Jared Dunnmon | Stanford MLSys#100

Teaching LLMs to Use Tools at Scale - Shishir Patil | Stanford MLSys #98

Scaling Up “Vibe Checks” for LLMs - Shreya Shankar | Stanford MLSys #97

EVO: DNA Foundation Models - Eric Nguyen | Stanford MLSys #96

Towards Conversational Diagnostic AI - Khaled Saab | Stanford MLSys #95

How Fine-tuning Open Source LLMs Solves GenAI Productionization - Piero Molino | Stanford MLSys #94

Online A/B Testing of Real-Time Event Detection Systems - David Tagliamonti | Stanford MLSys #93

The Next 100x - Gavin Uberti | Stanford MLSys #92

Large Language Models for Program Optimization - Osbert Bastani | Stanford MLSys #91

Multimodal Reasoning: PaLM-E & Gemini - Aakanksha Chowdhery | Stanford MLSys #90

Text2SQL: The Dream versus Reality - Laurel Orr | Stanford MLSys #89

Notes on AI Hardware - Benjamin Spector | Stanford MLSys #88

Hardware-aware Algorithms for Sequence Modeling - Tri Dao | Stanford MLSys #87

Monarch Mixer: Making Foundation Models More Efficient - Dan Fu | Stanford MLSys #86

Foundation Models on Consumer Devices - Tianqi Chen | Stanford MLSys #85

Serving 100s of LLMs on 1 GPU with LoRAX - Travis Addair | Stanford MLSys #84

Training LLMs at Scale - Deepak Narayanan | Stanford MLSys #83

Democratizing Foundation Models via k-bit Quantization - Tim Dettmers | Stanford MLSys #82

A Taxonomy of ML for Systems Problems - Martin Maas | Stanford MLSys #81

ML for ML Compilers - Mangpo Phothilimthana | Stanford MLSys #80

AI Safety, RLHF, and Self-Supervision - Jared Kaplan | Stanford MLSys #79

Common Sense as Dark Matter - Yejin Choi | Stanford MLSys #78

Open Pretrained Transformers - Susan Zhang | Stanford MLSys #77

Compression for AGI - Jack Rae | Stanford MLSys #76

Poisoning Web-Scale Training Datasets - Nicholas Carlini | Stanford MLSys #75

Professional Norms in Generative AI - Rob Reich | Stanford MLSys #74

Unlocking the RNA Universe - Raphael Townshend | Stanford MLSys #73

Building ML Models like Open-Source Software - Colin Raffel | Stanford MLSys #72

A data-centric view on reliable generalization - Ludwig Schmidt | Stanford MLSys #71

Mechanistic Interpretability - Stella Biderman | Stanford MLSys #70

Pathways Language Model and Model Scaling - Aakanksha Chowdhery | Stanford MLSys #69

Distributed and Decentralized Learning - Ce Zhang | Stanford MLSys #68

FlashAttention - Tri Dao | Stanford MLSys #67

Machine Learning in Production - Roman Kazinnik | Stanford MLSys #66

What can Data-Centric AI Learn from Data and ML Engineering? - Alkis Polyzotis | Stanford MLSys #65

How Uber Predicts Arrival Times - Xinyu Hu and Olcay Cirit | Stanford MLSys #64

Robustness/Interpretability in Vision & Language Models - Arjun Akula | Stanford MLSys #63

Improving Transfer and Robustness of Supervised Contrastive Learning - Dan Fu | Stanford MLSys #62

Big Data Analytics - Kexin Rong | Stanford MLSys #61

Looper: An End-to-End ML Platform for Product Decisions - Igor Markov | Stanford MLSys #60

Alpa: Automated Model-Parallel Deep Learning - Zhuohan Li | Stanford MLSys #59

Translating Thousands of Languages - Shruti Bhosale | Stanford MLSys #58

Tiny ML, Harvard Style - Vijay Janapa Reddi | Stanford MLSys #57

Interactive Model Development - Fait Poms | Stanford MLSys #56

Lux: Visualization for Data Science - Doris Lee | Stanford MLSys #55

Symbols and Rules with Deep Learning - Ellie Pavlick | Stanford MLSys #54

Data Selection for Data-Centric AI - Cody Coleman | Stanford MLSys #53

How Facebook/Meta Design Sustainable Datacenters with AI - Bilge Acun | Stanford MLSys #52

Weak Supervision for Diverse Datatypes - Fred Sala | Stanford MLSys #51

Resource-Efficient Deep Learning Execution - Deepak Narayanan | Stanford MLSys #50

Pixelated Butterfly: Fast Machine Learning with Sparsity - Beidi Chen | Stanford MLSys #49

Open-Source Systems for Federated Learning | Stanford MLSys #48

Data Science for Infrastructure w Pixie CEO Zain Asgar | Stanford MLSys #47

Efficiently Modeling Long Sequences with Structured State Spaces - Albert Gu | Stanford MLSys #46

Learning from MASSIVE data feat UCLA Prof Baharan Mirzasoleiman | Stanford MLSys #45

ML Ops System Design feat Comet ML CEO Gideon Mendels | Stanford MLSys #44

CMU Prof Zhihao Jia on Automatically Optimizing ML with TASO and PET | Stanford MLSys #43

Using Machine Learning to fix bugs w Baishakhi Ray | Stanford MLSys #42

How to run a BILLION IoT devices w/ Mi Zhang | Stanford MLSys #41