メニュー
このリポジトリの skills
SkillsMP は hiyenwong/ai_collection から 4,114 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
hiyenwong/ai_collection収集済み skill 4,114 件中 40 件を表示しています。
Theoretical analysis of effective target shift in online learning and methods to correct for it. Explains why online learning struggles under distributional shift and how to characterize the relationship between online and offline learning. Activation…
原文の言語: 英語
SR²AM (Self-Regulated Simulative Reasoning Agentic LLM) methodology from arXiv:2605.22138 (May 2026). Three-system framework decomposing agent reasoning: System II (simulative planning via world model), System III (self-regulation deciding when/how to plan),…
原文の言語: 英語
Extended Sompolinsky-Crisanti-Sommers (SCS) chaos theory for Excitatory-Inhibitory recurrent networks with target-specific inhibition. Derives mean-field theory for E/I networks, identifies three dynamical regimes (quiescence, asynchronous chaos, coherent…
原文の言語: 英語
Expert guidance for designing modular Mixture-of-Experts (MoE) architectures using emergent document-level expert grouping. Based on EMO paper (arXiv:2605.06663). Use when designing sparse LLM architectures, MoE modularity, expert specialization,…
原文の言語: 英語
E-SPL: Evolutionary System Prompt Learning
原文の言語: 英語
Evolutionary Generative Merging (EvoGM) framework for training-free LLM composition via learnable generative modeling and dual-generator architecture with cycle-consistent learning
原文の言語: 英語
Excitation-driven data generation and distributed control optimization for building thermal systems and district heating networks. Combines BuilDyn framework (arXiv:2605.29849) and distributed NMPC with ADMM (arXiv:2605.29841).
原文の言語: 英語
FCN-LLM: Empowering LLMs for Brain Functional Connectivity Network Understanding via Graph-level Multi-task Instruction Tuning. Covers the multi-scale FCN encoder, semantic projection into LLM, 19-attribute multi-paradigm instruction tuning, two-stage…
原文の言語: 英語
Backpropagation-free continual learning using feedback-aligned Hebbian plasticity. Replaces backpropagation with local Hebbian updates guided by random feedback connections, enabling biologically plausible continual learning without catastrophic forgetting.…
原文の言語: 英語
FLAME: Adaptive Mixture-of-Experts for continual multimodal multi-task learning. Handles both co-available multi-task pretraining and sequential continual adaptation. Activation triggers: FLAME MoE, continual multimodal learning, adaptive mixture of experts,…
原文の言語: 英語
Free Energy Principle-based Mixture-of-Experts routing methodology. Uses LIF membrane potentials (beta) for temporal memory, precision-weighted gating (Pi) for reliability assessment, and anticipatory routing to solve domain transition failures in sparse MoE.…
原文の言語: 英語
Free Energy Principle-based MoE routing using LIF membrane dynamics. Solves domain transition failures in sparse MoE with three mechanisms: temporal memory (beta), precision-weighted gating (Pi), and anticipatory routing. 124x improvement at transitions.…
原文の言語: 英語
Geno-Synthetic Algorithm: type-factored coevolutionary optimization for heterogeneous genotypes and assembled phenotypes. Use when: coevolutionary algorithms, heterogeneous genotype optimization, assembled phenotype synthesis, evolutionary computation, neural…
原文の言語: 英語
GFFMERGE methodology for efficient closed-form model merging in Graph Neural Networks. Exploits linear structure of message-passing layers to enable near-quantum accuracy atomistic simulations without retraining foundation models. Applicable to drug…
原文の言語: 英語
图神经网络与 Transformer 融合的多模态数据融合方法论。 整合非欧几里得脑影像数据与欧几里得表格数据，支持时间感知的纵向预测。 触发词：多模态融合、脑网络、GNN、Transformer、时序预测、纵向分析、 multimodal fusion, brain connectivity, GNN-TF, temporal fusion。
原文の言語: 複数言語
Graph-based workflow management paradigm for efficient LLM agent serving using unified directed graphs (wGraph) for dynamic workflow instantiation with KV-cache optimization. Use for LLM agent serving, workflow management, agent serving optimization, agent…
原文の言語: 英語
GTaS Generative Spike Train Model
原文の言語: 英語
GyralNet Subnetwork Partitioning
原文の言語: 英語
Hardware-motivated noise modeling methodology for fault-tolerant quantum computing benchmarking. Uses structured noise families (Pauli bias, measurement bias, spatial non-uniformity) instead of uniform depolarizing model to faithfully reflect real device…
原文の言語: 英語
Benchmarking 7 local Hebbian learning rules for associative memory storage and prototype extraction. Bayesian-Hebbian rules achieve highest capacity. Activation: hebbian learning benchmark, associative memory capacity, prototype extraction, Bayesian-Hebbian…
原文の言語: 英語
分层 MoE 架构技能 - 用于对象检测的分层实例条件化混合专家模型 (HI-MoE)。通过两级路由机制实现稀疏计算与实例中心结构的匹配。基于论文 HI-MoE: Hierarchical Instance-Conditioned Mixture-of-Experts (arXiv 2604.04908)。激活关键词: MoE, mixture of experts, object detection MoE, instance routing, 分层路由, 实例条件化。
原文の言語: 中国語
Web-scale LLM-based multi-agent system architecture for the Agentic Web. Focuses on five-layer coordination architecture, heterogeneous agent interaction, and open-world scaling challenges. Use when: (1) Designing large-scale multi-agent systems, (2)…
原文の言語: 英語
Modern Hopfield Networks for continual learning in diffusion models via energy-based intrinsic forgetting and replay selection
原文の言語: 英語
Hippocampal-Entorhinal (HPC-MEC) inspired hierarchical world model for structure abstraction and generalization from video sequences. Based on arXiv:2605.15733 (May 2026). Use when: designing brain-inspired world models, HPC-MEC cognitive architecture,…
原文の言語: 英語
Integrative neurocybernetic modeling framework for large-scale neuroscience. Treats brain as controller pursuing latent objectives in closed-loop coupling with body and environment. Keywords: neurocybernetics, closed-loop modeling, large-scale neuroscience,…
原文の言語: 英語
Bayesian modeling methodology for ion channel gating with missed event correction. Integrates temporal resolution limitations of patch clamp recordings into hierarchical Markov chain likelihood functions, enabling unbiased kinetic parameter inference and…
原文の言語: 英語
DMOSOPT — scalable optimization framework using jointly learned surrogate models for constrained multi-objective optimization of neural dynamical systems. Learns smooth approximations of objective landscapes and feasibility boundaries to guide search with…
原文の言語: 英語
Kinetic energy in random recurrent neural networks - links chaotic dynamics and unstable fixed points through dynamical mean-field theory. Cubic scaling at critical point, shell-like chaotic manifold geometry. Activation: kinetic energy, random RNN, chaos,…
原文の言語: 英語
Neuro-inspired phase encoding using Kuramoto oscillators (KoPE) applied to Vision Transformers, combining oscillatory dynamics with attention mechanisms for improved learning efficiency. Based on ICLR 2025 paper by Xiao et al. (Microsoft Research).
原文の言語: 英語
基于格论的RNN剪枝方法论。将RNN建模为偏序集，构建依赖格，识别不可约元进行选择性剪枝。相比传统幅度剪枝更好地保留功能连接性。触发词：RNN剪枝、格剪枝、偏序集、依赖格、不可约元、网络压缩、lattice pruning、poset、meet irreducible。
原文の言語: 中国語
Distributionally robust free energy principle for reliable robotic control. Jointly learns environment dynamics and rewards while ensuring robustness to epistemic uncertainties. Validated on Franka Research 3 arm manipulation tasks.
原文の言語: 英語
Developmental scaffoldings methodology for guiding self-organisation through learned pre-patterns. Joint NCA+SIREN model that offloads information to initial conditions, enabling robustness, encoding capacity, and symmetry breaking improvements. Activation:…
原文の言語: 英語
Learning Dynamic Stability Landscapes in Synchronization Networks methodology - graph-to-image prediction paradigm for predicting stability landscapes from network topology. Pioneers image-like per-node stability landscapes beyond scalar indices. Applicable…
原文の言語: 英語
Long Context Pre-Training with Lighthouse Attention — NousResearch 提出的对称选择式分层注意力算法。面向超长上下文预训练，通过对称 Q/K/V 金字塔池化、无参数打分 top-K 选择、选择与注意力解耦、两阶段训练，实现 O(N·d) 复杂度。触发词：lighthouse attention, long context pretraining, hierarchical attention, symmetric pooling, sparse…
原文の言語: 中国語
Design LLM agent systems using the externalization framework from cognitive artifacts theory (Norman). Covers memory externalization (state across time), skills externalization (procedural expertise), protocol externalization (interaction structure), and…
原文の言語: 英語
Decision-Centric framework for LLM systems that separates decision signals from action policies. Apply this when designing LLM control flow, routing, adaptive inference, or building diagnosable agent systems.
原文の言語: 英語
Methodology for identifying and analyzing functional emotion representations in LLM internals. Covers finding emotion-related neural activity patterns, testing their causal influence via activation steering, and understanding how abstract emotion concepts…
原文の言語: 英語
Neuroscience-inspired geometric account of in-context learning (ICL) in LLMs — how representational geometry reshapes to support online untangling and classification without parameter updates.
原文の言語: 英語
大型语言模型通过内部置信度信号检测和纠正自身错误的研究。基于决策神经科学二阶置信度模型。
原文の言語: 中国語
大型语言模型通过内部置信度信号检测和纠正自身错误的研究。基于决策神经科学二阶置信度模型和PANL token机制。
原文の言語: 中国語