Sparks of Explainability: Recent Advancements in Explaining Large Vision Models | Paper Citation Record

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This paper cites Quantifying attention flow in transformers.

Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Quantifying attention flow in transformers

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Metaplasticity: the plasticity of synaptic plasticity

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This paper cites From attribution maps to human-understandable explanations through concept relevance propagation.

Sparks of Explainability: Recent Advancements in Explaining Large Vision Models From attribution maps to human-understandable explanations through concept relevance propagation

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Sanity checks for saliency maps

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Rethinking stability for attribution-based explanations

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This paper cites Does explainable artificial intelligence improve human decision-making? In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2021.

Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Does explainable artificial intelligence improve human decision-making? In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2021

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Jaakkola

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This paper cites Towards better understanding of gradient-based attribution methods for deep neural networks.

Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Towards better understanding of gradient-based attribution methods for deep neural networks

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This paper cites Current challenges and future opportunities for xai in machine learning-based clinical decision support systems: a systematic review.

Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Current challenges and future opportunities for xai in machine learning-based clinical decision support systems: a systematic review

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models u ller, and Wojciech Samek

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This paper cites Explaining recurrent neural network predictions in sentiment analysis.

Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Explaining recurrent neural network predictions in sentiment analysis

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Øygard Audun

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Layer normalization

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This paper cites On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation.

Sparks of Explainability: Recent Advancements in Explaining Large Vision Models On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Adversarial training and provable defenses: Bridging the gap

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models The moore--penrose pseudoinverse: A tutorial review of the theory

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Foster, and Matus Telgarsky

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Towards Formal XAI: Formally Approximate Minimal Explanations of Neural Networks

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Explanation: A mechanist alternative

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Algorithmic differentiation of implicit functions and optimal values

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Fairness seen as global sensitivity analysis

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Joint rotational invariance and adversarial training of a dual-stream transformer yields state of the art Brain-Score for area V4

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Numerical influence of relu’(0) on backpropagation

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Pay attention to your loss: understanding misconceptions about lipschitz neural networks

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Robust one-class classification with signed distance function using 1-lipschitz neural networks

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Dp-sgd without clipping: The lipschitz neural network way

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models FlexiViT: One Model for All Patch Sizes

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Quality metrics for transparent machine learning with and without humans in the loop are not correlated

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Efficient and modular implicit differentiation

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models B-cos networks: alignment is all we need for interpretability

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Holistically Explainable Vision Transformers

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models When pigs fly: Contextual reasoning in synthetic and natural scenes

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Proper network interpretability helps adversarial robustness in classification

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Exemplary natural images explain cnn activations better than state-of-the-art feature visualization

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Asteryx: A model-agnostic sat-based approach for symbolic and score-based explanations

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Stability and generalization

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Diffusion models as artists: Are we closing the gap between humans and machines? In Proceedings of the International Conference on Machine Learning (ICML), 2023

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models CYBORG : Blending human saliency into the loss improves deep learning

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Distributed optimization and statistical learning via the alternating direction method of multipliers

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Towards monosemanticity: Decomposing language models with dictionary learning

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models How people look at pictures: a study of the psychology and perception in art

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models The many faces of 1-lipschitz neural networks, 2021

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Visual sensitivity to two-dimensional spatial phase

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Thread: Circuits

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Carvalho, Eduardo M

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models On the length of programs for computing finite binary sequences: statistical considerations

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Algorithmic information theory

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Meta-reinforcement learning with self-modifying networks

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Do explanations make vqa models more predictable to a human? Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2018

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models A simple framework for contrastive learning of visual representations

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Transformer tracking

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Dual path networks

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Fast fourier convolution

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Xception: Deep learning with depthwise separable convolutions

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Xception: Deep learning with depthwise separable convolutions

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Fast local algorithms for large scale nonnegative matrix and tensor factorizations

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models What i cannot predict, i do not understand: A human-centered evaluation framework for explainability methods

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Characterizations of an empirical influence function for detecting influential cases in regression

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Image complexity measure based on visual attention

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Global sensitivity analysis with dependence measures

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Efficient estimation of sensitivity indices

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Underspecification presents challenges for credibility in modern machine learning

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models ConViT : Improving vision transformers with soft convolutional inductive biases

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Approximating rate-distortion graphs of individual data: Experiments in lossy compression and denoising

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Emergent properties of foveated perceptual systems

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models How does the brain solve visual object recognition? Neuron, 73 0 (3): 0 415--434, February 2012

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models On the equivalence of nonnegative matrix factorization and spectral clustering

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Towards a rigorous science of interpretable machine learning

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models An image is worth 16x16 words: Transformers for image recognition at scale

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models An image is worth 16x16 words: Transformers for image recognition at scale

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models An image is worth 16x16 words: Transformers for image recognition at scale

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Incorporating nesterov momentum into adam

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Decomon: Automatic certified perturbation analysis of neural networks, 2021

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Dictionary learning algorithms and applications

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Do humans look where deep convolutional neural networks ``attend''? In Advances in Visual Computing, pages 53--65

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models The approximation of one matrix by another of lower rank

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Formal verification of piece-wise linear feed-forward neural networks

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Toy models of superposition

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Explaining classifiers using adversarial perturbations on the perceptual ball

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Adversarial Robustness as a Prior for Learned Representations

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Visualizing higher-layer features of a deep network

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models The pascal visual object classes (voc) challenge

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models The characteristics and limits of rapid visual categorization

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Initialization for non-negative matrix factorization: a comprehensive review

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Representativity and consistency measures for deep neural network explanations

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Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis

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