NeurIPS 2024 Past Other
NeurIPS 2024 Workshop on Symmetry and Geometry in Neural Representations
NeurReps 2024
- Submission deadline
- Sep 24, 2024, 12:00 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (61)
Fetched from OpenReview (v2) on 2026-06-10.
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A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing
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A minimalistic representation model for head direction system
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A New Geometric Approach of Adaptive Neighborhood Selection for Classification
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Adversarially-robust representation learning through spectral regularization of features
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An Information Parsimony Perspective on Probabilistic Symmetries
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BiEquiFormer: Bi-Equivariant Representations for Global Point Cloud Registration
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CantorNet: A Sandbox for Testing Topological and Geometrical Measures
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Certifying Robustness via Topological Representations
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Communication subspaces align with training in ANNs
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Connecting Neural Models Latent Geometries with Relative Geodesic Representations
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Constrained Belief Updating and Geometric Structures in Transformer Representations
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Convergence of Manifold Filter-Combine Networks
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Counterfactual Explanations via Riemannian Latent Space Traversal
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Does equivariance matter at scale?
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Does Maximizing Neural Regression Scores Teach Us About The Brain?
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Dynamical symmetries in the fluctuation-driven regime: an application of Noether's theorem to noisy dynamical systems
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Efficient Subgraph GNNs via Graph Products and Coarsening
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Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identification
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EqNIO: Subequivariant Neural Inertial Odometry
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Exploring Geometric Representational Alignment through Ollivier-Ricci Curvature and Ricci Flow
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Galois features: Nearly-complete invariants on symmetric matrices
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Geometric Machine Learning on EEG Signals
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Geometric Signatures of Compositionality Across a Language Model’s Lifetime
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Graph Neural Networks Uncover Geometric Neural Representations in Reinforcement-Based Motor Learning
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Hamiltonian Matching for Symplectic Neural Integrators
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Harmformer: Harmonic Networks Meet Transformers for Continuous Roto-Translation Equivariance
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Hidden Holes - topological aspects of language models
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Improving Deep Learning Speed and Performance through Synaptic Neural Balance
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In-Context Symmetries: Self-Supervised Learning through Contextual World Models
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Invariant Graphon Networks: Approximation and Cut Distance
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Klein Model for Hyperbolic Neural Networks
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Knowledge Distillation for Teaching Symmetry Invariances
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Learning Effective NeRFs and SDFs Representations with 3D GANs for Object Generation
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Learning Symmetric Contexts for Anomaly Detection
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Leveraging Symmetry to Accelerate Learning of Trajectory Tracking Controllers for Free-Flying Robotic Systems
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ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose Estimation
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MNIST-Nd: a set of naturalistic datasets to benchmark clustering across dimensions
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Modeling dynamic neural activity by combining naturalistic video stimuli and stimulus-independent latent factors
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Multi-task Learning yields Disentangled World Models: Impact and Implications
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Neural Network Symmetrisation in Concrete Settings
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Neural Representational Geometry of Concepts in Large Language Models
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On Layer-wise Representation Similarity: Application for Multi-Exit Models with a Single Classifier
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On Optimal Lifting to SE(2) in Equivariant Neural Networks
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On the Reconstruction of Training Data from Group Invariant Networks
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On the Ricci Curvature of Attention Maps and Transformers Training and Robustness
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Probabilistic Nested Homogeneous Spaces for Dimensionality Reduction
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Range-aware Positional Encoding via High-order Pretraining: Theory and Practice
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RelWire: Metric Based Rewiring
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Rethinking Message Passing for Algorithmic Alignment
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sa-SVAE: a Shared and Aligned Structured Variational Autoencoder for Extracting Behaviorally Relevant and Preserved Neural Dynamics Across Animals
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Storing overlapping associative memories on latent manifolds in low-rank spiking networks
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Structure Development in List Sorting Transformers
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Structure Matters: Deciphering Neural Network's Properties from its Structure
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Structured In-Context Task Representations
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Supervised Quadratic Feature Analysis: An information geometry approach to dimensionality reduction
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Symmetry-Aware Generative Modeling through Learned Canonicalization
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Theoretical Insights into Line Graph Transformation on Graph Learning
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Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity
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Toward Understanding How the Data Affects Neural Collapse: A Kernel-Based Approach
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Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs
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Visualizing Loss Functions as Topological Landscape Profiles