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Research, evidence gathering, literature, reports, investigation, and synthesis
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- Meta Learning For WrestlingSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Memoir Memory Rewriting Neural NetworksMethodology for analyzing the effects of memory rewriting during neural network inference, comparing coupled vs read-only pondering architectures.Votes: 0GitHub stars: 3
- Medical Domain AdaptationMedical image domain adaptation and transfer learning methodology. Use when working with medical imaging AI tasks including: (1) adapting pre-trained models to new clinical domains with scarce annotated data, (2) parameter-efficient fine-tuning for medical image segmentation/classification, (3) handling domain shift between different medical imaging sites/modalities, (4) federated learning for medical images across institutions. Covers RKHS-MMD, PEFT, MedSR, and imbalanced classification in m...Votes: 0GitHub stars: 3
- Margin Runtime Confidence Calibration"Multi-Agent Runtime Grading via Incremental Normalization (MARGIN) — online confidence calibration for multi-agent AI coordination. Use when building multi-agent systems that need to weight agent trustworthiness at runtime: (1) coordinating responses from multiple foundation models, (2) selecting which agent's output to trust when self-reported confidence is unreliable, (3) calibrating confidence under distribution shift without held-out data or retraining.Votes: 0GitHub stars: 3
- Magic Number Theoretic ComplexityAnalyze quantum algorithms through the lens of magic (non-stabilizerness) and number-theoretic complexity. Covers the resource-theoretic framework for quantifying genuinely quantum resources in quantum algorithms, particularly Shor's factoring algorithm. Use when: (1) analyzing quantum algorithm resource requirements beyond gate counts, (2) studying the connection between classical computational hardness and quantum resource consumption, (3) evaluating magic state requirements for fault-toler...Votes: 0GitHub stars: 3
- Linear Reservoir Computing BottleneckMethodology for analyzing information processing capacity limits in linear reservoir computing systems and identifying quantum advantages in reservoir computing.Votes: 0GitHub stars: 3
- Learning To Play Minecraft With Video PretrainingSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Learning To Model Other MindsSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Learning To Cooperate Compete And CommunicateSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Learning To CommunicateSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- L System Neural Network EvolutionL-System genetic encoding methodology for scalable neural network evolution. Uses Lindenmayer system grammar to encode neural networks, enabling compact representation and efficient evolutionary search. Applies to: neuroevolution, scalable network encoding, genetic algorithms, neural architecture search. Activation: L-system neural encoding, Lindenmayer neuroevolution, genetic network encoding, scalable neural evolution, grammar-based NAS.Votes: 0GitHub stars: 3
- Krylov Complexity Analog SimulatorBridging Krylov complexity theory with universal analog quantum simulation — using Lanczos algorithm and Krylov subspace growth to characterize computational power of analog quantum simulators. Activation: Krylov complexity, analog quantum simulator, Lanczos algorithm quantum, operator growth complexity.Votes: 0GitHub stars: 3
- Klr Hopfield Event Driven RetrievalKernel Logistic Regression (KLR) Hopfield Network with asynchronous event-driven retrieval methodology. Enables high-capacity associative memory (P/N ≈ 30, vs classical 0.14N) with neuromorphic-compatible sparse computation. Use when: designing associative memory systems, event-driven neuromorphic hardware deployment, comparing Hopfield variants (KLR vs MHN), analyzing attractor landscapes, kernel-based neural networks, or studying asynchronous vs synchronous retrieval dynamics in neural comp...Votes: 0GitHub stars: 3
- Jost Function Analytic OdeMethodology for analyzing analytic properties of Jost functions in quantum scattering theory via parameter-dependent ODEs (Poincare-Picard theorem). Applies to scattering matrix analytic continuation, complex energy plane analysis, and quantum scattering problems with short-range potentials. Bridges mathematical analysis (ODE theory, complex analysis) with quantum physics. Activation: jost function, quantum scattering theory, analytic continuation scattering matrix, Poincare-Picard theorem, p...Votes: 0GitHub stars: 3
- Intrinsic Noise Consolidation Continual LearningDoob-barrier-conditioned diffusion methodology for turning analog neuromorphic hardware noise into a continual-learning resource — per-synapse consolidation via Doob h-transform creates noise-amplified restoring force that yields inverted-U noise-retention relationship. Validated on BrainScaleS-2 neuromorphic silicon with hardware-in-the-loop training.Votes: 0GitHub stars: 3
- Interpretable Machine Learning Through TeachingSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Infrastructure For Deep LearningSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Implicit Regularization With Polynomial Growth In Deep Tensor Factorization**arXiv ID:** 2207.08942 **Authors:** Kais Hariz, Hachem Kadri, Stéphane Ayache, Maher Moakher, Thierry Artières **Published:** 2022-07-18T21:04:37Z **Abstract:** We study the implicit regularization effects of deep learning in tensor factorization. While implicit regularization in deep matrix and 'shallow' tensor factorization via linear and certain type of non-linear neural networks promotes low-rank solutions with at most quadratic growth, we show that its effect in deep tensor factorizati...Votes: 0GitHub stars: 3
- Hypergeometric High Precision EvaluationMethodology for high-precision numerical evaluation of multivariate hypergeometric functions using Pfaffian systems and contour restriction. Applicable to quantum field theory, string theory, number theory, and statistics computations.Votes: 0GitHub stars: 3
- Hes Data Selection ReasoningHES (High-Entropy Sum) methodology from arXiv:2605.22389 (May 2026). Training-free metric for LLM reasoning data selection: sums entropy of top-k highest-entropy tokens per reasoning sample. Effective across SFT, RFT, and RL training paradigms. Use when: LLM reasoning data curation, data quality filtering, rejection sampling for reasoning, RL training data selection, long-CoT data filtering.Votes: 0GitHub stars: 3
- Hebbian Fast Weights VitHebbian Fast-Weight (HFW) modules integrated into Vision Transformer architectures for few-shot learning. Activation triggers: hebbian fast weights, hebbian ViT, fast synaptic updates, transformer meta-learning, few-shot transformer, hebbian plasticity vision, swin hebbian, prototypical network hebbian, rapid adaptation transformer.Votes: 0GitHub stars: 3
- Growing Neural Network Breadth Depth TimeDifferentiable cost framework for jointly optimizing neural network architecture (breadth/width, depth/layers, temporal recurrence) alongside task performance. Bio-inspired growth principle enabling networks to autonomously develop architectures matching task complexity — mimicking biological neural development.Votes: 0GitHub stars: 3
- Group Intervention Causal Discovery SubsystemsGroup intervention-based causal discovery for identifying causal structure in deep neural network subsystems — extending causal discovery from single neurons to functional subnetwork groups. Activation: causal discovery, deep network, subsystem, group intervention, causal structure, neural circuit, interpretability, interventional causality.Votes: 0GitHub stars: 3
- Gradientbased Design Of Computational Granular Crystals**arXiv ID:** 2404.04825 **Authors:** Atoosa Parsa, Corey S. O'Hern, Rebecca Kramer-Bottiglio, Josh Bongard **Published:** 2024-04-07T06:24:47Z **Abstract:** There is growing interest in engineering unconventional computing devices that leverage the intrinsic dynamics of physical substrates to perform fast and energy-efficient computations. Granular metamaterials are one such substrate that has emerged as a promising platform for building wave-based information processing devices with the pot...Votes: 0GitHub stars: 3