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SkillsMP has collected 4,114 skills from hiyenwong/ai_collection. Open a skill to review its source and details.
Showing 40 of 4,114 collected skills.
Graph-native Python reimplementation of the Information Dynamics of Music (IDyOM) model that represents predictive memories as explicit graph objects for musical expectation modeling and network analysis.
Physics-aware end-to-end deep reinforcement learning methodology for quadcopter control with actuator dynamics modeling.
Reinforced Dreamer methodology for asymmetric reinforcement learning using latent guidance to improve world model representations and behaviors in model-based RL.
Relay On-Policy Distillation (Relay-OPD) methodology for trajectory-relayed token-level supervision to overcome prefix failure in reasoning models.
Methodology for identifying and preventing phantom evidence - false positives manufactured by generative AI that appear convincing but lack genuine evidential value due to narrow hypothesis spaces and data leakage.
Gain-Load-Alignment Principle for Dendritic E/I Networks - Framework for understanding when branch-local shunting helps in neural population readout. Analyzes DendriNet architecture with varying integration rules, morphology, and synaptic allocation. Use when…
LLM-powered EEG analysis agent grounded in MNE-Python that separates semantic interpretation from scientific validation using deterministic contracts and confirmation controls to prevent false positives.
Methodology for analyzing and addressing the spectral-temporal dissociation in EEG foundation models that causes cross-population fragility due to blindness to long-range temporal correlations (LRTC).
Analysis of ~400,000 Claude Code sessions showing domain expertise creates persistent returns in agentic coding performance, with expert users achieving 2-3x higher success rates and more efficient tool usage.
Scalable Variational Quantum Optimization via Pauli Correlation Encoding (PCE) methodology for large-scale combinatorial optimization problems, particularly power demand portfolio optimization. Uses expectation values of Pauli correlation operators to…
Large-scale benchmark methodology for EEG motor imagery decoding that addresses subject-level heterogeneity through portfolio-based pipeline selection. Use when analyzing inter-individual variability in EEG BCI systems, comparing covariance tangent-space…
Runtime framework for speculative sandbox preallocation and scheduling in LLM agent serving environments to optimize resource utilization and reduce tail latency.
Synaptic clustering methodology for learning covariance structure discrimination using Dendrinet architecture with hierarchical dendritic segments and sparse conductance-based synapses. Use when analyzing how functional synapse clusters (FSCs) emerge from…
Methodology for studying emergent lifelike patterns (Limbomorphs) in Gifbreeder systems that encode spatiotemporal fields through aesthetic selection, analyzing their species-specific reactions to perturbations and assessing whether they exhibit genuine…
Methodology for identifying distributed functional-connectivity signatures of Alzheimer's disease using subject-specific reservoir-computing models and developing personalized neuromodulation strategies.
Fluid search methodology for adaptive search efficiency in autonomous research systems. Uses portfolio bandit to dynamically allocate evaluation budget across search processes, optimizing area under Pareto frontier curve.
ModernMOE (MMOE) methodology for modernizing diffusion transformers with efficient expert design. Adapts routed experts, shared and lightweight experts, gate-residual routing, and attention-residual information reuse to AIGC generation.
Physics Transformer methodology for PDE prediction using function-projection-based tokenization. Treats physical fields as continuous functions with adaptive local basis functions and locality-preserving spatial patches.
Distributed functional-connectivity signature of Alzheimer's disease methodology using subject-specific reservoir-computing models to reconstruct individual lagged functional connectivity and develop personalized neuromodulation strategies. Shows that optimal…
Adaptive electrode-selection method using discounted Poisson-Gamma model with Thompson sampling for tracking non-stationary spontaneous activity during long-term HD-MEA recordings under fixed channel budget constraints.
Comprehensive benchmarking framework for stress-testing EEG foundation models with dataset identity analysis and targeted negative controls to evaluate clinical decoding robustness.
The Semantic Least-Energy Principle (SLEP) hypothesis that intelligent systems evolve internal representations by maximizing semantic utility while minimizing semantic, predictive, and computational energy.
SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing that bridges deep learning and neuromorphic computing. Use when working with neuromorphic hardware design, spiking neural network acceleration, or brain-inspired…
Self-supervised pretraining on spontaneous neural activity using masked autoencoders to improve perception decoding in clinical neuroprosthetics. Achieves 84.1% accuracy on psychometric tasks and 64.0% on threshold-level tasks.
MOJO (Masked autOencoder-based JOint training) framework for decoding neural population activity using self-supervised learning with unlabelled data. Use when: working with limited labelled neural data, needing cross-session generalization, or wanting to…
Universal BCI Personalization API for trunk-agnostic EEG foundation model integration. Provides one contract encode to Bayesian head to BrainState architecture that works across heterogeneous frozen EEG trunks without per-architecture personalization stacks.…
Framework for designing control systems that govern not only goal pursuit but also goal appropriateness in changing environments. Implements two-timescale control with fast regulation loop and slow goal governance loop.
Graph-Based Correlation Matrix Generation using convex optimization for controlled sparsity and mean off-diagonal values. Use when generating realistic correlation matrices for neuroscience, finance, or other domains requiring graph-structured correlations…
Interpretable EEG biomarkers with bag-of-waves: Spatial and temporal waveform dictionaries for low-data regimes. Use when analyzing EEG data in low-data scenarios, needing interpretable biomarkers, or working with clinical EEG classification. Activation:…
Cross-Tokenizer On-Policy Distillation framework using byte-prefix marginalization to enable knowledge transfer between models with different tokenizers while preserving policy quality.
IFCLoRA framework for topology-aware rank allocation in parameter-efficient fine-tuning, dynamically allocating LoRA ranks based on model architecture topology and task requirements.
LeAct framework for recovering chain-of-thought reasoning from expert systems that only produce actions without explicit reasoning traces, treating CoT as a latent variable optimized via action probability scoring.
Framework for teaching LLMs to self-evolve by cultivating core meta-skills with reinforcement learning, enabling autonomous capability expansion through iterative self-improvement cycles.
Methodology for optimizing spiking neural network simulation performance by managing NUMA balancing settings on HPC systems.
BrainAgent agentic LLM framework for knowledge-enhanced brain network analysis. Reformulates connectome classification as iterative topology-aware understanding, external retrieval, reasoning, and reflection. Use when analyzing brain networks with LLMs for…
NUMA balancing performance optimization for spiking network simulations on HPC systems. Identifies that automatic NUMA balancing can reduce energy efficiency by 30% in spiking network simulations and provides methodology for per-job NUMA balancing control to…
Novel coupling methodology for Rulkov neural maps preserving chaos and generating strange attractors
Local synaptic learning rules (STDP+ and homeostatic plasticity) can implement exact SIGReg-like self-supervised learning gradients without backpropagation, global error signals, or weight transport.
Automated computer vision based ICA rejection labeling tool for EEG analysis that reduces processing time by 7200 fold and achieves 89.45% accuracy
Computer vision based automated ICA rejection for EEG artifact removal with 89.45% accuracy and 7200x speedup over manual inspection. Compatible with ICLabel and EEGLab interfaces.