Neural Network Representations

Follow this topic Ask about this topic

Momentum

1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

Jul 6Week of Sep 21

Latest papers 33

All topics

Higher-Order Positional Encodings for Graph Representation Learning
Oct 1, 2026Caleb Stam, Aagrim Hoysal, Sanjukta KrishnagopalGraph Representation Learning Positional Encoding
University of California, Santa Barbara
Learning Continuous Neural Representation of Stochastic Hybrid Systems
Sep 30, 2026Sangli Teng, Hang Liu, Koushil SreenathStochastic Differential Equations Latent Dynamics
Minkowski Attractor Networks: Closed-Form Hyperbolic Flows for Visual Representations
Sep 29, 2026Zhongping JiHyperbolic Learning Hyperbolic Space
Early Learning Shapes Later Directions Of Representation Change In Continual Learning
Sep 28, 2026Yuantao Deng, Jinnuo Liu, Kaizhen Tan +1Neural Network Representations Continual Learning
New York University · Xi’an Jiaotong University
Building Transformation Layers for Riemannian Neural Networks
Sep 28, 2026Ziheng ChenRiemannian Manifolds Neural Network Representations
University of Trento · MPI for Intelligent Systems, Tübingen
Tensor network representations of discrete maximum entropy distributions via mean polytopes
Sep 7, 2026Alex Goessmann, Martin EigelTensor Networks Polytopes
Weierstrass Institute of Applied Analysis and Stochastics Anton-Wilhelm-Amo-Straße 39 Berlin, 10117, Germany
Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks
Aug 12, 2026Andrew Cheng, Ali Eslamian, Jie Cheng +2Neural Network Representations Reparameterization
Department of Computer Science, Tsinghua University, Beijing, China · Department of Computer Science, The University of Manchester, Manchester, UK · Department of Computer Science, University of Kentucky, Lexington, KY, USA +3
HYDRA: Hyperbolic Dynamic Representation Architecture for Kolmogorov-Arnold Networks
Aug 12, 2026Zhao Su, Yuxin Xia, Haoran Li +4Kolmogorov-Arnold Networks Hyperbolic Learning
School of Information Science and Engineering, Lanzhou University · Department of Data Science and AI, Monash University · School of Computing and Information Technology, University of Wollongong +1
Iterative Erasure Count Is Not an Affine-Invariant Concept Dimension
Aug 11, 2026Tingan Jin, Shuhang Dong, Haosong Li +1Intrinsic Dimensionality Concept Erasure
UCLA · Independent Researcher · Cal Poly Pomona
How to Tame Grokking: Representation Geometry as a Control Signal
Jul 13, 2026Maksim A KazanskiiGrokking Neural Network Representations
Independent Researcher
Enhancing Query Efficiency for d-DNNF Representations Through Preprocessing
Jul 13, 2026Jean Marie Lagniez, Emmanuel LoncaPreprocessing Efficient Planning
CRIL, U. Artois & CNRS, F-62300 Lens, France
Deep Neural Variation Spaces: A Unifying Perspective on Depth and Complexity
Jul 6, 2026Julia Nakhleh, Robert D. NowakRectified Linear Unit Networks Deep Network
Department of Computer Science · University of Wisconsin-Madison · Department of Electrical & Computer Engineering
Geometric and Information Compression of Representations in Deep Learning
Jun 19, 2026Linara Adilova, Henning Petzka, Asja Fischer +1Information Bottleneck Mutual Information
Research Center Trustworthy Data Science and Security, Dortmund · Faculty of Computer Science, Ruhr University Bochum · Signal Processing and Speech Communication Laboratory, Graz University of Technology +2
From Content to Knowledge: Lightning Fast Long-Video Understanding with Neural Knowledge Representations
Jun 10, 2026Yuchen Guan, Xiao Li, Zongyu Guo +4Video Understanding Frozen Vision-Language Models
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · Microsoft Research Asia
Symmetric Divergence and Normalized Similarity: A Unified Topological Framework for Representation Analysis
Jun 4, 2026Yan Wang, Tianyang HuTopology Representational Similarity Analysis
School of Data Science The Chinese University of Hong Kong, Shenzhen
Shortcomings and capacities of real-constrained neural networks in complex spaces
Jun 3, 2026Andrew GracykNeural Approximations Kernel Hilbert Spaces
Department of Mathematics Purdue University West Lafayette, IN 47907, United States
Neural Networks Provably Learn Spectral Representations for Group Composition
Jun 2, 2026Jianliang He, Leda Wang, Fengzhuo Zhang +2Neural Network Representations Neural Network
Department of Statistics and Data Science, Yale University
Learning in Low-Dimensional Subspaces: Orthogonal Bottlenecks for Reinforcement Learning
May 25, 2026Aleksandar Todorov, Matthia SabatelliNeural Network Representations Subspace
University of Groningen, Groningen, The Netherlands
Representability-Aware Neural Networks for Reduced Density Matrices: Application to Fractional Chern Insulators
May 19, 2026Justin B. Hart, Awwab A. Azam, Thomas Li +4Quantum Phases Many-Body Physics
Department of Physics, University of Florida, Gainesville, FL, USA · Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA · Palo Alto High School, Palo Alto, CA, USA +4
PySIFT: GPU-Resident Deterministic SIFT for Deep Learning Vision Pipelines
May 18, 2026Sivakumar K. S., Mohammad Daniyalur Rahman, Gopi Raju MattaHarder Better Faster Denser Feature Matching Keypoint Detection
1Indian Institute of Technology Madras
From Layers to Networks: Comparing Neural Representations via Diffusion Geometry
May 15, 2026Atharva Khandait, Jan E. GerkenNeural Network Representations Neural Representations
Department of Mathematical Sciences, Chalmers University of Technology and the University of Gothenburg, SE-412 96 Gothenburg, Sweden.
How Data Augmentation Shapes Neural Representations
May 14, 2026Tianxiao He, Alex H. Williams, Sarah E. HarveyNeural Network Representations Deep Network
Department of Computer Science New York University New York, NY 10012 USA · Center for Neural Science New York University New York, NY 10012 USA · Flatiron Institute New York, NY 10010, USA
Oversmoothing as Representation Degeneracy in Neural Sheaf Diffusion
May 11, 2026Arif Dönmez, Axel Mosig, Ellen Fritsche +1Sheaf Neural Networks Sheaf
IUF – Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany · DNTOX GmbH, Düsseldorf, Germany · Bioinformatics Group, Ruhr University Bochum, Bochum, Germany +1
TRAJGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations
May 7, 2026Maria Despoina Siampou, Gengchen Mai, Ni Lao +4Urban Mobility Traccia
Google Research, Mountain View, CA · Dept. of Computer Science, University of Southern California, Los Angeles, CA · SEAI Lab, Dept. of Geography and the Environment, The University of Texas at Austin, Austin, TX +1
Topological Signatures of Grokking
May 7, 2026Yifan Tang, Qiquan Wang, Inés García-Redondo +1Persistent Homology Neural Network Representations
Imperial College London · Queen Mary University of London · University of Fribourg
Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior
May 6, 2026Daniel Wurgaft, Can Rager, Matthew Kowal +13Neural Network Representations Linear Activation Steering
Stanford University · University College London · Northeastern University +2
Diffusion Operator Geometry of Feedforward Representations
May 1, 2026Kanishka ReddyNeural Network Representations Feed-Forward
Department of Applied Mathematics University of Washington Seattle, WA
Explicit integral representations and quantitative bounds for two-layer ReLU networks
Apr 25, 2026Anthony LeeRectified Linear Unit Networks Rectified Linear Unit
School of Mathematics, University of Bristol
Sheaf Neural Networks on SPD Manifolds: Second-Order Geometric Representation Learning
Apr 22, 2026Yuhan Peng, Junwen Dong, Yuzhi Zeng +6Sheaf Neural Networks Sheaf
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore · Chern Institute of Mathematics, Nankai University, Tianjin, China · Department of Mathematics, Faculty of Science, National University of Singapore, Singapore +3
Python library supporting Discrete Variational Formulations and training solutions with Collocation-based Robust Variational Physics Informed Neural Networks (DVF-CRVPINN)
Apr 16, 2026Tomasz Służalec, Marcin Łoś, Askold Vilkha +1Variational Formulation Neural Network
AGH University of Krakow, Poland
Neural parametric representations for thin-shell shape optimisation
Apr 8, 2026Xiao Xiao, Fehmi CirakShape Optimization Neural Network Representations
School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. · Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, United Kingdom.
Similarity of Neural Network Representations in Superposition
Mar 31, 2026Sunny Liu, Habon Issa, André Longon +4Representational Similarity Analysis Neural Network Representations
Cold Spring Harbor Laboratory · UC San Diego · Anthropic +2
Accelerated MR Elastography Using Learned Neural Network Representation
Jan 17, 2026Xi PengMagnetic Resonance Imaging Reconstruction Magnetic Resonance Imaging
Departments of Radiology, Biomedical Engineering, Electronic and Computer Engineering, and Mechanical Engineering, University of Iowa, Iowa City, IA 52246 · Department of Radiology, University of Iowa, Iowa City, IA, 52246

Oct 1, 2026, cs.LGJ/K move · Enter open · S save

Higher-Order Positional Encodings for Graph Representation Learning

Caleb Stam, Aagrim Hoysal, Sanjukta Krishnagopal

University of California, Santa Barbara

Graph Representation Learning Positional Encoding Higher-Order Structures Neural Network Representations

Open paper AskPDF arXiv

Many real-world systems exhibit higher-order interactions among groups of entities that cannot be captured by pairwise relationships alone. Graph Transformers and Graph Neural Networks increasingly rely on positional encodings to enrich graph representations, yet existing positional encodings are computed solely from the original graph and therefore cannot directly capture observed higher-order interactions. Topological Deep Learning addresses this limitation by lifting graphs to simplicial complexes, but typically requires performing message passing or attention on higher-order neural network representations. We introduce a representation learning paradigm that enriches graph representations with higher-order topology through positional encodings, enabling standard graph learning models to exploit lifted incidence structure without modifying the backbone. We derive a theoretical characterization of the expressivity of higher-order positional encodings, proving that node-level operators induced by higher-order lifts can mix graph Laplacian frequencies in ways that scalar graph spectral filters cannot. Guided by this theory, we instantiate higher-order positional encodings using Hodge Laplacians derived from clique complexes. Experiments with Graph Transformers on ZINC and controlled synthetic benchmarks demonstrate improvements in predictive performance, while a fixed-1-skeleton experiment shows that the pipeline can transmit higher-order information when cells are supplied independently of the graph. Together, our results establish higher-order positional encodings as a principled bridge between graph positional encodings and topological deep learning.

Momentum

Related topics

Latest papers 33