MLab
ACMLab is Stanford's premier machine learning club. Its goal is to teach anyone with basic CS experience machine learning. After an intensive ramp-up workshop in the fall, members work on publishing papers at top ML conferences and workshops. We have published 6 workshop papers so far at top conferences and workshops such as ACL and ICLR. Alumni have gone on to Google AI, Stanford ML Group, Stanford NLP Group, and VMWare.
Board
Patrick Liu
2024
Co-Director
Niveditha Iyer
2024
Co-Director
Erik Rozi
2024
Co-Director
Fall 2022 Schedule
Fri Nov 04
ACM Info Session
Fri Nov 11
Workshop 1: Shallow Neural Networks
Fri Nov 18
Workshop 2: Deep Neural Networks with Pytorch
Fri Nov 25
Workshop 3: CNNs
Thu Dec 01
Workshop 4: Implementation I
Thu Dec 08
Workshop 5: Implementation II
Thu Dec 15
Project Office Hours
Thu Dec 22
Project Office Hours
Thu Dec 29
Onboarding Projects Due
Recent Projects
SemEVAL
We submitted to the Workshop on Semantic Evaluation's Task 1 (lexical complexity modelling) and Task 8 (automatically extracting measurements from scientific text). Our teams performed competitively on both tasks, including second place in one of the Task 8 subcategories. Our task description papers will appear at SemEval at ACL 2021.
Google BIG-Bench
Members proposed 4 tasks to be used in Google's BIG-Bench challenge. The purpose of this challenge was to create a collaborative benchmark for enourmous language models like GPT-3. MLab submitted tasks about temporal sequences, logic puzzles, sarcasm, and IPA translation.
VQA
We are currently preparing a submission on the ChartQA workshop at CVPR 2021, aiming to automatically parse structured information from diverse chart-based visual representations.