AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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180 papers
XAI-Refine is proposed, an automated explanation-knowledge loop for brain-age prediction from resting-state functional connectivity that consolidates complementary post-hoc analyses across repeated training runs into reliable, structured model explanations and proposes a structured route from post-hoc analysis to evide...
We introduce a probability-wave framework for modeling the collective behavior of interacting adaptive agents, deriving testable eigenmodes through a generalized behavioral intelligence (GBI) nonlocal probability-wave equation. This framework captures a broad range of human intelligence behaviors with analytical mechan...
Recurrent neural networks are canonical models of biological memory. In these models, memories are represented by distributed patterns of neural activity that are stored in the recurrent connections between neurons, such that they become attractors of the network's dynamics. During memory recall, network dynamics thus...
This work reformulates temporal credit assignment as a state separation problem: extracting task-required components induced by historical perturbations directly from the current neural state, which enables an online feedback learning framework for NMCs through a gradient tunneling algorithm and the lead-lag expansion...
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Dimensional attention in learning is often implemented as a globally shared attention vector, where each stimulus dimension corresponds to a single scalar. These scalars are learned by models through gradient-descent on error, where predictive features acquire more salience. We show that under multi-outcome learning, w...
EmoMind is presented, the first end-to-end pipeline for decoding affective captions directly from fMRI signals and establishes continuous brain-decoded affect as a viable control signal for individualized affective caption generation and open new directions for studying individual affective brain organisation.
It is hypothesized that neuroimaging data could open a window into elements of human cognition that are not accessible through observable actions, and it is argued that this additional knowledge could be used, alongside classical training data, to overcome some of the current limitations of foundation models.
A minimal analytical model distinguishing functional rules Phi_t = {R0, ..., Rk}, modification mechanisms Mt, and evaluation criteria Nt is proposed, which yields a conditional crossed-opacities hypothesis: humans often have richer endogenous self-description at abstract and strategic levels than at implementation leve...
The results demonstrate the mathematical consistency and computational feasibility of the proposed model, while the neurobiological hypotheses outline potential directions for its experimental verification.
This perspective lays down the basic concepts needed to understand MEG foundation models and provides a didactic overview of the field's key design choices, including tokenization, sensor- versus source-space representations, sensor-geometry encoding, backbone architectures, self-supervised objectives, and pretraining...
Recursive Quadrature Filters are identified as a parameter-efficient recurrent substrate and prospective-input coding as an input-side correction for deep continuous-time recurrent networks.
How a simple artificial agent that instantiates attributes of an affective system engaging in felt uncertainty about its intrinsic needs in relation to environmental resources can similarly display hedonic place preference behaviour — through an apparently subjective form of information processing — while simultaneousl...
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