arXiv AI

Machine Learning Methods for Studying Latent Neural Activity Dynamics

arXiv:2606. 10530v1 Announce Type: cross Abstract: Recent developments in brain recording are driving a demand for machine learning tools capable of decoding the latent structure of large populations of neurons.

arXiv Machine Learning

Jun 3

A Factorized Low-Rank RNN Framework for Uncovering Independent Neural Latent Dynamics and Connectivity

arXiv:2511. 13899v2 Announce Type: replace-cross Abstract: Low-rank recurrent neural networks (lrRNNs) are a class of models that uncover low-dimensional latent dynamics underlying neural population activity.

By Chengrui Li, Yunmiao Wang, Yule Wang, Weihan Li, Dieter Jaeger, Anqi Wu

safety

arXiv Machine Learning

Jul 14

Emergent Generalization by Representation Learning in Artificial Neural Networks

arXiv:2607. 10430v1 Announce Type: cross Abstract: Dimensionality reduction has proven powerful for identifying neural manifolds, which are low-dimensional structures underlying high-dimensional neural activity.

By Hardik Rajpal, Dan Goodman

safety

arXiv Machine Learning

6d ago

Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates

arXiv:2608. 11435v1 Announce Type: new Abstract: Forward and inverse modeling of parametric dynamical systems requires surrogate models that are not only accurate for state prediction, but also informative for parameter calibration.

By Qiyao Zhou, Xujia Zhu, Pierre Joli, Yu Cong, Sibo Cheng

benchmarks

arXiv AI

Jul 20

Toward a mechanistic understanding of inference in visual cortex and diffusion models

arXiv:2607. 15693v1 Announce Type: cross Abstract: We describe a model of perceptual inference in primary visual cortex (V1) equivalent to a minimal diffusion model whose function can be readily understood from its parameters.

By Zeyu Yun, Alexander Belsten, Dasheng Bi, Zahra Kadkhodaie, Yubei Chen, Bruno A. Olshausen

diffusion

arXiv Machine Learning

Jul 3

Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs

arXiv:2603. 12676v3 Announce Type: replace Abstract: Generalizing neural surrogate models across different PDE parameters remains difficult because changes in PDE coefficients often make learning harder and optimization less stable.

By Zhangyong Liang, Huanhuan Gao

rag benchmarks

arXiv Machine Learning

Jul 27

Variational Low-rank Tensor Decomposition for Multisubject Spatiotemporal Data Analysis

arXiv:2607. 22262v1 Announce Type: cross Abstract: Modeling shared and subject-specific structure in multisubject spatiotemporal data remains challenging, particularly in neuroimaging, where both spatial and temporal patterns exhibit rich variability across subjects.

By Laura M. Montaldo, Ricardo A. Borsoi, Sebastian Miron, Tulay Adali

benchmarks

arXiv AI

Jun 6

EEGDancer: Dynamic Emotion Latent Space Masked Modeling with Reinforcement Learning for EEG Continuous Emotion Prediction

arXiv:2606. 05855v1 Announce Type: cross Abstract: Continuous electroencephalography (EEG) emotion prediction aims to model the temporal evolution of human emotional states from EEG signals.

By Zhihao Zhou, Weishan Ye, Li Zhang, Gan Huang, Zhen Liang

llms diffusion reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Jun 4

Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks

arXiv:2602. 14885v2 Announce Type: replace-cross Abstract: Recurrent neural networks (RNNs) provide a theoretical framework for understanding computation in biological neural circuits, yet classical results, such as Hopfield's model of associative memory, rely on symmetric connectivity that restricts network dynamics to gradient-like flows.

By Ram\'on Nartallo-Kaluarachchi, Renaud Lambiotte, Alain Goriely

rag agents diffusion

arXiv Machine Learning

Aug 6

Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation

arXiv:2510. 14190v3 Announce Type: replace Abstract: Diffusion models excel at generation, but their latent spaces are high dimensional and not explicitly organized for interpretation or control.

By Ruchi Sandilya, Sumaira Perez, Charles Lynch, Lindsay Victoria, Benjamin Zebley, Derrick Matthew Buchanan, Mahendra T. Bhati, Nolan Williams, Timothy J. Spellman, Faith M. Gunning, Conor Liston, Logan Grosenick

rag diffusion safety

arXiv Machine Learning

Jun 5

MSTN: A Lightweight and Fast Model for General TimeSeries Analysis

arXiv:2511. 20577v5 Announce Type: replace Abstract: Real-world time series often exhibit strong non-stationarity, complex nonlinear dynamics, and behavior expressed across multiple temporal scales, from rapid local fluctuations to slow-evolving long-range trends.

By Sumit S Shevtekar, Chandresh K Maurya

llms nlp benchmarks

arXiv AI

Jun 18

DecNefSimulator: A Modular, Interpretable Framework for Decoded Neurofeedback Simulation Using Generative Models

arXiv:2511. 14555v4 Announce Type: replace-cross Abstract: Decoded Neurofeedback (DecNef) is a promising non-invasive approach to brain modulation with wide-ranging applications in neuromedicine and cognitive neuroscience.

By Alexander Olza, Roberto Santana, David Soto

arXiv Machine Learning

Aug 10

Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics

arXiv:2604. 00669v2 Announce Type: replace Abstract: This study examines the challenges of modeling complex and noisy data related to socioeconomic factors over time, with a focus on data from various districts in Odisha, India.

By Sandeep Kumar Samota, Reema Gupta, Snehashish Chakraverty

rag diffusion

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