17.10.2024
44 Paper bei NeurIPS 2024 akzeptiert
Bei der diesjährigen NeurIPS-Konferenz wurden 44 Beiträge von Forschenden unseres Exzellenzclusters akzeptiert.
Die 38. Konferenz zu Neural Information Processing Systems (NeurIPS) findet am Vancouver Convention Center in Kanada vom 10. - 15. Dezember 2024 statt. NeurIPS ist die größte Konferenz für Maschinelles Lernen und Computational Neuroscience. Ziel der jährlichen Treffen ist es, den Forschungsaustausch zu neuronalen Informationsverarbeitungssysteme in ihren biologischen, technologischen, mathematischen und theoretischen Aspekten zu fördern. Der Schwerpunkt liegt auf peer-reviewed, neuartigen Forschungsarbeiten, die in einer allgemeinen Session vorgestellt und diskutiert werden, sowie auf eingeladenen Vorträgen von ausgewiesenen Experten.
In diesem Jahr ist unser Cluster mit 44 Papern auf der NeurIPS vertreten.
Liste der akzeptierten Beiträge unserer Mitglieder (alle Beiträge sind hier zu finden):
- Tristan Cinquin, Marvin Pförtner, Vincent Fortuin, Philipp Hennig, Robert Bamler
FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning - Sebastian Damrich, Philipp Berens, Dmitry Kobak
Persistent Homology for High-dimensional Data Based on Spectral Methods - Karsten Roth, Vishaal Udandarao, Sebastian Dziadzio, Ameya Prabhu, Mehdi Cherti, Oriol Vinyals, Olivier Henaff, Samuel Albanie, Matthias Bethge, Zeynep Akata
A Practitioner's Guide to Real-World Continual Multimodal Pretraining - Vishaal Udandarao, Ameya Prabhu, Adhiraj Ghosh, Yash Sharma, Philip Torr, Adel Bibi, Samuel Albanie, Matthias Bethge
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance - Ori Press, Andreas Hochlehnert, Ameya Prabhu, Vishaal Udandarao, Ofir Press, Matthias Bethge
CiteME: Can Language Models Accurately Cite Scientific Claims? - Matthias Tangemann, Matthias Kümmerer, Matthias Bethge
Object segmentation from common fate: Motion energy processing enables human-like zero-shot generalization to random dot stimuli - Ameya Prabhu, Vishaal Udandarao, Philip Torr, Matthias Bethge, Adel Bibi, Samuel Albanie
Efficient Lifelong Model Evaluation in an Era of Rapid Progress - Anna Mészáros, Patrik Reizinger, Szilvia Ujváry, Wieland Brendel, Ferenc Huszar
Rule Extrapolation in Language Modeling: A Study of Compositional Generalization on OOD Prompts - Roland S. Zimmermann, David Klindt, Wieland Brendel
Measuring Per-Unit Interpretability at Scale Without Humans - Tankred Saanum, Peter Dayan, Eric Schulz
Simplifying Latent Dynamics with Softly State-Invariant World Models - Jack Merullo, Carsten Eickhoff, Ellie Pavlick
Talking Heads: Understanding Inter-Layer Communication in Transformer Language Models - Haiwen Huang, Songyou Peng, Dan Zhang, Andreas Geiger
Renovating Names in Open-Vocabulary Segmentation Benchmarks - Shenyuan Gao, Jiazhi Yang, Li Chen, Kashyap Chitta, Yihang Qiu, Andreas Geiger, Jun Zhang, Hongyang Li
Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability - Daniel Dauner, Marcel Hallgarten, Tianyu Li, Xinshuo Weng, Zhiyu Huang, Zetong Yang, Hongyang Li, Igor Gilitschenski, Boris Ivanovic, Marco Pavone, Andreas Geiger, Kashyap Chitta
NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking - Alexander Lappe, Anna Bognár, Ghazaleh Ghamkahri Nejad, Albert Mukovskiy, Lucas Martini, Martin Giese, Rufin Vogels
Parallel Backpropagation for Shared-Feature Visualization - Vivian Nastl, Moritz Hardt
Do causal predictors generalize better to new domains? - Ricardo Dominguez-Olmedo, Moritz Hardt, Celestine Mendler-Dünner
Questioning the Survey Responses of Large Language Models - Celestine Mendler-Dünner, Gabriele Carovano, Moritz Hardt
An engine not a camera: Measuring performative power of online search - André F. Cruz, Celestine Mendler-Dünner, Moritz Hardt
Evaluate calibration of language models with folktexts - Hrittik Roy, Marco Miani, Carl Henrik Ek, Philipp Hennig, Marvin Pförtner, Lukas Tatzel, Søren Hauberg
Reparameterization invariance in approximate Bayesian inference - Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob Gardner, Geoff Pleiss, John Cunningham
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference - Bálint Mucsányi, Michael Kirchhof, Seong Joon Oh
Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks - Irene Huang, Wei Lin, Muhammad Mirza, Jacob Hansen, Sivan Doveh, Victor Butoi, Roei Herzig, Assaf Arbelle, Hilde Kuehne, Trevor Darrell, Chuang Gan, Aude Oliva, Rogerio Feris, Leonid Karlinsky
ConMe: Rethinking Evaluation of Compositional Reasoning for Modern VLMs - Felix Petersen, Christian Borgelt, Tobias Sutter, Hilde Kuehne, Oliver Deussen, Stefano Ermon
Fishers and Hessians of Continuous Relaxations - Felix Petersen, Hilde Kuehne, Christian Borgelt, Julian Welzel, Stefano Ermon
Convolutional Differentiable Logic Gate Networks - Jan-Niklas Dihlmann, Arjun Majumdar, Andreas Engelhardt, Raphael Braun, Hendrik PA Lensch
Subsurface Scattering for Gaussian Splatting - Robi Bhattacharjee, Ulrike Luxburg
Auditing Local Explanations is Hard - Julius Vetter, Guy Moss, Cornelius Schröder, Richard Gao, Jakob H Macke
Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation - Matthijs Pals, A Erdem Sağtekin, Felix Pei, Manuel Gloeckler, Jakob H Macke
Inferring stochastic low-rank recurrent neural networks from neural data - Jaivardhan Kapoor, Auguste Schulz, Julius Vetter, Felix Pei, Richard Gao, Jakob H Macke
Latent Diffusion for Neural Spiking Data - Joachim Baumann, Celestine Mendler-Dünner
Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists - István Sárándi, Gerard Pons-Moll
Neural Localization Fields for Continuous 3D Human Pose and Shape Estimation - Yuxuan Xue, Xianghui Xie, Riccardo Marin, Gerard Pons-Moll
Human 3Diffusion: Realistic Avatar Creation via Explicit 3D Consistent Diffusion Models - Luca Eyring, Shyamgopal Karthik, Karsten Roth, Alexey Dosovitskiy, Zeynep Akata
ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization - Ahmad-Reza Ehyaei, Golnoosh Farnadi, Samira Samadi
Wasserstein Distributionally Robust Optimization through the Lens of Structural Causal Models and Individual Fairness - Mohammad-Amin Charusaie, Samira Samadi
A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems - 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 - Siyuan Guo, Chi Zhang, Karthika Mohan, Ferenc Huszar, Bernhard Schölkopf
Do Finetti: On Causal Effects for Exchangeable Data - Carl-Johann Simon-Gabriel, Alessandro Barp, Bernhard Schölkopf, Lester Mackey
Metrizing Weak Convergence with Maximum Mean Discrepancies - Giorgio Piatti, Zhijing Jin, Max Kleiman-Weiner, Bernhard Schölkopf, Mrinmaya Sachan, Rada Mihalcea
Cooperate or Collapse: Emergence of Sustainability in a Society of LLM Agents - Robin Chan, Reda Boumasmoud, Anej Svete, Yuxin Ren, Qipeng Guo, Zhijing Jin, Shauli Ravfogel, Mrinmaya Sachan, Bernhard Schölkopf, Mennatallah El-Assady, Ryan Cotterell
On Affine Homotopy between Language Encoders - Sergio Garrido Mejia, Patrick Blöbaum, Bernhard Schölkopf, Dominik Janzing
Causal vs. Anticausal merging of predictors - Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
From Causal to Concept-Based Representation Learning - Jonathan Thomm, Aleksandar Terzic, Giacomo Camposampiero, Michael Hersche, Bernhard Schölkopf, Abbas Rahimi
Limits of Transformer Language Models on Learning to Compose Algorithms