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] iwr.uni-heidelberg.de) 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
https://arxiv.org/abs/2310.01704
Recently discussed papers:
06.11.23
Free-form Flows: Make Any Architecture a Normalizing Flow
Felix Draxler, Sorrenson, Peter Rangi, Rousselot, Armand Louis Amedee, Zimmermann, Lea, Ullrich Köthe
https://arxiv.org/abs/2310.16624
30.10.23
Nougat: Neural Optical Understanding for Academic Documents
Lukas Blecher, Guillem Cucurull, Thomas Scialom, Robert Stojnic
https://arxiv.org/abs/2308.13418
23.10.23
White-Box Transformers via Sparse Rate Reduction
Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Benjamin D. Haeffele, Yi Ma
https://arxiv.org/abs/2306.01129
16.10.23
Emergence of Segmentation with Minimalistic White-Box Transformers
Yaodong Yu, Tianzhe Chu, Shengbang Tong, Ziyang Wu, Druv Pai, Sam Buchanan, Yi Ma
https://arxiv.org/abs/2308.16271
09.10.23
Large Language Models as Optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, Xinyun Chen
https://arxiv.org/abs/2309.03409
02.10.23
PointLLM: Empowering Large Language Models to Understand Point Clouds
Runsen Xu, Xiaolong Wang, Tai Wang, Yilun Chen, Jiangmiao Pang, Dahua Lin
https://arxiv.org/abs/2308.16911
25.09.23
Loss of Plasticity in Deep Continual Learning
Shibhansh Dohare, J. Fernando Hernandez-Garcia, Parash Rahman, Richard S. Sutton, A. Rupam Mahmood
https://arxiv.org/abs/2306.13812
18.09.23
Tuning Computer Vision Models With Task Rewards
André Susano Pinto, Alexander Kolesnikov, Yuge Shi, Lucas Beyer, Xiaohua Zhai
https://openreview.net/forum?id=zzOooeAqtT
11.09.23
Deep Learning on Implicit Neural Representations of Shapes
Luca De Luigi, Adriano Cardace, Riccardo Spezialetti, Pierluigi Zama Ramirez, Samuele Salti, Luigi Di Stefano
https://arxiv.org/abs/2302.05438
28.08.23
Equivariant Diffusion for Molecule Generation in 3D
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, Max Welling
https://arxiv.org/abs/2203.17003
21.08.23
Fourier Neural Operator for Parametric Partial Differential Equations
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar
https://arxiv.org/abs/2010.08895
14.08.23
Equivariant Architectures for Learning in Deep Weight Spaces
Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron
https://openreview.net/forum?id=SCU1xlr9Y4
07.08.23
VectorAdam for Rotation Equivariant Geometry Optimization
Selena Ling, Nicholas Sharp, Alec Jacobson
https://openreview.net/forum?id=df1g_KeEjQ
31.07.23
Adding Conditional Control to Text-to-Image Diffusion Models
Lvmin Zhang and Maneesh Agrawala
https://arxiv.org/pdf/2302.05543.pdf
24.07.23
DeepSea: An efficient deep learning model for single-cell segmentation and tracking of time-lapse microscopy images
Zargari, Abolfazl, et al.
https://www.biorxiv.org/content/10.1101/2021.03.10.434806v2.abstract
17.07.23
Track Anything: Segment Anything Meets Videos
Jinyu Yang, Mingqi Gao, Zhe Li, Shang Gao, Fangjing Wang, Feng Zheng
https://arxiv.org/abs/2304.11968
10.07.23
Trans-Dimensional Generative Modeling via Jump Diffusion Models
Andrew Campbell, William Harvey, Christian Weilbach, Valentin De Bortoli, Tom Rainforth, Arnaud Doucet
https://arxiv.org/abs/2305.16261
03.07.23
Scaling Transformer to 1M tokens and beyond with RMT
Aydar Bulatov, Yuri Kuratov, Mikhail S. Burtsev
https://arxiv.org/abs/2304.11062
26.06.23
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
Xingang Pan, Ayush Tewari, Thomas Leimkühler, Lingjie Liu, Abhimitra Meka, Christian Theobalt
https://arxiv.org/abs/2305.10973
19.06.23
Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability
Ziming Liu, Eric Gan, Max Tegmark
https://arxiv.org/abs/2305.08746
12.06.23
Supervised Training of Conditional Monge Maps
Charlotte Bunne, Andreas Krause, Marco Cuturi
https://arxiv.org/abs/2206.14262
05.06.23
Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
Emre Kıcıman, Robert Ness, Amit Sharma, Chenhao Tan
https://arxiv.org/abs/2305.00050
22.05.23
How Attentive are Graph Attention Networks?
Shaked Brody, Uri Alon, Eran Yahav
https://arxiv.org/abs/2105.14491
15.05.23
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan et al
https://arxiv.org/abs/2303.12712
08.05.23
Discrete Variational Autoencoders
Jason Tyler Rolfe
https://arxiv.org/abs/1609.02200
24.04.23
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
https://openreview.net/forum?id=FZdJQgy05rz
17.04.23
Image as Set of Points
Xu Ma, Yuqian Zhou, Huan Wang, Can Qin, Bin Sun, Chang Liu, Yun Fu
https://openreview.net/forum?id=awnvqZja69
03.04.23
Advancing mathematics by guiding human intuition with AI
Davies, A., Veličković, P., Buesing, L., Blackwell, S., Zheng, D., Tomašev, N., ... & Kohli, P
https://www.nature.com/articles/s41586-021-04086-x
27.03.23
DreamFusion: Text-to-3D using 2D Diffusion
Ben Poole, Ajay Jain, Jonathan T. Barron, Ben Mildenhall
https://openreview.net/forum?id=FjNys5c7VyY
20.03.23
Flow Matching for Generative Modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matt Le
https://arxiv.org/pdf/2210.02747.pdf