arXiv AI
Aug 5
Computing Actual Causes for Neural Network Predictions under Structured Causal Inputs
arXiv:2608. 03772v1 Announce Type: new Abstract: Explaining the predictions of neural networks is a central challenge in trustworthy AI.
Binary Spiking Neural Networks as Causal Models
arXiv:2604. 27007v2 Announce Type: replace Abstract: We provide a causal analysis of Binary Spiking Neural Networks (BSNNs) to explain their behavior.
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arXiv Machine Learning
Jul 9
Mechanistic Interpretability for Neural Networks: Circuits, Sparse Features and Symbolic Reasoning
arXiv:2607. 07316v1 Announce Type: new Abstract: This article offers a comprehensive overview of mechanistic interpretability, an emerging field that seeks to reverse-engineer the internal algorithms of modern neural networks.
arXiv AI
Jul 3
Causal Explanations for Image Classifiers
arXiv:2411. 08875v4 Announce Type: replace Abstract: Existing algorithms for explaining the output of image classifiers use different definitions of explanations and a variety of techniques to find them.
arXiv AI
Jul 10
How Causal Abstraction Underpins Computational Explanation
arXiv:2508. 11214v2 Announce Type: replace-cross Abstract: Explanations of cognitive behavior often appeal to computations over representations.
arXiv AI
Aug 7
Symbol Grounding in Neuro-Symbolic AI: A Gentle Introduction to Reasoning Shortcuts
arXiv:2510. 14538v3 Announce Type: replace Abstract: Neuro-symbolic (NeSy) AI aims to develop deep neural networks whose predictions comply with prior knowledge encoding, e.
arXiv Machine Learning
Jun 5
Expand Neurons, Not Parameters
arXiv:2510. 04500v3 Announce Type: replace Abstract: This work demonstrates how increasing the number of neurons in a network without increasing its total number of non-zero parameters improves performance.
arXiv Machine Learning
Aug 10
Mathematical Principles and Experimental Discoveries of the Emergence of Symbolic Patterns in Artificial Neural Networks
arXiv:2608. 06839v1 Announce Type: new Abstract: Artificial Neural networks (ANNs) are often treated as black-box models, making explainability a central challenge in deep learning.
arXiv AI
Jul 1
Can VLMs Reason Robustly? A Neuro-Symbolic Investigation
arXiv:2603. 23867v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have been applied to a wide range of reasoning tasks, yet it remains unclear whether they can reason robustly under distribution shifts.
arXiv AI
Aug 11
SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks
arXiv:2608. 08479v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing.
arXiv AI
Aug 6
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
arXiv:2608. 04285v1 Announce Type: new Abstract: Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention.
arXiv AI
Aug 10
Learning in Deep Networks under Dale's Constraint
arXiv:2608. 06963v1 Announce Type: new Abstract: Biologically plausible learning models aim to explain how neural circuits can implement effective learning under the constraints of real neurons.
arXiv Machine Learning
Aug 3
Mining Verdict Boundaries for Neural Network Verification
arXiv:2607. 28954v1 Announce Type: new Abstract: Branch and Bound (BaB) aims to achieve complete verification of neural networks by adaptively partitioning the problem and applying off-the-shelf verifiers to subproblems.