NeurIPS 2025PastOther
NeurIPS 2025 Workshop on Symmetry and Geometry in Neural Representations
NeurReps 2025
- Submission deadline
- Sep 11, 2025, 04:00 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (121)
Fetched from OpenReview (v2) on 2026-06-10.
A Comparative Empirical Study of Relative Embedding Alignment in Neural Dynamical System Forecasters
A Dendritic-Inspired Network Science Generative Model for Topological Initialization of Connectivity in Sparse Artificial Neural Networks
A New Perspective for Graph Learning Architecture Design: Linearize Your Depth Away
A Variational Manifold Embedding Framework for Nonlinear Dimensionality Reduction
Activation Matching for Explanation Generation and Circuit Discovery
Affect2Act: Graph Attention Networks for Emotion-Informed Decision Making
An Analytical Framework for Multi-Area Balanced Networks
An Information-Geometric View of the Platonic Hypothesis
Any-Subgroup Equivariant Networks via Symmetry Breaking
Balancing Fairness and Accuracy in Graph Learning via Fairness-Constrained Rewiring
Beyond I-Con: A Roadmap for Representation Learning Loss Discovery
Beyond Parallelism: Synergistic Computational Graph Effects in Multi-Head Attention
Beyond Pixels: A Differentiable Pipeline for Probing Neuronal Selectivity in 3D
Bispectral OT: Dataset Comparison using Symmetry-Aware Optimal Transport
Boundary Guidance for Efficient 3D CT Vision–Language Reasoning
Brain network science modelling of sparse neural networks enables Transformers and LLMs to perform as fully connected
Cannistraci-Hebb Training of Convolutional Neural Networks
CAP$_{\mathcal{M}}$ : Curvature-Aware Pulling on Riemannian Manifolds
Causal Geometry of Batch Size and Generalisation
Causality $\neq$ Decodability, and Vice Versa: Lessons from Interpreting Counting ViTs
Complete Characterization of Gauge Symmetries in Transformer Architectures
Composed Program Induction with Latent Program Lattice
Compositional Symmetry as Compression: Lie‑Pseudogroup Structure in Algorithmic Agents
Context-Dependent Manifold Learning in Dynamical Systems: A Neuromodulated Constrained Autoencoder Approach
Contrast inversion reveals hierarchical asymmetries of contrast processing in biological and artificial vision
Contrastive Learning with Latent Tension Regularization for Tight Orbits
Covering Relations in the Poset of Combinatorial Neural Codes
Curvature Dynamic Black-box Attack: revisiting adversarial robustness via dynamic curvature estimation
Curvature Estimation on Data Manifolds via Diffusion-augmented Sampling
Curvature Meets Bispectrum: A Correspondence Theory for Transformer Gauge Invariants
Data Augmentation: A Fourier Analysis Perspective
Deep neural network model of sound localization replicates “what” and “where” representations in auditory cortex
DIET-CP: Lightweight and Data Efficient Self Supervised Continued Pretraining
Dimensionality of population-level latent mechanisms encoding spatial representations
Do Masked Autoencoders Learn a Human-Like Geometry of Neural Representation? Divergence and Convergence Across Brains and Machines During Naturalistic Vision
Do traveling waves make good positional encodings?
Dual-Stream EEG Decoding for 3D Visual Perception
ECoNets: Rotation Equivariant Contrail Detection Neural Networks in Satellite Imagery
Emergent Riemannian geometry over learning discrete computations on continuous manifolds
Equivariance by Local Canonicalization: A Matter of Representation
Event2Vec: A Geometric Approach to Learning Composable Representations of Event Sequences
Exact Learning Dynamics of In-Context Learning in Linear Transformers and Its Application to Non-Linear Transformers
Exploring Learnability in Dynamical Stochastic Networks: A Field-Theoretic Approach
Factorized Prefrontal Geometry of Goal and Uncertainty Explains Flexible yet Stable Human Goal Pursuit
Far from the Shallow: Brain-Predictive Reasoning Embedding through Residual Disentanglement
Filter Equivariant Functions: A symmetric account of length-general extrapolation on lists
Flow Equivariant World Models: Structured Dynamics Outside the Field of View
From Extrapolation to Generalization: How Conditioning Transforms Symmetry Learning in Diffusion Models
From Finite to Infinite Groups: A Polynomial-Time Algorithm for Learning with Exact Invariances
Gauge Fiber Bundle Geometry of Transformers
Generalizable Representation Geometry for Grating Stimuli in Primary Visual Cortex and Artificial Neural Networks
Geometric Priors for Generalizable World Models via Vector Symbolic Architecture
Geometry matters: insights from Ollivier Ricci Curvature and Ricci Flow into representational alignment
Graph Mixing Additive Networks
Group Convolutional Self-Attention for Roto-Translation Equivariance in ViTs
Hilbert geometry of the symmetric positive-definite bicone
Homological Representation Learning for Molecular Graphs
How does training shape the Riemannian geometry of neural network representations?
Inferring dynamical features from neural data through joint learning of latents factors and weights
K-theoretic Persistent Cohomology
Koopman Autoencoders Learn Neural Representation Dynamics
Learning from Frustration: Torsor CNNs on Graphs
Learning rate collapse prevents training recurrent neural networks at scale
Learning representations on Lp hyperspheres: The equivalence of loss functions in a MAP approach
LFMA: Parameter-Efficient Fine-Tuning via Layerwise Fourier Masked Adapter with Top-k Frequency Selection
Local Predictions, Global Learning: Radial Basis Function Networks for Spatially-Aware Predictive Coding
Logit-Based Losses Limit the Effectiveness of Feature Knowledge Distillation
Mapping neural representations of topologically non-trivial spaces
MAPS: A Dataset for Controlled Probing of Representational Topology in Vision Models
Measure Before You Look: Grounding Embeddings Through Manifold Metrics
Measuring and Controlling Solution Degeneracy across Task-Trained Recurrent Neural Networks
Meta-learning three-factor plasticity rules for structured credit assignment with sparse feedback
Mixed Monotonicity Reachability Analysis of Neural ODE: A Trade-Off Between Tightness and Efficiency
Model manifold analysis suggests the human visual brain is less like an optimal classifier and more like a feature bank
Model Transferability Informed by Embedding’s Topology
Modeling Human Vision with Differential Geometry
Neural Fields Meet Attention
Neural Manifold Geometry Encodes Feature Fields
Neurosymbolic Rabbit Brain: Fractal Attractor Geometry for Neural Representations
On a Geometry of Interbrain Networks
On neural circuits of working memory sequence permutation: optimizing circuit architectures via Cayley graphs
On the geometry of recurrent spiking networks
On the Impact of Topological Regularization on Geometrical and Topological Alignment in Autoencoders: An Empirical Study
On Uncertainty Calibration for Invariant Functions
Persistent Homology Distances for Comparing Disease-Filtered Structural Connectomes
Poisson-Algebraic Parallel Scan: A Fast Symplectic Framework for Neural Hamiltonians
Provable Low-Frequency Bias of In-Context Learning of Representations
Quantifying information stored in synaptic connections rather than in firing activities of neural networks
Radial-VCReg: More Informative Representation Learning Through Radial Gaussianization
REM3DI: Learning smooth, chiral 3D molecular representations from equivariant atomistic foundation models
Representational Homomorphism Error Predicts Compositional Generalization In Language Models
Response Patterns to Rotation Angle in a Rotation Pretext Task Vary Across Datasets and Architectures: An Observation and a Negative Result
Saliency Thresholds in Neural Code and its Relation to the Power-Law, Gaussian, and Lambert W Function
Sample Efficient Offline RL via T-symmetry Enforced Latent State-Stitching
Scalable GPU-Accelerated Euler Characteristic Curves: Optimization and Differentiable Learning for PyTorch
Self-Supervised Learning from Structural Invariance
Shape-Based Features Complement CLIP Features and Features Learned from Voxels in 3D Object Classification
Shaping Latent Geometry with Noise-Injected Hopfield Dynamics
Sheaf Cohomology of Linear Predictive Coding Networks
Slow Transition to Low-Dimensional Chaos in Heavy-Tailed Recurrent Neural Networks
SRTD: A Symmetric Divergence for Interpretable Comparison of Representation Topology
Symmetry as Intervention; Causal Estimation with Data Augmentation
Symmetry-Regularized Learning of Continuous Attractor Dynamics
The Binding Problem in Vision Models: Geometric, Functional, and Behavioral Approaches
The Cue or not the Cue? A Mechanistic Study of Memory Mechanisms in RNNs
The Geometry and Topology of Modular Addition Representations
The Geometry of Cortical Computation: Manifold Disentanglement and Predictive Dynamics in VCNet
The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm
The Human Brain as a Combinatorial Complex
The Representations of Deep Neural Networks Trained on Dihedral Group Multiplication
Theoretical Analysis of HyperCube Objective for Group Representation Learning
Time-Resolved Circuit Discovery in RNNs via Windowed Causal Interventions and Local Linearization
Topological Neural Data Analysis with Behavioral Constraint
Topological Signatures of Altered Brain Network Centrality in ADHD: A TDA Mapper Study
Towards the Identification of Latent Structures in Language Embeddings
Tracking Memorization Geometry throughout the Diffusion Model Generative Process
Transformers Represent Causal Abstractions
Unified Generative Latent Representation for Functional Brain Graphs
Unifying Global Topology Manifolds and Local Persistent Homology for Data Pruning
Unifying Regression and Uncertainty Quantification with Contrastive Spectral Representation Learning
Why all roads don’t lead to Rome: Representation geometry varies across the human visual cortical hierarchy