Saved in:
| Main Authors: | Longon, André, Klindt, David, Khosla, Meenakshi |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.03186 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Measuring the Representational Alignment of Neural Systems in Superposition
by: Liu, Sunny, et al.
Published: (2026)
by: Liu, Sunny, et al.
Published: (2026)
Barycentric alignment for instance-level comparison of neural representations
by: Saha, Shreya, et al.
Published: (2026)
by: Saha, Shreya, et al.
Published: (2026)
Sparse components distinguish visual pathways & their alignment to neural networks
by: Marvi, Ammar I, et al.
Published: (2025)
by: Marvi, Ammar I, et al.
Published: (2025)
Naturally Computed Scale Invariance in the Residual Stream of ResNet18
by: Longon, André
Published: (2025)
by: Longon, André
Published: (2025)
Interpreting the Residual Stream of ResNet18
by: Longon, André
Published: (2024)
by: Longon, André
Published: (2024)
Representational Alignment Across Model Layers and Brain Regions with Multi-Level Optimal Transport
by: Shah, Shaan, et al.
Published: (2025)
by: Shah, Shaan, et al.
Published: (2025)
From superposition to sparse codes: interpretable representations in neural networks
by: Klindt, David, et al.
Published: (2025)
by: Klindt, David, et al.
Published: (2025)
Modeling the language cortex with form-independent and enriched representations of sentence meaning reveals remarkable semantic abstractness
by: Saha, Shreya, et al.
Published: (2025)
by: Saha, Shreya, et al.
Published: (2025)
Modeling the Human Visual System: Comparative Insights from Response-Optimized and Task-Optimized Vision Models, Language Models, and different Readout Mechanisms
by: Saha, Shreya, et al.
Published: (2024)
by: Saha, Shreya, et al.
Published: (2024)
Partial Soft-Matching Distance for Neural Representational Comparison with Partial Unit Correspondence
by: Kapoor, Chaitanya, et al.
Published: (2026)
by: Kapoor, Chaitanya, et al.
Published: (2026)
Uncovering hidden geometry in Transformers via disentangling position and context
by: Song, Jiajun, et al.
Published: (2023)
by: Song, Jiajun, et al.
Published: (2023)
Measuring the Measures: Discriminative Capacity of Representational Similarity Metrics Across Model Families
by: Wu, Jialin, et al.
Published: (2025)
by: Wu, Jialin, et al.
Published: (2025)
Data Whitening Improves Sparse Autoencoder Learning
by: Saraswatula, Ashwin, et al.
Published: (2025)
by: Saraswatula, Ashwin, et al.
Published: (2025)
IndiSeek learns information-guided disentangled representations
by: Gui, Yu, et al.
Published: (2025)
by: Gui, Yu, et al.
Published: (2025)
Human alignment of neural network representations
by: Muttenthaler, Lukas, et al.
Published: (2022)
by: Muttenthaler, Lukas, et al.
Published: (2022)
Sparsity regularization via tree-structured environments for disentangled representations
by: Layne, Elliot, et al.
Published: (2024)
by: Layne, Elliot, et al.
Published: (2024)
Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations?
by: Ibrahim, Mark, et al.
Published: (2024)
by: Ibrahim, Mark, et al.
Published: (2024)
Compute Optimal Inference and Provable Amortisation Gap in Sparse Autoencoders
by: O'Neill, Charles, et al.
Published: (2024)
by: O'Neill, Charles, et al.
Published: (2024)
Brain-Model Evaluations Need the NeuroAI Turing Test
by: Feather, Jenelle, et al.
Published: (2025)
by: Feather, Jenelle, et al.
Published: (2025)
Disentangled and Self-Explainable Node Representation Learning
by: Piaggesi, Simone, et al.
Published: (2024)
by: Piaggesi, Simone, et al.
Published: (2024)
Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning
by: Kunin, Daniel, et al.
Published: (2024)
by: Kunin, Daniel, et al.
Published: (2024)
How does Graph Structure Modulate Membership-Inference Risk for Graph Neural Networks?
by: Khosla, Megha
Published: (2026)
by: Khosla, Megha
Published: (2026)
Variational decomposition autoencoding improves disentanglement of latent representations
by: Ziogas, Ioannis, et al.
Published: (2026)
by: Ziogas, Ioannis, et al.
Published: (2026)
Winner-Take-All bottlenecks enforce disentangled symbolic representations in multi-task learning
by: Gutheil, Julian, et al.
Published: (2026)
by: Gutheil, Julian, et al.
Published: (2026)
Dimensions underlying the representational alignment of deep neural networks with humans
by: Mahner, Florian P., et al.
Published: (2024)
by: Mahner, Florian P., et al.
Published: (2024)
Getting aligned on representational alignment
by: Sucholutsky, Ilia, et al.
Published: (2023)
by: Sucholutsky, Ilia, et al.
Published: (2023)
Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research
by: Reizinger, Patrik, et al.
Published: (2025)
by: Reizinger, Patrik, et al.
Published: (2025)
GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification
by: Zhao, Tianqi, et al.
Published: (2024)
by: Zhao, Tianqi, et al.
Published: (2024)
Transferring disentangled representations: bridging the gap between synthetic and real images
by: Dapueto, Jacopo, et al.
Published: (2024)
by: Dapueto, Jacopo, et al.
Published: (2024)
Are aligned neural networks adversarially aligned?
by: Carlini, Nicholas, et al.
Published: (2023)
by: Carlini, Nicholas, et al.
Published: (2023)
Evaluating alignment between humans and neural network representations in image-based learning tasks
by: Demircan, Can, et al.
Published: (2023)
by: Demircan, Can, et al.
Published: (2023)
An unsupervised tour through the hidden pathways of deep neural networks
by: Doimo, Diego
Published: (2025)
by: Doimo, Diego
Published: (2025)
Enforcing hidden physics in physics-informed neural networks
by: Chen, Nanxi, et al.
Published: (2025)
by: Chen, Nanxi, et al.
Published: (2025)
Stop Probing, Start Coding: Why Linear Probes and Sparse Autoencoders Fail at Compositional Generalisation
by: Pacela, Vitória Barin, et al.
Published: (2026)
by: Pacela, Vitória Barin, et al.
Published: (2026)
Causality is Key for Interpretability Claims to Generalise
by: Joshi, Shruti, et al.
Published: (2026)
by: Joshi, Shruti, et al.
Published: (2026)
Draw a Portrait of Your Graph Data: An Instance-Level Profiling Framework for Graph-Structured Data
by: Zhao, Tianqi, et al.
Published: (2025)
by: Zhao, Tianqi, et al.
Published: (2025)
AGALE: A Graph-Aware Continual Learning Evaluation Framework
by: Zhao, Tianqi, et al.
Published: (2024)
by: Zhao, Tianqi, et al.
Published: (2024)
Latent Functional Maps: a spectral framework for representation alignment
by: Fumero, Marco, et al.
Published: (2024)
by: Fumero, Marco, et al.
Published: (2024)
Knowledge distillation through geometry-aware representational alignment
by: Bhattarai, Prajjwal, et al.
Published: (2025)
by: Bhattarai, Prajjwal, et al.
Published: (2025)
Adversarial Examples Are Not Bugs, They Are Superposition
by: Gorton, Liv, et al.
Published: (2025)
by: Gorton, Liv, et al.
Published: (2025)
Similar Items
-
Measuring the Representational Alignment of Neural Systems in Superposition
by: Liu, Sunny, et al.
Published: (2026) -
Barycentric alignment for instance-level comparison of neural representations
by: Saha, Shreya, et al.
Published: (2026) -
Sparse components distinguish visual pathways & their alignment to neural networks
by: Marvi, Ammar I, et al.
Published: (2025) -
Naturally Computed Scale Invariance in the Residual Stream of ResNet18
by: Longon, André
Published: (2025) -
Interpreting the Residual Stream of ResNet18
by: Longon, André
Published: (2024)