Journal Club Advanced Machine Learning

The Research Seminar: Advanced Machine Learning is a weekly reading group. The focus is on discussing current research papers and results in machine learning. The target audience is active researchers (postdocs, phd students and advanced master students) in the field who want to discuss and stay up to date with recent developments.

Contact Peter Lippmann (peter.lippmann [at] for further details.

Next Seminar: 13.11.2023 in INF 205, SR 4.300 starting at 1:00pm
Paper to be discussed:

Transformers are efficient hierarchical chemical graph learners
Zihan Pengmei, Zimu Li, Chih-chan Tien, Risi Kondor, Aaron R. Dinner

Recently discussed papers:

Free-form Flows: Make Any Architecture a Normalizing Flow
Felix Draxler, Sorrenson, Peter Rangi, Rousselot, Armand Louis Amedee, Zimmermann, Lea, Ullrich Köthe

Nougat: Neural Optical Understanding for Academic Documents
Lukas Blecher, Guillem Cucurull, Thomas Scialom, Robert Stojnic

White-Box Transformers via Sparse Rate Reduction
Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Benjamin D. Haeffele, Yi Ma

Emergence of Segmentation with Minimalistic White-Box Transformers
Yaodong Yu, Tianzhe Chu, Shengbang Tong, Ziyang Wu, Druv Pai, Sam Buchanan, Yi Ma

Large Language Models as Optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, Xinyun Chen

PointLLM: Empowering Large Language Models to Understand Point Clouds
Runsen Xu, Xiaolong Wang, Tai Wang, Yilun Chen, Jiangmiao Pang, Dahua Lin

Loss of Plasticity in Deep Continual Learning
Shibhansh Dohare, J. Fernando Hernandez-Garcia, Parash Rahman, Richard S. Sutton, A. Rupam Mahmood

Tuning Computer Vision Models With Task Rewards
André Susano Pinto, Alexander Kolesnikov, Yuge Shi, Lucas Beyer, Xiaohua Zhai

Deep Learning on Implicit Neural Representations of Shapes
Luca De Luigi, Adriano Cardace, Riccardo Spezialetti, Pierluigi Zama Ramirez, Samuele Salti, Luigi Di Stefano

Equivariant Diffusion for Molecule Generation in 3D
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, Max Welling

Fourier Neural Operator for Parametric Partial Differential Equations
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar

Equivariant Architectures for Learning in Deep Weight Spaces
Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron

VectorAdam for Rotation Equivariant Geometry Optimization
Selena Ling, Nicholas Sharp, Alec Jacobson

Adding Conditional Control to Text-to-Image Diffusion Models
Lvmin Zhang and Maneesh Agrawala

DeepSea: An efficient deep learning model for single-cell segmentation and tracking of time-lapse microscopy images
Zargari, Abolfazl, et al.

Track Anything: Segment Anything Meets Videos
Jinyu Yang, Mingqi Gao, Zhe Li, Shang Gao, Fangjing Wang, Feng Zheng

Trans-Dimensional Generative Modeling via Jump Diffusion Models
Andrew Campbell, William Harvey, Christian Weilbach, Valentin De Bortoli, Tom Rainforth, Arnaud Doucet

Scaling Transformer to 1M tokens and beyond with RMT
Aydar Bulatov, Yuri Kuratov, Mikhail S. Burtsev

Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
Xingang Pan, Ayush Tewari, Thomas Leimkühler, Lingjie Liu, Abhimitra Meka, Christian Theobalt

Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability
Ziming Liu, Eric Gan, Max Tegmark

Supervised Training of Conditional Monge Maps
Charlotte Bunne, Andreas Krause, Marco Cuturi

Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
Emre Kıcıman, Robert Ness, Amit Sharma, Chenhao Tan

How Attentive are Graph Attention Networks?
Shaked Brody, Uri Alon, Eran Yahav

15.05.23 Sparks of Artificial General Intelligence: Early experiments with GPT-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan et al

08.05.23 Discrete Variational Autoencoders
Jason Tyler Rolfe

Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification
Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama

Image as Set of Points
Xu Ma, Yuqian Zhou, Huan Wang, Can Qin, Bin Sun, Chang Liu, Yun Fu

Advancing mathematics by guiding human intuition with AI
Davies, A., Veličković, P., Buesing, L., Blackwell, S., Zheng, D., Tomašev, N., ... & Kohli, P

DreamFusion: Text-to-3D using 2D Diffusion
Ben Poole, Ajay Jain, Jonathan T. Barron, Ben Mildenhall

20.03.23 Flow Matching for Generative Modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matt Le