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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。
Eigenspectral decomposition framework for analyzing how information processing capacity (IPC) is distributed across reservoir state-space modes, with degree-wise representation energy and noise-aware capacity metrics for physical reservoir computers.
原文の言語: 英語
Paving the way for agents in biology
原文の言語: 英語
Structure-aware lightweight 3D CNN pre-decoder for scalable surface code quantum error correction. Use when designing or benchmarking neural QEC decoders for surface codes, implementing spatio-temporal syndrome processing, replacing dense 3D convolutions with…
原文の言語: 英語
"Skill for understanding and applying quantum reservoir computing theory based on quantum magic and non-commutativity as computational resources. Use when analyzing QRC architectures, designing quantum reservoir systems, or studying quantum advantages in…
原文の言語: 英語
Real-time optimal control framework using SHallow REcurrent Decoder networks-based Reduced Order Modeling (SHRED-ROM) for high-dimensional and parametric dynamical systems. Enables synthesis of closed-loop controllers from limited state sensor readings with…
原文の言語: 英語
Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization (DSTD) — reduces memory and training time for deep SNNs via fixed-time discretization and synfire-chain-inspired regularization
原文の言語: 英語
Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs
原文の言語: 英語
SpikingMOT framework for spike-driven multi-object tracking using spiking neural networks. Implements activation sparsity preference (ASP) with adaptive sparse trajectory dynamics modeling, achieving state-of-the-art performance while reducing parameters by…
原文の言語: 英語
A skill for modeling subjective experiences as emergent properties of neural processes based on the mathematical framework of subjective functions. Use this skill when you need to bridge objective neural measurements with subjective reports, model endogenous…
原文の言語: 英語
Framework for understanding nerve pulse propagation using soliton theory, connecting nonlinear wave dynamics to action potential generation and propagation in neurons.
原文の言語: 英語
Search Hardness-Aware LLM-Based Problem Formulation (SHA-PF) framework for expensive simulation-driven design. Prioritizes formulations that guide efficient search by focusing on rare samples with greater progress potential, reducing evaluation requirements…
原文の言語: 英語
Federated Cognitive Digital Twin (FCDT) architecture methodology combining federation and cognition within a unified approach for distributed Cyber-Physical Systems (CPSs).
原文の言語: 英語
Current Injection Spiking Neural Network (CIS-Fuse) for energy-efficient infrared and visible image fusion using membrane-potential level cross-modal integration
原文の言語: 英語
Spectral theory framework for analyzing population density dynamics of spiking neurons with finite refractory time. Provides rigorous operator-theoretic methods for studying neuronal population stability, oscillatory modes, and transfer functions. Use when…
原文の言語: 英語
Spectral theory framework for analyzing population density dynamics of spiking neurons with refractoriness. Provides rigorous mathematical foundation for spectral decomposition methods in computational neuroscience by formulating the problem as a…
原文の言語: 英語
Representing Entity Importance in AI Knowledge Systems: A Dual-Signal Framework of Audience Evaluati (arXiv: 2607.20925)
原文の言語: 英語
GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Dia (arXiv: 2607.21117)
原文の言語: 英語
RUMBA: Russian User Memory Benchmark (arXiv: 2607.21447)
原文の言語: 英語
X$^3$-OPD: Distilling Reasoning into Large Audio-Language Models via On-Policy Alignment (arXiv: 2607.21550)
原文の言語: 英語
LLMs Get Lost in Evolving User Intent (arXiv: 2607.20734)
原文の言語: 英語
IssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests (arXiv: 2607.20759)
原文の言語: 英語
Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs (arXiv: 2607.20814)
原文の言語: 英語
Is Deep Research Reliable? Misleading Knowledge Induces False Conclusions (arXiv: 2607.20891)
原文の言語: 英語
Multi-turn RL with Structural and Performance Aware Rewards for CUDA Kernel Generation (arXiv: 2607.20908)
原文の言語: 英語
SciExplore: Evaluating Autonomous Agents from Scientific Navigation to Information Integration (arXiv: 2607.20926)
原文の言語: 英語
The Weight of Silence: A Causal Case for Weights Over the Scratchpad in Latent Chess Reasoning (arXiv: 2607.20952)
原文の言語: 英語
Delivery, Not Storage: Cue-Anchored Working Memory as a Harness Property for Coding Agents (arXiv: 2607.20972)
原文の言語: 英語
GuardianAgentBench: Where Agents Fail and How to Guard Them (arXiv: 2607.20982)
原文の言語: 英語
HyWorldVLA: A Vision-Language-Action Model with Hybrid World Modeling for Autonomous Driving (arXiv: 2607.20988)
原文の言語: 英語
Workflow-Localized Mechanism Learning: Attribution-Guided Repair and Knowledge Reuse for Structured (arXiv: 2607.20999)
原文の言語: 英語
Reexamining zero-shot summarization: Empirical investigation of trustworthiness of LLM-summarizers (arXiv: 2607.21010)
原文の言語: 英語
EmoAgent-R1: Towards Multimodal Emotion Understanding with Reinforcement Learning-based Dynamic Agen (arXiv: 2607.21013)
原文の言語: 英語
HiMe: Real-Time Self-Hosted Personal Agent Platform for Health Insights with Wearable Devices (arXiv: 2607.21019)
原文の言語: 英語
Training Large Language Models for Self-Explanation Faithfulness (arXiv: 2607.21090)
原文の言語: 英語
A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GN (arXiv: 2607.21094)
原文の言語: 英語
AttriMem: Attribution-Guided Process Feedback for Agent Memory Learning (arXiv: 2607.21106)
原文の言語: 英語
TOUR: A Trajectory-Level Unlearning Benchmark for Offline Reinforcement Learning (arXiv: 2607.21111)
原文の言語: 英語
Explainability Framework for Policy-Aware Autonomous Agents (arXiv: 2607.21209)
原文の言語: 英語
ICAE-Bench: Evaluating Coding Agents as Interactive Project Builders (arXiv: 2607.21217)
原文の言語: 英語
Search Hardness-Aware LLM-Based Problem Formulation for Expensive Simulation-Driven Design (arXiv: 2607.21220)
原文の言語: 英語