arXiv Machine Learning

Normalized Relevance Measure as a Unifying Framework to Explain Neural Network Latent Structures

arXiv:2606. 00557v1 Announce Type: new Abstract: To understand how a neural network (NN) functions and makes predictions, it has become increasingly clear that analyzing only the input domain is insufficient -- one must also examine its internal inference mechanisms to capture the complete picture.

arXiv Machine Learning

Sep 15

Neuron Activation-based Computation of Logical Explanations for Deep Neural Networks

The paper introduces a flexible symbolic framework that efficiently computes logical explanations for deep neural networks by parameterizing explanations with internal neuron activations and leveraging general-purpose logical engines like SMT solvers. Unlike previous methods that rely on specialized verifiers or are limited to individual input features, this approach is not restricted in shape and can scale to deep architectures. Experiments on image recognition and medical benchmarks demonstrate improved computational efficiency and the ability to explain networks that were previously intractable for logic-based methods.

By Tom\'a\v{s} Kol\'arik, Faezeh Labbaf, Fabrizio Leopardi, Grigory Fedyukovich, Michael Wand, Natasha Sharygina

computer-vision benchmarks

arXiv Machine Learning

Sep 1

Integrating attention into explanation frameworks for language and vision transformers

arXiv:2508.08966v2 Announce Type: replace Abstract: The attention mechanism lies at the core of the transformer architecture, providing an interpretable model-internal signal that has motivated a gro...

By Marte Eggen, Jacob Lysn{\ae}s-Larsen, Inga Str\"umke

llms computer-vision nlp benchmarks

arXiv Machine Learning

Aug 4

LAYA: Layer-wise Attention Aggregation for Interpretable Depth-Aware Neural Networks

arXiv:2511. 12723v2 Announce Type: replace Abstract: Deep neural networks typically rely on the representation produced by their final hidden layer to make predictions, implicitly assuming that this single vector fully captures the semantics encoded across all preceding transformations.

By Gennaro Vessio

rag computer-vision safety

arXiv Machine Learning

Aug 18

Beyond $L_2$: Generalizing Abductive Latent Explanations to Diverse Prototype-Based Architectures

arXiv:2608. 16773v1 Announce Type: new Abstract: Prototype-based neural networks are hailed as interpretable-by-design architectures.

By Jules Soria, Alban Grastien, Romain Xu-Darme, Julien Girard-Satabin, Zakaria Chihani, Daniela Cancila

diffusion benchmarks safety

arXiv Machine Learning

Sep 1

Tracing distinguishability through transformer processing with stochastic LayerNorm

arXiv:2608.30720v1 Announce Type: new Abstract: Representational similarity is foundational to analyses of deep networks, yet distances between point-valued representations are not intrinsically tied...

By Kieran Murphy

llms fine-tuning efficiency safety

arXiv AI

Jun 10

Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification

arXiv:2411. 05698v3 Announce Type: replace-cross Abstract: Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification.

By Antonio De Santis, Riccardo Campi, Matteo Bianchi, Marco Brambilla

computer-vision benchmarks safety

arXiv AI

Aug 13

Class Activation Mapping in Explainable Computer Vision: A Method-Centered Review of CNN, Transformer, and Foundation-Model-Era Visual Explanations

arXiv:2608. 12299v1 Announce Type: cross Abstract: Class activation mapping (CAM) is one of the most widely used visual explanation families in explainable artificial intelligence.

By AmirHossein Eshghi, Hamid Saadatfar, Seyyed Ali Hoseini, AmirMohsen Eshghi, Siavash Arjomand Bigdel

llms computer-vision benchmarks

arXiv Machine Learning

Jul 7

Gradient Descent as Implicit EM in Distance-Based Neural Models

arXiv:2512. 24780v2 Announce Type: replace Abstract: Neural networks trained with standard objectives exhibit behaviors characteristic of probabilistic inference: soft clustering, prototype specialization, and Bayesian uncertainty tracking.

By Alan Oursland

llms

arXiv AI

Jul 1

Compositional Concept-Based Neuron-Level Interpretability for Deep Reinforcement Learning

arXiv:2502. 00684v2 Announce Type: replace-cross Abstract: Deep reinforcement learning (DRL) has successfully addressed many complex control problems.

By Zeyu Jiang, Hai Huang, Xingquan Zuo

agents computer-vision reinforcement-learning safety

arXiv Machine Learning

Sep 15

SPICE: Simple Polysemantic Feature Interpretation via Clustering-based Explanation

arXiv:2609.13198v1 Announce Type: new Abstract: One of the pivotal recent challenges in neural network interpretability is polysemanticity, where a single neuron is activated by multiple, often unrel...

By Sehyun Lee, Dahee Kwon, Damin Lee, Jaesik Choi

llms safety

arXiv Machine Learning

Sep 22

The Ups and Downs of Backprop Weights

The paper discusses how backpropagation enables deep learning but does not inherently organize parameters for reusable functional components, leading to weight entanglement where overlapping parameter sets hinder independent modification. It introduces weight operators—parameterized modules that can be composed at inference—to address this, proposing a two-stage learning process that first infers operator composition and then updates only the selected operators. Vector Networks (VNs) are presented as an implementation that couples operator selection to local error-driven updates, demonstrating that learned operators can be recombined in unseen ways while keeping updates confined to the relevant parameter sets.

By Giuseppe Chindemi, Benjamin F. Grewe

arXiv AI

Sep 10

Instance-wise Linearization of Neural Network for Model Interpretation

arXiv:2310.16295v2 Announce Type: replace-cross Abstract: Neural network have achieved remarkable successes in many scientific fields. However, the interpretability of the neural network model is sti...

By Zhimin Li, Shusen Liu, Kailkhura Bhavya, Peer-Timo Bremer, Valerio Pascucci

safety

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