newsitem: NeurIPS 2024
We’re happy to present the following works at NeurIPS 2024:
Main track
[Spotlight] Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits
Gennaro Gala, Cassio de Campos, Antonio Vergari, Erik Quaeghebeur
DeiSAM: Segment Anything with Deictic Prompting
Hikaru Shindo, Manuel Brack, Gopika Sudhakaran, Devendra Singh Dhami, Patrick Schramowski, Kristian Kersting
Graph Neural Networks Need Cluster-Normalize-Activate Modules
Arseny Skryagin, Felix Divo, Mohammad Amin Ali,Devendra Singh Dhami, Kristian Kersting
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficien 3D Medical Image Segmentation
Boqian Wu*, Qiao Xiao*, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Decebal Constantin Mocanu, Maurice van Keulen, Elena Mocanu
Dynamic Neural Regeneration: Enhancing Deep Learning Generalization on Small Datasets
Vijaya Raghavan T Ramkumar, Elahe Arani, Bahram Zonooz
Tracks and Workshops
Workshop on System-2 Reasoning at Scale
Bongard in Wonderland: Visual Puzzles that Still Make AI Go Mad?
Antonia Wüst, Tim Tobiasch, Lukas Helff, Devendra Singh Dhami, Constantin A. Rothkopf, Kristian Kersting
Datasets and Benchmarks Track
[Spotlight] Croissant: A Metadata Format for ML-Ready Datasets
Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Luca Foschini, Joan Giner-Miguelez, Pieter Gijsbers, Sujata Goswami, Nitisha Jain, Michalis Karamousadakis, Michael Kuchnik, Satyapriya Krishna, Sylvain Lesage, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Hamidah Oderinwale, Pierre Ruyssen, Tim Santos, Rajat Shinde, Elena Simperl, Arjun Suresh, Goeff Thomas, Slava Tykhonov, Joaquin Vanschoren, Susheel Varma, Jos van der Velde, Steffen Vogler, Carole-Jean Wu, Luyao Zhang
Causal Representation Learning Workshop
Systems with Switching Causal Relations: A Meta-Causal Perspective
Moritz Willig, Tim Tobiasch, Florian Peter Busch, Jonas Seng, Devendra Singh Dhami, Kristian Kersting
On the role of prognostic factors and effect modifiers in structural causal models
Rianne M. Schouten
Workshop on Scientific Methods for Understanding Deep Learning
Rethinking Knowledge Transfer in Learning Using Privileged Information
Danil Provodin, Bram Van Den Akker, Christina Katsimerou, Maurits Kaptein, Mykola Pechenizkiy
Workshop on Data-driven and Differentiable Simulations, Surrogates, and Solvers (D3S3)
Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scale
Pol Timmer, Koen Minartz, Vlado Menkovski
Workshop on Socially Responsible Language Modelling Research
On Adversarial Robustness of Language Models in Transfer Learning
Bohdan Turbal, Anastasiia Mazur, Jiaxu Zhao, Mykola Pechenizkiy
Workshop on Machine Learning and Compression
Graph Transformation Augmentation for Contrastive Learning of Graph-Level Representation: An Initial Exploration
Tianchao Li, Yulong Pei