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- Recursive Gaussian Processes Predictive CodingRecursive Gaussian Processes (RGPs) methodology connecting predictive coding to Bayesian brain theories with neurobiological constraints. Use when implementing hierarchical Bayesian inference models that map to cortical microcircuits.Votes: 0GitHub stars: 3
- Recurrent Divisive Normalization NetworkRecurrent Divisive Normalization Network (RDNN) methodology for continuous working memory with low-rank slow manifolds. Implements biophysical divisive normalization constraint to prevent manifold shattering in RNNs while maintaining robust continuous representations. Use when: designing RNNs for continuous variable maintenance, implementing biologically-plausible working memory models, or addressing manifold shattering in artificial neural networks.Votes: 0GitHub stars: 3
- Reasoning As Double Edged Sword Architecture Cross Stage Robustness Vision Language ActionSkill derived from arXiv:2607.17786 - Reasoning as a Double-Edged Sword: Architecture and Cross-Stage Robustness in Vision-Language-ActionVotes: 0GitHub stars: 3
- Rdnn Divisive Normalization Working MemoryRecurrent Divisive Normalization Network (RDNN) framework for continuous working memory with robust low-rank slow manifolds. Use when implementing or analyzing neural networks that need to maintain and update continuous variables without manifold shattering, particularly in computational neuroscience, working memory modeling, or RNN architecture design.Votes: 0GitHub stars: 3
- Random Neural Network DimensionalityRandom neural networks methodology for matching observed dimensionality of neural population recordings using Dynamical Mean-Field Theory. Quantitative validation of minimal models with experimental data. Activation: 随机神经网络, 神经种群维度, dimensionality, neural population, mean-field theory, 维度性分析.Votes: 0GitHub stars: 3
- Qif Neurons Gradient Descent AdvantageQuadratic Integrate-and-Fire (QIF) neurons exhibit continuous spike-based gradient descent with less fragmented loss landscapes and outperform LIF neurons in SNN training — computational neuroscience methodology for improved spiking neural network optimization.Votes: 0GitHub stars: 3
- Psvit A Methodology For Structurally Pruning Spiking Vision Transformers**arXiv ID:** 2606.03257 **Authors:** Rachmad Vidya Wicaksana Putra, Achyuta Muthuvelan, Alberto Marchisio, Muhammad Shafique **Published:** 2026-06-02T07:18:57Z **Abstract:** Spiking Vision Transformer (SViT) models are promising low-power ViT models for solving vision-based tasks with state-of-the-art performance. However, their large sizes limit their deployments for resource-constrained embedded platforms, underscoring the needs of model compression. One of prominent compression technique...Votes: 0GitHub stars: 3
- Polydag Efficient Causal DiscoveryPolynomial acyclicity constraints for efficient continuous causal discovery in visual semantic graphs - 33% speedup over exponential baseline with improved F1 scores.Votes: 0GitHub stars: 3
- Photonic Neural Network MemoryMemory mechanisms in integrated photonic neural networks from physical principles to system design. Use for understanding optical computing memory, photonic reservoir computing, and neuromorphic photonics. Keywords: photonic neural networks, optical computing, memory mechanisms, integrated photonics, neuromorphic photonics, reservoir computing.Votes: 0GitHub stars: 3
- Phenomenological Renormalization Group Neuronal CriticalityPhenomenological Renormalization Group (PRG) validation methodology for detecting criticality in neuronal models. Validates PRG coarse-graining on excitable cellular automata and stochastic E/I LIF networks, introduces adaptive ISI-based time binning to eliminate spurious criticality signatures. Use for brain criticality analysis, avalanche dynamics, renormalization group neuroscience, scale-invariant neural activity. Activation: PRG, renormalization group, critical brain, neuronal criticalit...Votes: 0GitHub stars: 3
- On The Inductive Bias Of Dropout**arXiv ID:** 1412.4736 **Authors:** David P. Helmbold, Philip M. Long **Published:** 2014-12-15T19:40:46Z **Abstract:** Dropout is a simple but effective technique for learning in neural networks and other settings. A sound theoretical understanding of dropout is needed to determine when dropout should be applied and how to use it most effectively. In this paper we continue the exploration of dropout as a regularizer pioneered by Wager, et.al. We focus on linear classification where a convex...Votes: 0GitHub stars: 3
- On Policy Distillation Test Time ScalingOPD: efficiency vs capability via test-time scaling.Votes: 0GitHub stars: 3
- On First Order Meta Learning AlgorithmsSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Nonlinear Separation Principle Neural NetworksNonlinear separation principle for recurrent neural networks (RNNs) using contraction theory. Guarantees global exponential stability for contracting state-feedback controllers and observers. Applies to firing-rate and Hopfield RNN architectures. Based on paper by Gokhale et al. (arXiv 2604.15238, April 2026).Votes: 0GitHub stars: 3
- Non Equilibrium Continual LearningNon-equilibrium stochastic dynamics framework for continual learning using Kramers escape theory. Unifies insight and repetitive learning through thermodynamic perspective. Activation: non-equilibrium continual learning, Kramers escape learning, stability-plasticity dilemma, 非平衡持续学习, Kramers逃逸学习.Votes: 0GitHub stars: 3
- Neural Associative Memory For Dualsequence Modeling**arXiv ID:** 1606.03864 **Authors:** Dirk Weissenborn **Published:** 2016-06-13T09:08:04Z **Abstract:** Many important NLP problems can be posed as dual-sequence or sequence-to-sequence modeling tasks. Recent advances in building end-to-end neural architectures have been highly successful in solving such tasks. In this work we propose a new architecture for dual-sequence modeling that is based on associative memory. We derive AM-RNNs, a recurrent associative memory (AM) which augments generi...Votes: 0GitHub stars: 3
- Naturality Violation ScoreCategory-theory-based brain-DNN alignment methodology using Naturality Violation Score (NVS). Shifts alignment assessment from per-stimulus correspondence to preservation of candidate transformations. Activation: brain-DNN alignment, naturality violation, RSA critique, representational alignment, transformation alignment, brain model comparison, fMRI alignmentVotes: 0GitHub stars: 3
- Ml Qem Variational AlgorithmsMachine Learning-based Quantum Error Mitigation (ML-QEM) for variational quantum algorithms. Uses near-Clifford circuit simulation for training data, transfers across Hamiltonians, outperforms ZNE in high-noise regimes. Applicable to NISQ processors. arXiv:2606.02697.Votes: 0GitHub stars: 3
- Mice To Machines Neural Representations From Visual Cortex For Domain Generalization**arXiv ID:** 2505.06886 **Authors:** Ahmed Qazi, Hamd Jalil, Asim Iqbal **Published:** 2025-05-11T07:37:37Z **Abstract:** The mouse is one of the most studied animal models in the field of systems neuroscience. Understanding the generalized patterns and decoding the neural representations that are evoked by the diverse range of natural scene stimuli in the mouse visual cortex is one of the key quests in computational vision. In recent years, significant parallels have been drawn between the ...Votes: 0GitHub stars: 3
- Metatrained Agents Implement Bayesoptimal Agents**arXiv ID:** 2010.11223 **Authors:** Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega **Published:** 2020-10-21T18:05:21Z **Abstract:** Memory-based meta-learning is a powerful technique to build agents that adapt fast to any task within a target distribution. A previous theoretical study has argued that this remarkable performance is because the meta-training protocol incentivises agents to behave Bayes-optimally. We empirically inve...Votes: 0GitHub stars: 3
- Meta Learning In Context Enables Training FreeMeta-learning In-Context Enables Training-Free Cross Subject Brain Decoding - Research insights and implementation patterns from arXiv:2604.08537v1Votes: 0GitHub stars: 3
- Meta Learning In Context Brain Decoding V5BrainCoDec v5 — Foundation framework for training-free cross-subject brain decoding using meta-learning in-context approach. Enables zero-shot visual decoding from fMRI without subject-specific training. Activation: brain decoding, meta-learning, in-context, cross-subject, fMRI decoding, zero-shot brain decoding.Votes: 0GitHub stars: 3
- Meta Learning In Context Brain Decoding V4BrainCoDec v4 — Foundation framework for training-free cross-subject fMRI-based semantic visual decoding via meta-optimized in-context learning. Achieves zero-shot generalization across subjects and scanners without anatomical alignment or stimulus overlap. Use when: cross-subject brain decoding, fMRI visual reconstruction, training-free neural decoding, meta-learning for neuroscience, brain-computer interfaces. Trigger: brain decoding, fMRI decoding, cross-subject, meta-learning in-context, ...Votes: 0GitHub stars: 3
- Meta Learning Human Visual RepresentationsMeta-learning methodology for achieving human-like visual representations. Proposes that meta-learning (learning to learn) pressure shapes neural representations to support open-ended tasks. Compared to pretrained models, meta-learned representations better predict human similarity judgments, semantic rule learning, and high-level visual cortex activity. Activation: meta-learning, visual representations, brain alignment, human similarity, semantic learning, few-shot learning, visual cortexVotes: 0GitHub stars: 3