arXiv Machine Learning June 2, 2026 By Ping Xiong, Thomas Schnake, Gr\'egoire Montavon, Klaus-Robert M\"uller, Shinichi Nakajima

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

computer-vision

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

1d ago

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 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

6d ago

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

Related stories

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

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

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

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

Gradient Descent as Implicit EM in Distance-Based Neural Models

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