Data & Analytics

Two-stage interpretation of attention as in-context empirical Bayes inference via particle dynamics with posterior mean recovery guarantees

**arXiv ID:** 1807.04587 **Authors:** Sergey Bartunov, Adam Santoro, Blake A. Richards, Luke Marris, Geoffrey E. Hinton, Timothy Lillicrap **Published:** 2018-07-12T12:53:50Z **Abstract:** The backpropagation of error algorithm (BP) is impossible to implement in a real brain. The recent success of deep networks in machine learning and AI, however, has inspired proposals for understanding how the brain might learn across multiple layers, and hence how it might approximate BP. As of yet, none o...

**arXiv ID:** 2608.26822v1 **Authors:** Timothy A. Smith, Mariah Pope, Sergey Frolov, Brett Basarab, Daniel Abdi, Paul Madden, Isidora Jankov **URL:** http://arxiv.org/abs/2608.26822v1 **Utility Score:** 1.00

Hybrid Machine Learning Framework for Herd-Level Cattle Growth Pattern and Weight Gain Forecasting in Grazing-Based Production Systems (arXiv: 2608.06001v1)

Hybrid Machine Learning Framework for Herd-Level Cattle Growth Pattern and Weight Gain Forecasting in Grazing-Based Production Systems (arXiv: 2608.06001)

Skill generated from arXiv paper 2607.14086: Leveraging unlabelled data for generalizable neural population decoding

Skill generated from arXiv paper 2112.12251v1: ML4CO: Is GCNN All You Need? Graph Convolutional N...

Skill generated from arXiv paper 2012.03793v1: Inter-layer Information Similarity Assessment of D...

ARFIMA decomposition of stride-to-stride fluctuations in human walking for sensorimotor control analysis. Activation triggers: stride fluctuations, human gait, DFA, ARFIMA, fractal analysis, sensorimotor control

Generalized framework of antisymmetric cross-polyspectral indices for identifying high-order neural interactions. Quantifies cross-frequency coupling while being intrinsically robust to volume conduction artifacts. Applicable to EEG/MEG analysis and personalized mTMS protocol design. Activation: antisymmetric polyspectral, cross-frequency coupling, high-order neural interactions, volume conduction robust, bispectral analysis, trispectral analysis, multi-frequency coupling, mTMS protocol.

Algorithmic Bohmian Mechanics (aBM) methodology using algorithmic randomness to formulate the distribution postulate as an objective constraining law. Guarantees standard Born statistics for canonical quantum experiments in the limit. Use for quantum foundations, interpretation of quantum mechanics, and algorithmic randomness in physical theories.

Skill for exploring brain networks using noninvasive electrophysiological measurements (EEG/MEG) based on arXiv:2607.17602v1. Covers forward/inverse problems, source reconstruction, connectivity measures, and analysis pipelines.

Artificial Intelligence applications in complex network science - network analysis, topology learning, dynamics prediction, and emergent behavior detection. Comprehensive survey covering AI potential, methodology, and applications. Use when analyzing complex networks, network topology learning, dynamics prediction, emergent behavior, social networks, biological networks, or transportation networks. Keywords: complex networks, network science, AI networks, topology dynamics, emergent behavior,...

**arXiv ID:** 1610.01922 **Authors:** Arif Budiman, Mohamad Ivan Fanany, Chan Basaruddin **Published:** 2016-10-06T16:08:52Z **Abstract:** A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regres...

Proposes that active sensing (energy expenditure for information) is not driven by sensory goals but is necessary for task-level control. Integrates empirical data and control theory to explain explore-exploit mode switching in biological sensorimotor systems. Use when researching active sensing, sensorimotor control, control theory in neuroscience, explore-exploit tradeoffs, or bio-inspired robotics.

**arXiv ID:** 2005.08083 **Authors:** Alex G. C. de Sá, Cristiano G. Pimenta, Gisele L. Pappa, Alex A. Freitas **Published:** 2020-05-16T20:08:04Z **Abstract:** Automated Machine Learning (AutoML) has emerged to deal with the selection and configuration of algorithms for a given learning task. With the progression of AutoML, several effective methods were introduced, especially for traditional classification and regression problems. Apart from the AutoML success, several issues remain open. O...

**arXiv ID:** 2412.13350 **Authors:** A. K. Alkhayyata, N. M. Hewahi **Published:** 2024-12-17T21:54:55Z **Abstract:** In this paper, a novel classification algorithm that is based on Data Importance (DI) reformatting and Genetic Algorithms (GA) named GADIC is proposed to overcome the issues related to the nature of data which may hinder the performance of the Machine Learning (ML) classifiers. GADIC comprises three phases which are data reformatting phase which depends on DI concept, trainin...

**arXiv ID:** 2507.10678 **Authors:** Cutter Dawes, Simon Segert, Kamesh Krishnamurthy, Jonathan D. Cohen **Published:** 2025-07-14T18:01:38Z **Abstract:** A major challenge in the use of neural networks both for modeling human cognitive function and for artificial intelligence is the design of systems with the capacity to efficiently learn functions that support radical generalization. At the roots of this is the capacity to discover and implement symmetry functions. In this paper, we invest...

Online Generalised Predictive Coding via Dynamic Expectation Maximisation (ODEM) for biologically plausible online learning. Activation: predictive coding, online learning, DEM, dynamic expectation maximisation, active inference.

On-Policy Distillation (OPD) methodology for transforming autoregressive models into diffusion language models efficiently, eliminating train-inference mismatch.

Nonstabilizerness diffusion dynamics methodology for analyzing magic resource generation in many-body quantum systems using stabilizer Renyi entropy and tensor network methods.

Linear equivalence of nonlinear recurrent neural networks using two-site cavity method. Shows covariance matrix of large nonlinear RNNs takes same form as linear networks with mean-field order parameters. Activation: nonlinear RNN, linear equivalence, cavity method, mean-field analysis, covariance matrix.

Analytical solution for large nonlinear recurrent neural networks at fixed connectivity. Calculates moments and response functions without synaptic weight averaging, linking connectivity to spontaneous activity and perturbation response. Trigger words: nonlinear RNN, fixed connectivity, moments, response functions, large N limit.

Nonequilibrium physics framework for brain dynamics analysis. Covers entropy production, time-irreversibility, broken detailed balance, and nonequilibrium computation in neural systems. Use when analyzing brain dynamics from nonequilibrium statistical physics perspective, measuring entropy production, studying time-irreversibility in neural data, or investigating consciousness/cognitive complexity through nonequilibrium metrics.