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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Dynamic Sampling Non Stationary Spontaneous ActivityAdaptive 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.Votes: 0GitHub stars: 3
- Dynamic Spiking Framework For Graph Neural Networks**arXiv ID:** 2401.05373 **Authors:** Nan Yin, Mengzhu Wang, Zhenghan Chen, Giulia De Masi, Bin Gu, Huan Xiong **Published:** 2023-12-15T12:45:47Z **Abstract:** The integration of Spiking Neural Networks (SNNs) and Graph Neural Networks (GNNs) is gradually attracting attention due to the low power consumption and high efficiency in processing the non-Euclidean data represented by graphs. However, as a common problem, dynamic graph representation learning faces challenges such as high complexi...Votes: 0GitHub stars: 3
- E SplE-SPLVotes: 0GitHub stars: 3
- Echo State Queueing Network A New Reservoir Computing Learning Tool**arXiv ID:** 1212.6276 **Authors:** Sebastián Basterrech, Gerardo Rubino **Published:** 2012-12-26T22:31:13Z **Abstract:** In the last decade, a new computational paradigm was introduced in the field of Machine Learning, under the name of Reservoir Computing (RC). RC models are neural networks which a recurrent part (the reservoir) that does not participate in the learning process, and the rest of the system where no recurrence (no neural circuit) occurs. This approach has grown rapidly due ...Votes: 0GitHub stars: 3
- Efficient Learning Of Locomotion Skills Through The Discovery Of Diverse Environmental Trajectory Generator Priors**arXiv ID:** 2210.04819 **Authors:** Shikha Surana, Bryan Lim, Antoine Cully **Published:** 2022-10-10T16:31:11Z **Abstract:** Data-driven learning based methods have recently been particularly successful at learning robust locomotion controllers for a variety of unstructured terrains. Prior work has shown that incorporating good locomotion priors in the form of trajectory generators (TGs) is effective at efficiently learning complex locomotion skills. However, defining a good, single TG as ...Votes: 0GitHub stars: 3
- Efficient Methods For Unsupervised Learning Of Probabilistic Models**arXiv ID:** 1205.4295 **Authors:** Jascha Sohl-Dickstein **Published:** 2012-05-19T04:25:04Z **Abstract:** In this thesis I develop a variety of techniques to train, evaluate, and sample from intractable and high dimensional probabilistic models. Abstract exceeds arXiv space limitations -- see PDF.Votes: 0GitHub stars: 3
- Eliminating Meta Optimization Through Selfreferential Meta Learning**arXiv ID:** 2212.14392 **Authors:** Louis Kirsch, Jürgen Schmidhuber **Published:** 2022-12-29T17:53:40Z **Abstract:** Meta Learning automates the search for learning algorithms. At the same time, it creates a dependency on human engineering on the meta-level, where meta learning algorithms need to be designed. In this paper, we investigate self-referential meta learning systems that modify themselves without the need for explicit meta optimization. We discuss the relationship of such syste...Votes: 0GitHub stars: 3
- Emergence Of Novelty In Evolutionary Algorithms**arXiv ID:** 2207.04857 **Authors:** David Herel, Dominika Zogatova, Matej Kripner, Tomas Mikolov **Published:** 2022-06-27T13:49:41Z **Abstract:** One of the main problems of evolutionary algorithms is the convergence of the population to local minima. In this paper, we explore techniques that can avoid this problem by encouraging a diverse behavior of the agents through a shared reward system. The rewards are randomly distributed in the environment, and the agents are only rewarded for col...Votes: 0GitHub stars: 3
- Empirical Learning Aided By Weak Domain Knowledge In The Form Of Feature Importance**arXiv ID:** 1005.5556 **Authors:** Ridwan Al Iqbal **Published:** 2010-05-30T19:28:01Z **Abstract:** Standard hybrid learners that use domain knowledge require stronger knowledge that is hard and expensive to acquire. However, weaker domain knowledge can benefit from prior knowledge while being cost effective. Weak knowledge in the form of feature relative importance (FRI) is presented and explained. Feature relative importance is a real valued approximation of a feature's importance provid...Votes: 0GitHub stars: 3
- Encodings For Predictionbased Neural Architecture Search**arXiv ID:** 2403.02484 **Authors:** Yash Akhauri, Mohamed S. Abdelfattah **Published:** 2024-03-04T21:05:52Z **Abstract:** Predictor-based methods have substantially enhanced Neural Architecture Search (NAS) optimization. The efficacy of these predictors is largely influenced by the method of encoding neural network architectures. While traditional encodings used an adjacency matrix describing the graph structure of a neural network, novel encodings embrace a variety of approaches from unsu...Votes: 0GitHub stars: 3
- Engineered Ordinary Differential Equations As Classification Algorithm Eodeca Thorough Characterization And Testing**arXiv ID:** 2312.14681 **Authors:** Raffaele Marino, Lorenzo Buffoni, Lorenzo Chicchi, Lorenzo Giambagli, Duccio Fanelli **Published:** 2023-12-22T13:34:18Z **Abstract:** EODECA (Engineered Ordinary Differential Equations as Classification Algorithm) is a novel approach at the intersection of machine learning and dynamical systems theory, presenting a unique framework for classification tasks [1]. This method stands out with its dynamical system structure, utilizing ordinary differential eq...Votes: 0GitHub stars: 3
- Enhancing Genetic Algorithms With Graph Neural Networks A Timetabling Case Study**arXiv ID:** 2602.08619 **Authors:** Laura-Maria Cornei, Mihaela-Elena Breabăn **Published:** 2026-02-09T13:10:16Z **Abstract:** This paper investigates the impact of hybridizing a multi-modal Genetic Algorithm with a Graph Neural Network for timetabling optimization. The Graph Neural Network is designed to encapsulate general domain knowledge to improve schedule quality, while the Genetic Algorithm explores different regions of the search space and integrates the deep learning model as an e...Votes: 0GitHub stars: 3
- Environment Free Synthetic Data Generation Api Calling AgentsSkill derived from arXiv:2607.16900 - Environment-free Synthetic Data Generation for API-Calling AgentsVotes: 0GitHub stars: 3
- Environment Free Synthetic Data Generation For ApiDerived from arXiv:2607.16900 - Environment-free Synthetic Data Generation for API-Calling AgentsVotes: 0GitHub stars: 3
- Equation Asymmetry Information SecurityEquation Asymmetry Degree (EAD) framework for unifying secrecy and covertness in information-theoretic security. EAD = 1 - r/n governs both equivocation and detection error probability. Applies to MIMO wiretap, secure network coding, FRFT multi-angle transmission, traffic steganography, post-quantum security. Use when analyzing information-theoretic security, secrecy capacity, covertness, or designing secure communication protocols.Votes: 0GitHub stars: 3
- Estimation Aware ControlEstimation-Aware (EA) control paradigm for underactuated nonlinear systems — incorporates estimation quality into feedback law to isolate estimation-induced loops. Mitigates structural coupling between estimation and tracking dynamics. Validated on quadrotor flight at 57.6 km/h with 39% bandwidth extension and 55% stability margin improvement.Votes: 0GitHub stars: 3
- Euclid Mcp A Model Context Protocol Server ForEuclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via PrologVotes: 0GitHub stars: 3
- Evaluation Of A Treebased Pipeline Optimization Tool For Automating Data Science**arXiv ID:** 1603.06212 **Authors:** Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, Jason H. Moore **Published:** 2016-03-20T13:32:27Z **Abstract:** As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement an open source T...Votes: 0GitHub stars: 3
- Evidence Before Expansion Reuse Spawn Or Defer In Lifelong Expert Pools**arXiv ID:** 2608.19888 **Authors:** Kentaro Oda **Published:** 2026-08-20T10:54:17Z **Abstract:** Streaming systems that maintain a pool of expert models must repeatedly decide whether to reuse an existing expert for arriving data, spawn a new one, or defer. We present a decision layer that makes all three outcomes statistically meaningful. Reuse and spawn are posed as one-sided sequential hypotheses on a conditional (mechanism-level) discrepancy, separated by an indifference zone; defer is...Votes: 0GitHub stars: 3
- Evofed Leveraging Evolutionary Strategies For Communicationefficient Federated Learning**arXiv ID:** 2311.07485 **Authors:** Mohammad Mahdi Rahimi, Hasnain Irshad Bhatti, Younghyun Park, Humaira Kousar, Jaekyun Moon **Published:** 2023-11-13T17:25:06Z **Abstract:** Federated Learning (FL) is a decentralized machine learning paradigm that enables collaborative model training across dispersed nodes without having to force individual nodes to share data. However, its broad adoption is hindered by the high communication costs of transmitting a large number of model parameters. This...Votes: 0GitHub stars: 3
- Evolutionary Discovery Of Developmental Reward SchDerived from arXiv:2606.20858 - Evolutionary Discovery of Developmental Reward Schedules in Deep Reinforcement LearningVotes: 0GitHub stars: 3
- Evolutionary Hyperparameter Optimization To Find LSkill derived from arXiv paper 2606.29684: Evolutionary Hyperparameter Optimization to Find Lightweight CNN Models for Autonomous SteeringVotes: 0GitHub stars: 3
- Evolutionary Multiobjective Optimization Driven By Generative Adversarial Networks**arXiv ID:** 1907.04482 **Authors:** Cheng He, Shihua Huang, Ran Cheng, Kay Chen Tan, Yaochu Jin **Published:** 2019-07-10T01:50:20Z **Abstract:** Recently, more and more works have proposed to drive evolutionary algorithms using machine learning models.Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models.Since it usually requires a certain amount of data (i.e. the candidate solutions generated by the algorit...Votes: 0GitHub stars: 3
- Evolutionbased Feature Selection For Predicting Dissolved Oxygen Concentrations In Lakes**arXiv ID:** 2403.18923 **Authors:** Runlong Yu, Robert Ladwig, Xiang Xu, Peijun Zhu, Paul C. Hanson, Yiqun Xie, Xiaowei Jia **Published:** 2024-02-15T20:27:33Z **Abstract:** Accurate prediction of dissolved oxygen (DO) concentrations in lakes requires a comprehensive study of phenological patterns across ecosystems, highlighting the need for precise selection of interactions amongst external factors and internal physical-chemical-biological variables. This paper presents the Multi-populatio...Votes: 0GitHub stars: 3
- Evolved Sample Weights For Bias Mitigation Effectiveness Depends On The Fairness Objective**arXiv ID:** 2511.20909 **Authors:** Anil K. Saini, Jose Guadalupe Hernandez, Emily F. Wong, Debanshi Misra, Tiffani J. Bright, Jason H. Moore **Published:** 2025-11-25T22:50:59Z **Abstract:** Machine learning models trained on real-world data may inadvertently make biased predictions that negatively impact marginalized communities. Reweighting, which assigns a weight to each data point used during model training, can mitigate such bias, though sometimes at the cost of predictive accuracy. I...Votes: 0GitHub stars: 3
- Evolving Simgans To Improve Abnormal Electrocardiogram Classification**arXiv ID:** 2205.10116 **Authors:** Gabriel Wang, Anish Thite, Rodd Talebi, Anthony D'Achille, Alex Mussa, Jason Zutty **Published:** 2022-05-12T17:27:38Z **Abstract:** Machine Learning models are used in a wide variety of domains. However, machine learning methods often require a large amount of data in order to be successful. This is especially troublesome in domains where collecting real-world data is difficult and/or expensive. Data simulators do exist for many of these domains, but the...Votes: 0GitHub stars: 3
- Evomerge Neuroevolution For Large Language Models**arXiv ID:** 2402.00070 **Authors:** Yushu Jiang **Published:** 2024-01-30T19:37:21Z **Abstract:** Extensive fine-tuning on Large Language Models does not always yield better results. Oftentimes, models tend to get better at imitating one form of data without gaining greater reasoning ability and may even end up losing some intelligence. Here I introduce EvoMerge, a systematic approach to large language model training and merging. Leveraging model merging for weight crossover and fine-tuning...Votes: 0GitHub stars: 3
- Evothink Evolving Thinking In Large Reasoning ModeSkill generated from arXiv paper 2607.19962: EvoThink: Evolving Thinking in Large Reasoning Models via Self-Pruning and Aha-Moment Preference OptimizationVotes: 0GitHub stars: 3
- Expressivity Trainability Dla QmlDynamical Lie Algebra (DLA) framework for navigating the expressivity-trainability paradox in QML - using group-theoretic geometric priors as structural regularizers to guarantee scalable, gradient-rich training landscapes.Votes: 0GitHub stars: 3
- Fast Exploration Of Weight Sharing Opportunities For Cnn Compression**arXiv ID:** 2102.01345 **Authors:** Etienne Dupuis, David Novo, Ian O'Connor, Alberto Bosio **Published:** 2021-02-02T06:45:56Z **Abstract:** The computational workload involved in Convolutional Neural Networks (CNNs) is typically out of reach for low-power embedded devices. There are a large number of approximation techniques to address this problem. These methods have hyper-parameters that need to be optimized for each CNNs using design space exploration (DSE). The goal of this work is to...Votes: 0GitHub stars: 3
- Fast Whole Brain Spectralot AlignmentFunctional alignment method for fMRI using SpectralOT to embed cortical geometry into Laplace-Beltrami eigenmodes for cross-subject decodingVotes: 0GitHub stars: 3
- Feature Learning In Featuresample Networks Using Multiobjective Optimization**arXiv ID:** 1710.09300 **Authors:** Filipe Alves Neto Verri, Renato Tinós, Liang Zhao **Published:** 2017-10-25T15:18:27Z **Abstract:** Data and knowledge representation are fundamental concepts in machine learning. The quality of the representation impacts the performance of the learning model directly. Feature learning transforms or enhances raw data to structures that are effectively exploited by those models. In recent years, several works have been using complex networks for data repre...Votes: 0GitHub stars: 3
- Feature Weight Tuning For Recursive Neural Networks**arXiv ID:** 1412.3714 **Authors:** Jiwei Li **Published:** 2014-12-11T16:35:27Z **Abstract:** This paper addresses how a recursive neural network model can automatically leave out useless information and emphasize important evidence, in other words, to perform "weight tuning" for higher-level representation acquisition. We propose two models, Weighted Neural Network (WNN) and Binary-Expectation Neural Network (BENN), which automatically control how much one specific unit contributes to the ...Votes: 0GitHub stars: 3
- Feynman Clock Error MitigationBBGKY-ISM quantum error mitigation using Feynman's clock Hamiltonian with polynomial overhead (arXiv: 2607.06752)Votes: 0GitHub stars: 3
- Fifa World Cup 2026 As A Contamination Free BenchmDerived from arXiv:2607.17765 - FIFA World Cup 2026 as a Contamination-Free Benchmark for LLM Forecasting Agents: Four Models, a Bookmaker, and 104 MatchesVotes: 0GitHub stars: 3
- Finite Reliability RepresentationsFinite Reliability Representations (FRR) methodology for noise-calibrated belief-space covers in decision-making systems. Provides certified suboptimality bounds based on sensing, process, and actuation noise. Use when designing reliable decision systems, POMDP policies, or safety-critical control.Votes: 0GitHub stars: 3
- Fitness Landscape Footprint A Framework To Compare Neural Architecture Search Problems**arXiv ID:** 2111.01584 **Authors:** Kalifou René Traoré, Andrés Camero, Xiao Xiang Zhu **Published:** 2021-11-02T13:20:01Z **Abstract:** Neural architecture search is a promising area of research dedicated to automating the design of neural network models. This field is rapidly growing, with a surge of methodologies ranging from Bayesian optimization,neuroevoltion, to differentiable search, and applications in various contexts. However, despite all great advances, few studies have presented...Votes: 0GitHub stars: 3
- Fmrp Lean A Hipaa Compliant Ai Augmented Lims ArchSkill generated from arXiv paper 2607.20382: FMRP-LEAN: A HIPAA-Compliant AI-Augmented LIMS Architecture for End-to-End Clinical Assay Workflow OptimizationVotes: 0GitHub stars: 3
- Forecasting Of Nonstationary Sales Time Series Using Deep Learning**arXiv ID:** 2205.11636 **Authors:** Bohdan M. Pavlyshenko **Published:** 2022-05-23T21:06:27Z **Abstract:** The paper describes the deep learning approach for forecasting non-stationary time series with using time trend correction in a neural network model. Along with the layers for predicting sales values, the neural network model includes a subnetwork block for the prediction weight for a time trend term which is added to a predicted sales value. The time trend term is considered as a pro...Votes: 0GitHub stars: 3
- Free Probability Rnn Spectral AnalysisFree probability approach to analyzing stationary covariance spectra of random recurrent neural networks. Derives closed functional equations for moment generating functions of limiting stationary covariance spectra with random non-normal Gaussian weights.Votes: 0GitHub stars: 3
- From Conceptual Hydrologic Models To ConceptuallyFrom Conceptual Hydrologic Models to Conceptually Interpretable Neural Networks: A Snow-Water Mass-CVotes: 0GitHub stars: 3
- From Data To Actions In Intelligent Transportation Systems A Prescription Of Functional Requirements For Model Actionability**arXiv ID:** 2002.02210 **Authors:** Ibai Lana, Javier J. Sanchez-Medina, Eleni I. Vlahogianni, Javier Del Ser **Published:** 2020-02-06T12:02:30Z **Abstract:** Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that {are} data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a ``story'' intensively producing and consuming large amounts of data. A~diversity o...Votes: 0GitHub stars: 3
- From Observation To Prediction Lstm For Vehicle Lane Change Forecasting On Highway Onofframps**arXiv ID:** 2601.14848 **Authors:** Mohamed Abouras, Catherine M. Elias **Published:** 2026-01-21T10:31:03Z **Abstract:** On and off-ramps are understudied road sections even though they introduce a higher level of variation in highway interactions. Predicting vehicles' behavior in these areas can decrease the impact of uncertainty and increase road safety. In this paper, the difference between this Area of Interest (AoI) and a straight highway section is studied. Multi-layered LSTM archite...Votes: 0GitHub stars: 3
- Generalization Bounds For Deep Learning**arXiv ID:** 2012.04115 **Authors:** Guillermo Valle-Pérez, Ard A. Louis **Published:** 2020-12-07T23:45:09Z **Abstract:** Generalization in deep learning has been the topic of much recent theoretical and empirical research. Here we introduce desiderata for techniques that predict generalization errors for deep learning models in supervised learning. Such predictions should 1) scale correctly with data complexity; 2) scale correctly with training set size; 3) capture differences between arch...Votes: 0GitHub stars: 3
- Generalize And Guide Decomposing Rewards For Few SDerived from arXiv:2607.17760 - Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement LearningVotes: 0GitHub stars: 3
- Generative Adversarial Network Rooms In Generative Graph Grammar Dungeons For The Legend Of Zelda**arXiv ID:** 2001.05065 **Authors:** Jake Gutierrez, Jacob Schrum **Published:** 2020-01-14T22:22:11Z **Abstract:** Generative Adversarial Networks (GANs) have demonstrated their ability to learn patterns in data and produce new exemplars similar to, but different from, their training set in several domains, including video games. However, GANs have a fixed output size, so creating levels of arbitrary size for a dungeon crawling game is difficult. GANs also have trouble encoding semantic req...Votes: 0GitHub stars: 3
- Genplusss A Genetic Algorithm Based Plugin For Measured Subsurface Scattering Representation**arXiv ID:** 2401.15245 **Authors:** Barış Yıldırım, Murat Kurt **Published:** 2024-01-26T23:31:53Z **Abstract:** This paper presents a plugin that adds a representation of homogeneous and heterogeneous, optically thick, translucent materials on the Blender 3D modeling tool. The working principle of this plugin is based on a combination of Genetic Algorithm (GA) and Singular Value Decomposition (SVD)-based subsurface scattering method (GenSSS). The proposed plugin has been implemented using ...Votes: 0GitHub stars: 3
- Geometric Decoherence Time LindbladianGeometric decoherence time methodology for open many-body quantum systems — defines the earliest moment logarithmic negativity and Rényi-1/2 entropy relation breaks down under open-system evolution. Use when: analyzing decoherence in open quantum systems, Lindbladian dynamics, entanglement decay, quantum mutual information diagnostics, topological phase coherence.Votes: 0GitHub stars: 3
- Geometric Laplace Transform SystemsGeometric Algebra Laplace transforms for system analysis.Votes: 0GitHub stars: 3
- Getting Aligned On Representational Alignment**arXiv ID:** 2310.13018 **Authors:** Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller, Andi Peng, Andreea Bobu, Been Kim, Bradley C. Love, Christopher J. Cueva, Erin Grant, Iris Groen, Jascha Achterberg, Joshua B. Tenenbaum, Katherine M. Collins, Katherine L. Hermann, Kerem Oktar, Klaus Greff, Martin N. Hebart, Nathan Cloos, Nikolaus Kriegeskorte, Nori Jacoby, Qiuyi Zhang, Raja Marjieh, Robert Geirhos, Sherol Chen, Simon Kornblith, Sunayana Rane, Talia Konkle, Thomas P. O'Connell, Thomas ...Votes: 0GitHub stars: 3