Towards Utilising a Range of Neural Activations for Comprehending Representational Associations | Paper Citation Record

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This paper cites Understanding inter- mediate layers using linear classifier probes.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Understanding inter- mediate layers using linear classifier probes

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This paper cites Network dissection: Quantifying inter- pretability of deep visual representations.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Network dissection: Quantifying inter- pretability of deep visual representations

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Gan dissection: Visualizing and understanding gener- ative adversarial networks

Reference 3

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Rep- resentation learning: A review and new perspectives

Reference 4

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations An Interpretability Illusion for BERT

Reference 5

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This paper cites Towards monose- manticity: Decomposing language models with dictionary learning.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Towards monose- manticity: Decomposing language models with dictionary learning

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This paper cites Gender shades: Inter- sectional accuracy disparities in commercial gender classifi- cation.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Gender shades: Inter- sectional accuracy disparities in commercial gender classifi- cation

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This paper cites Labeling neural representations with in- verse recognition.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Labeling neural representations with in- verse recognition

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This paper cites Isolating sources of disentanglement in varia- tional autoencoders.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Isolating sources of disentanglement in varia- tional autoencoders

Reference 9

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This paper cites Fair prediction with disparate im- pact: A study of bias in recidivism prediction instruments.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Fair prediction with disparate im- pact: A study of bias in recidivism prediction instruments

Reference 10

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This paper cites Independent component analysis, a new con- cept? Signal processing, 36(3):287–314, 1994.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Independent component analysis, a new con- cept? Signal processing, 36(3):287–314, 1994

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Flexibly fair representation learning by disentan- glement

Reference 12

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This paper cites Imagenet: A large-scale hierarchical image database.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Imagenet: A large-scale hierarchical image database

Reference 13

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This paper cites Toy- modelsof superposition.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Toy- modelsof superposition

Reference 14

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This paper cites Visualizing higher-layer features of a deep network.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Visualizing higher-layer features of a deep network

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This paper cites A holistic approach to unifying automatic concept extraction and concept importance estimation.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations A holistic approach to unifying automatic concept extraction and concept importance estimation

Reference 16

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This paper cites Craft: Concept recursive activation factoriza- tion for explainability.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Craft: Concept recursive activation factoriza- tion for explainability

Reference 17

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neu- ral networks

Reference 18

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This paper cites Scaling and evaluating sparse autoencoders.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Scaling and evaluating sparse autoencoders

Reference 19

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This paper cites Shortcut learning in deep neural networks.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Shortcut learning in deep neural networks

Reference 20

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This paper cites Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness

Reference 21

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This paper cites Don’t trust your eyes: on the (un) reliability of feature visualizations.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Don’t trust your eyes: on the (un) reliability of feature visualizations

Reference 22

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Concept discovery and dataset exploration with singular value decomposition

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Uncovering unique con- cept vectors through latent space decomposition

Reference 24

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Fairness without demo- graphics in repeated loss minimization

Reference 25

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Deep residual learning for image recognition

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations A baseline for detect- ing misclassified and out-of-distribution examples in neural networks

Reference 27

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations The origins and prevalence of texture bias in convolutional neu- ral networks

Reference 28

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations What shapes feature representations? exploring datasets, architectures, and training

Reference 29

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations beta-vae: Learning basic visual concepts with a constrained variational framework

Reference 30

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Disentanglement via latent quantiza- tion

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Does distributionally robust supervised learning give robust classifiers? In International Conference on Machine Learn- ing, pages 2029–2037

Reference 32

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Independent component analysis: algorithms and applications

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Simple data balancing achieves com- petitive worst-group-accuracy

Reference 34

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Shape or texture: Understanding discriminative features in cnns

Reference 35

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations On feature learning in the presence of spuri- ous correlations

Reference 36

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Removing spurious fea- tures can hurt accuracy and affect groups disproportionately

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Interpretability be- yond feature attribution: Quantitative testing with concept activation vectors (tcav)

Reference 38

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Last layer re-training is sufficient for robustness to spu- rious correlations

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Concept bottleneck models

Reference 40

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Towards a fuller understanding of neurons with clustered compositional explanations

Reference 41

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 42

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Just train twice: Improving group robustness without training group information

Reference 43

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Deep learning face attributes in the wild

Reference 44

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Challenging common assumptions in the unsuper- vised learning of disentangled representations

Reference 45

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Weakly-supervised disentanglement without compromises

Reference 46

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Understanding and Mitigating Human-Labelling Errors in Supervised Contrastive Learning

Reference 47

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations On interpretability of deep learning based skin lesion classifiers using concept activation vectors

Reference 48

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Understanding deep image representations by inverting them

Reference 49

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Promises and Pitfalls of Black-Box Concept Learning Models

Reference 50

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This paper cites Is this the subspace you are looking for? an inter- pretability illusion for subspace activation patching.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Is this the subspace you are looking for? an inter- pretability illusion for subspace activation patching

Reference 51

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This paper cites Do Concept Bottleneck Models Learn as Intended?.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Do Concept Bottleneck Models Learn as Intended?

Reference 52

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This paper cites Catastrophic inter- ference in connectionist networks: The sequential learning problem.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Catastrophic inter- ference in connectionist networks: The sequential learning problem

Reference 53

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Acquisition of chess knowledge in alphazero

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Umap: Uniform manifold approximation and projection

Reference 55

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Linguis- tic regularities in continuous space word representations

Reference 56

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This paper cites A comprehensive study of image classification model sensitivity to foregrounds, backgrounds, and visual attributes.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations A comprehensive study of image classification model sensitivity to foregrounds, backgrounds, and visual attributes

Reference 57

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Spuriosity rankings: sorting data to measure and miti- gate biases

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:07:23.364818Z

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Compositional explanations of neurons

Reference 59

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Beyond distribution shift: Spurious features through the lens of training dynamics

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:07:23.335775Z

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Spread spurious attribute: Improving worst-group accuracy with spurious attribute estimation

Reference 61

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This paper cites Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors

Reference 62

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Zoom in: An in- troduction to circuits

Reference 63

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Feature visualization

Reference 64

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Disentangling neuron representations with concept vectors

Reference 65

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations The linear rep- resentation hypothesis and the geometry of large language models

Reference 66

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Pytorch: An im- perative style, high-performance deep learning library

Reference 67

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Scikit-learn: Machine learning in python

Reference 68

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Identifying mislabeled data using the area under the margin ranking

Reference 69

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Why Should I Trust You?

Reference 70

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Parallel distributed processing, volume 1: Ex- plorations in the microstructure of cognition: Foundations

Reference 71

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Imagenet large scale visual recognition challenge

Reference 72

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Distributionally robust neural networks

Reference 73

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

Reference 74

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations A mathematical theory of semantic development in deep neural networks

Reference 75

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations The pitfalls of simplicity bias in neural networks

Reference 76

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Weakly supervised disentanglement with guarantees

Reference 77

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Opening the Black Box of Deep Neural Networks via Information

Reference 78

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations On the proper treatment of connectionism

Reference 79

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations No subclass left behind: Fine- grained robustness in coarse-grained classification prob- lems

Reference 80

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations In- triguing properties of neural networks

Reference 81

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Daniel Freeman, Theodore R

Reference 82

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Lin- ear spaces of meanings: compositional structures in vision- language models

Reference 83

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Thiagarajan

Reference 84

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Neuro- match academy: Teaching computational neuroscience with global accessibility

Reference 85

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Inves- tigating gender bias in language models using causal medi- ation analysis

Reference 86

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Concept algebra for score-based conditional model

Reference 87

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Caltech-ucsd birds 200

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:07:22.988455Z

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Sun database: Large-scale scene recognition from abbey to zoo

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:07:22.974979Z

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Observation e26a3b89-c132-4b35-b39d-d44c9ac45b85 · outbound

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Noise or signal: The role of image back- grounds in object recognition

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:07:22.960856Z

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Observation 0bdba247-9d96-41bb-8db4-2b47a85b318b · outbound

This paper cites Spurious correlations in machine learning: A survey.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Spurious correlations in machine learning: A survey

Reference 91

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Contin- ual learning through synaptic intelligence

Reference 92

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This paper cites Coping with label shift via distributionally robust optimisation.

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Coping with label shift via distributionally robust optimisation

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:07:22.939356Z

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Observation 0d7593fc-e1ab-4e2e-ab45-759e26941f2a · outbound

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Correct-n-contrast: a con- trastive approach for improving robustness to spurious corre- lations

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:07:22.925726Z

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Observation dabcd7b9-1d54-4db0-8785-b9816a2f1643 · outbound

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations trifea- ture

Reference 95

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Towards Utilising a Range of Neural Activations for Comprehending Representational Associations Unresolved cited work

Reference 2016

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