Type of course: Seminar
Study programs:
Master Computer Science
Master Data Science
Master Software Systems Engineering
Offering chair: Machine
Learning and Reasoning (i6), RWTH
Description: Students
will present papers from a list of important,
recent works in machine learning compiled by the
professor. Topics include deep and reinforcement
learning, and transformer and GNN architectures.
The focus will be in the use of these techniques
in simple applications that are crisp and
easy to understand. The list of papers will also
include those underlying well-known learning
systems like as ChaptGPT, AlphaZero, Gato,
etc. Final list of papers and topics to be covered
to be defined after the first meeting.
This is a new seminar by a new professor
and the offer is coming a bit late. Applications
will be accepted until the kickoff meeting.
Recommended prior knowledge:
Bachelor degree in CS or Equivalent. Basic AI and
ML courses.
Kickoff: 28/3/2023, 15h-17h,
Room 228, Theaterstr. 35, 2nd floor
Format,
organization, evaluation: TBA
Website: https://www-i6.informatik.rwth-aachen.de/~hector.geffner/Seminar-S2023.html
Introduction --
Slides
Tentative, partial list of
papers
RL/Planning
Playing
Atari with Deep Reinforcement
Learning
Volodymyr
Mnih, Koray Kavukcuoglu, David
Silver, et al, 2013
Mastering
Chess and Shogi by Self-Play with
a General Reinforcement Learning
Algorithm
David
Silver, Thomas Hubert, Julian
Schrittwieser, et al, 2017
Mastering
Atari, Go, Chess and Shogi by
Planning with a Learned Model
Julian
Schrittwieser, Ioannis Antonoglou,
Thomas Hubert, et al, 2020
Transformers
are Sample-Efficient World Models
Vincent
Micheli, Eloi Alonso, Francois
Fleuret, 2022
A
Generalist Agent
Scott
Reed, Konrad Zolna, Emilio
Parisotto, et al., 2023
Deep learning: Transformers
End-to-end symbolic
regression with transformers
Pierre-Alexandre Kamienny, Stephane d'Ascoli, Guillaume
Lample, Francois Charton, 2022
Linear algebra
with transformers
Francois Charton, 2021
What Can
Transformers Learn In-Context? A Case Study of Simple
Function Classes
Shivam Garg, Dimitris Tsipras, Percy Liang, Gregory
Valiant, 2023
Large Language Models, RL, Planning
Training
language models to follow instructions with human
feedback (ChatGPT)
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, et al,
2022
ReAct:
Synergizing Reasoning and Acting in Language
Models
S.
Yao, J. Zhao, D. Yu, et al. 3/2023
Guiding
Pretraining in Reinforcement Learning with Large
Language Models.
Yuqing Du, Olivia Watkins,
Zihan Wang, Cédric Colas, Trevor Darrell, Pieter
Abbeel, Abhishek Gupta, Jacob Andreas. 2/2023
On the
Planning Abilities of Large Language Models (A
Critical Investigation with a Proposed Benchmark)
K. Valmeekam, S.
Sreedharan, M. Marquez, A. Olmo, S.
Kambhampati. 2/2023