International Conference Contributions
On this page, you will find the latest contributions from Computational Health Center researchers at international AI conferences:
Massimo Bini, Karsten Roth, Zeynep Akata, Anna Khoreva
ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections. arXiv
Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Ratsch, Vincent Fortuin
Improving Neural Additive Models with Bayesian Principles. arXiv
Theodore Papamarkou, Maria Skoularidou,Konstantina Palla, Laurence Aitchison, Julyan Arbel, David Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A Osborne, Tim G. J. Rudner
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. arXiv
Julian Coda-Forno, Marcel Binz, Jane X. Wang, Eric Schulz
CogBench: A large language model walks into a psychology lab. arXiv
Johannes A. Schubert, Akshay K. Jagadish, Marcel Binz, Eric Schulz
In-context learning agents are asymmetric belief updaters. arXiv
Akshay K. Jagadish, Julian Coda-Forno, Mirko Thalmann, Eric Schulz, Marcel Binz
Ecologically rational meta-learned inference explains human category learning. arXiv
Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Lio, Paolo Di Lorenzo,Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T Schaub, Petar Veličković, Bei Wang, Yusu Wang
Position: Topological Deep Learning is the New Frontier for Relational Learning. arXiv
Jeremy Wayland, Corinna Coupette, Bastian Rieck
Mapping the Multiverse of Latent Representations. arXiv
Georgios Kaissis, Stefan Kolek, Borja Balle, Jamie Hayes, Daniel Rueckert
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy. arXiv
Dominik Klein, Théo Uscidda, Fabian Theis, Marco Cuturi
GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics. arXiv
Artur Szałata, Andrew Benz, Robrecht Cannoodt, Mauricio Cortes, Jason Fong, Sunil Kuppasani, Richard Lieberman, Tianyu Liu, Javier Mas-Rosario, Rico Meinl, Jalil Nourisa, Jared Tumiel, Tin M. Tunjic, Mengbo Wang, Noah Weber, Hongyu Zhao, Benedict Anchang, Fabian Theis, Malte Luecken, Daniel Burkhardt
A Benchmark for Prediction of Transcriptomic Responses to Chemical Perturbations Across Cell Types.
Sirine Ayadi, Leon Hetzel, Johanna Sommer, Fabian Theis, Stephan Günnemann
Unified Guidance for Geometry-Conditioned Molecular Generation. arXiv
Tristan Cinquin, Marvin Pförtner, Vincent Fortuin, Philipp Hennig, Robert Bamler
FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning. arXiv
Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin
Shaving Weights with Occam’s Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood. arXiv
Karsten Roth, Vishaal Udandarao, Sebastian Dziadzio, Ameya Prabhu, Medhi Cherti, Oriol Vinyals, Olivier Henaff, Samuel Albanie, Matthias Bethge, Zeynep Akata
A Practitioner’s Guide to Continual Multimodal Pretraining. arXiv
Luca Eyring, Shyamgopal Karthik, Karsten Roth, Alexey Dosovitskiy, Zeynep Akata
ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization. arXiv
Elisabeth Ailer, Niclas Dern, Jason Hartford, Niki Kilbertus
Targeted Sequential Indirect Experiment Design. arXiv
Yashas Annadani, Panagiotis Tigas, Stefan Bauer, Adam Foster
Amortized Active Causal Induction with Deep Reinforcement Learning. arXiv
Christina Bukas, Harshavardhan Subramanian, Fenja See, Carina Steinchen, Ivan Ezhov, Gowtham Boosarpu, Sara Asgharpour, Gerald Burgstaller, Mareike Lehmann, Florian Kofler, Marie Piraud
MultiOrg: A Multi-rater Organoid-detection Dataset. arXiv
Can Demircan, Tankred Saanum, Leonardo Pettini, Marcel Binz, Blazej Baczkowski, Christian Doeller, Mona Garvert, Eric Schulz
Evaluating Alignment Between Humans and Neural Network Representations in Image Based Learning Tasks. arXiv
Thomas Altstidl, David Dobre, Arthur Kosmala, Bjoern Eskofier, Gauthier Gidel, Leo Schwinn
On the Scalability of Certified Adversarial Robustness with Generated Data.
Katharina Limbeck, Rayna Andreeva, Rik Sarkar, Bastian Rieck
Metric Space Magnitude for Evaluating the Diversity of Latent Representations. arXiv
