ICML 2024PastGenerative models
ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling
ICML 2024 Workshop GRaM
- Submission deadline
- Jun 3, 2024, 20:00 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (82)
Fetched from OpenReview (v2) on 2026-06-10.
(Deep) Generative Geodesics
3D Shape Completion with Test-Time Training
A Coding-Theoretic Analysis of Hyperspherical Prototypical Learning Geometry
A Geometric Framework for Understanding Memorization in Generative Models
A Simple and Expressive Graph Neural Network Based Method for Structural Link Representation
A Theoretical Formulation of Many-body Message Passing Neural Networks
Adaptive Sampling for Continuous Group Equivariant Neural Networks
Aligned Diffusion Models for Retrosynthesis
Alignment of MPNNs and Graph Transformers
All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models
An Equivariant Flow Matching Framework for Learning Molecular Crystallization
Approximate natural gradient in Gaussian processes with non-log-concave likelihoods
Asynchrony Invariance Loss Functions for Graph Neural Networks
Bias-inducing geometries: exactly solvable data model with fairness implications
Bundle Neural Networks for message diffusion on graphs
Commute-Time-Optimised Graphs for GNNs
Consistency models with learned idempotent boundary conditions
Constructing gauge-invariant neural networks for scientific applications
CoordConformer: Heterogenous EEG datasets decoding using Transformers
Decoder ensembling for learned latent geometries
Decomposed Linear Dynamical Systems (dLDS) for identifying the latent dynamics underlying high-dimensional time-series
Dirac--Bianconi Graph Neural Networks - Enabling long-range graph predictions
E(n) Equivariant Message Passing Cellular Networks
Energy-based Hopfield Boosting for Out-of-Distribution Detection
Equivariant vs. Invariant Layers: A Comparison of Backbone and Pooling for Point Cloud Classification
Gaussian Process-Based Representation Learning via Timeseries Symmetries
Geometric algebra transformers for large 3D meshes via cross-attention
Geometric Wireless Simulation with Equivariant Transformers
Geometry Aware Deep Learning for Integrated Closed-shell and Open-shell Systems
Geometry Fidelity for Spherical Images
Geometry-Aware Autoencoders for Metric Learning and Generative Modeling on Data Manifolds
Geometry-informed Neural Networks
GLAudio Listens to the Sound of the Graph
Graph Convolutional Networks for Learning Laplace-Beltrami Operators
Improving Equivariant Networks with Probabilistic Symmetry Breaking
InfoNCE: Identifying the Gap Between Theory and Practice
Invertible Temper Modeling using Normalizing Flows and the Effects of Structure Preserving Loss
Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networks
Latent functional maps
Learning Diffeomorphic Lyapunov Functions from Data
Learning symmetries via weight-sharing with doubly stochastic tensors
Leveraging Topological Guidance for Improved Knowledge Distillation
Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space
Lorentzian Residual Neural Networks
Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design
Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold
Metric Learning for Clifford Group Equivariant Neural Networks
Mixed-Curvature Decision Trees and Random Forests
Multivector Neurons: Better and Faster O(n)-Equivariant Clifford GNNs
On Fairly Comparing Group Equivariant Networks
On The Local Geometry of Deep Generative Manifolds
On the Matter of Embeddings Dispersion on Hyperspheres
Path Complex Neural Network for Molecular Property Prediction
Permutation Tree Invariant Neural Architectures
Probabilistic World Modeling with Asymmetric Distance Measure
Relaxed Equivariant Graph Neural Networks
Revisiting Random Walks for Learning on Graphs
RIO-CPD: A Riemannian Geometric Method for Correlation-aware Online Change Point Detection
Scalable Local Intrinsic Dimension Estimation with Diffusion Models
SCENE-Net V2: Interpretable Multiclass 3D Scene Understanding with Geometric Priors
SE(3)-Hyena Operator for Scalable Equivariant Learning
SE3ET: SE(3)-Equivariant Transformer for Low-Overlap Point Cloud Registration
Self-Supervised Detection of Perfect and Partial Input-Dependent Symmetries
Sheaf Diffusion Goes Nonlinear: Enhancing GNNs with Adaptive Sheaf Laplacians
SINR: Equivariant Neural Vector Fields
Stability Analysis of Equivariant Convolutional Representations Through The Lens of Equivariant Multi-layered CKNs
Stitching Manifolds: Leveraging Interaction to Compose Object Representations into Scenes.
Strongly Isomorphic Neural Optimal Transport Across Incomparable Spaces
Temporal Graph Rewiring with Expander Graphs
The Geometry of Diffusion Models: Tubular Neighbourhoods and Singularities
The NGT200 Dataset - Geometric Multi-View Isolated Sign Recognition
The Price of Freedom: Exploring Tradeoffs between Expressivity and Computational Efficiency in Equivariant Tensor Products
Theoretical Analyses of Hyperparameter Selection in Graph-Based Semi-Supervised Learning
Topological and Dynamical Representations for Radio Frequency Signal Classification
Topology-Informed Graph Transformer
Towards General Geometries for Embedding Knowledge Graphs
Transferability for Graph Convolutional Networks
UHCone: Universal Hyperbolic Cone For Implicit Hierarchical Learning
Understanding Hallucinations in Diffusion Models through Mode Interpolation
Unsupervised Ground Metric Learning with Tree Wasserstein Distance
Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks
What Makes a Machine Learning Task a Good Candidate for an Equivariant Network?