All authors
Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Arxiv 2609 10494v1 Ibib A Protocol For Measuring Enterprise Ai System**arXiv ID:** 2609.10494v1 **Authors:** Blake Stenstrom, Charangan Vasantharajan, Brian Sathianathan **URL:** http://arxiv.org/abs/2609.10494v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 10518v1 Braintaskonomy Learning How To Pretrain And What T**arXiv ID:** 2609.10518v1 **Authors:** Junfeng Xia, Wenhao Ye, Junxiang Zhang, Jiayu Zuo, Mo Wang, Quanying Liu **URL:** http://arxiv.org/abs/2609.10518v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv Safe AccessBest practices for safely accessing arXiv programmatically, avoiding common pitfalls with HTTP, SSL, and automated tools.Votes: 0GitHub stars: 3
- Arxiv SearcharXiv paper search skill - search academic papers by keywords, authors, categories. Supports time filtering, category filtering, and paper detail retrieval. Activation: arxiv search, paper search, 论文搜索, search papers, arxiv 论文.Votes: 0GitHub stars: 3
- Assemblies Of Neurons Learn To Classify Wellseparated Distributions**arXiv ID:** 2110.03171 **Authors:** Max Dabagia, Christos H. Papadimitriou, Santosh S. Vempala **Published:** 2021-10-07T03:53:39Z **Abstract:** An assembly is a large population of neurons whose synchronous firing is hypothesized to represent a memory, concept, word, and other cognitive categories. Assemblies are believed to provide a bridge between high-level cognitive phenomena and low-level neural activity. Recently, a computational system called the Assembly Calculus (AC), with a reper...Votes: 0GitHub stars: 3
- Assessing The Scalability Of Biologicallymotivated Deep Learning Algorithms And Architectures**arXiv ID:** 1807.04587 **Authors:** Sergey Bartunov, Adam Santoro, Blake A. Richards, Luke Marris, Geoffrey E. Hinton, Timothy Lillicrap **Published:** 2018-07-12T12:53:50Z **Abstract:** The backpropagation of error algorithm (BP) is impossible to implement in a real brain. The recent success of deep networks in machine learning and AI, however, has inspired proposals for understanding how the brain might learn across multiple layers, and hence how it might approximate BP. As of yet, none o...Votes: 0GitHub stars: 3
- Audited Skill Graph Self ImprovementAudited Skill-Graph Self-ImprovementVotes: 0GitHub stars: 3
- Automated Dynamic Algorithm Configuration**arXiv ID:** 2205.13881 **Authors:** Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor Awad, Theresa Eimer, Marius Lindauer, Frank Hutter **Published:** 2022-05-27T10:30:25Z **Abstract:** The performance of an algorithm often critically depends on its parameter configuration. While a variety of automated algorithm configuration methods have been proposed to relieve users from the tedious and error-prone task of manually tuning parameters, there is still a lot of untapped potential as th...Votes: 0GitHub stars: 3
- Automatic Pattern Classification By Unsupervised Learning Using Dimensionality Reduction Of Data With Mirroring Neural Networks**arXiv ID:** 0712.0938 **Authors:** Dasika Ratna Deepthi, G. R. Aditya Krishna, K. Eswaran **Published:** 2007-12-06T13:52:04Z **Abstract:** This paper proposes an unsupervised learning technique by using Multi-layer Mirroring Neural Network and Forgy's clustering algorithm. Multi-layer Mirroring Neural Network is a neural network that can be trained with generalized data inputs (different categories of image patterns) to perform non-linear dimensionality reduction and the resultant low-dime...Votes: 0GitHub stars: 3
- Automating Rigid Origami Design**arXiv ID:** 2211.13219 **Authors:** Jeremia Geiger, Karolis Martinkus, Oliver Richter, Roger Wattenhofer **Published:** 2022-11-20T17:13:50Z **Abstract:** Rigid origami has shown potential in large diversity of practical applications. However, current rigid origami crease pattern design mostly relies on known tessellations. This strongly limits the diversity and novelty of patterns that can be created. In this work, we build upon the recently developed principle of three units method to for...Votes: 0GitHub stars: 3
- Autonomous Learning And Chaining Of Motor Primitives Using The Free Energy Principle**arXiv ID:** 2005.05151 **Authors:** Louis Annabi, Alexandre Pitti, Mathias Quoy **Published:** 2020-05-11T14:43:55Z **Abstract:** In this article, we apply the Free-Energy Principle to the question of motor primitives learning. An echo-state network is used to generate motor trajectories. We combine this network with a perception module and a controller that can influence its dynamics. This new compound network permits the autonomous learning of a repertoire of motor trajectories. To evalua...Votes: 0GitHub stars: 3
- Autotandemml Active Learning Enhanced Tandem Neural Networks For Inverse Design Problems**arXiv ID:** 2502.15643 **Authors:** Luka Grbcic, Juliane Müller, Wibe Albert de Jong **Published:** 2025-02-21T18:10:56Z **Abstract:** Inverse design in science and engineering involves determining optimal design parameters that achieve desired performance outcomes, a process often hindered by the complexity and high dimensionality of design spaces, leading to significant computational costs. To tackle this challenge, we propose a novel hybrid approach that combines active learning with Tan...Votes: 0GitHub stars: 3
- Bad And Good Errors Valueweighted Skill Scores In Deep Ensemble Learning**arXiv ID:** 2103.02881 **Authors:** Sabrina Guastavino, Michele Piana, Federico Benvenuto **Published:** 2021-03-04T08:05:13Z **Abstract:** In this paper we propose a novel approach to realize forecast verification. Specifically, we introduce a strategy for assessing the severity of forecast errors based on the evidence that, on the one hand, a false alarm just anticipating an occurring event is better than one in the middle of consecutive non-occurring events, and that, on the other hand, ...Votes: 0GitHub stars: 3
- Bayesian Deep Convolutional Networks With Many Channels Are Gaussian Processes**arXiv ID:** 1810.05148 **Authors:** Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Greg Yang, Jiri Hron, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein **Published:** 2018-10-11T17:49:41Z **Abstract:** There is a previously identified equivalence between wide fully connected neural networks (FCNs) and Gaussian processes (GPs). This equivalence enables, for instance, test set predictions that would have resulted from a fully Bayesian, infinitely wide trained FCN to be c...Votes: 0GitHub stars: 3
- Bayesian Optimization With Automatic Prior Selection For Dataefficient Direct Policy Search**arXiv ID:** 1709.06919 **Authors:** Rémi Pautrat, Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret **Published:** 2017-09-20T15:04:50Z **Abstract:** One of the most interesting features of Bayesian optimization for direct policy search is that it can leverage priors (e.g., from simulation or from previous tasks) to accelerate learning on a robot. In this paper, we are interested in situations for which several priors exist but we do not know in advance which one fits best the current sit...Votes: 0GitHub stars: 3
- Bcijelly Integrated Ecosystem BciBCIJelly: integrated BCI research ecosystem.Votes: 0GitHub stars: 3
- Benchmarking Randomized Optimization Algorithms On Binary Permutation And Combinatorial Problem Landscapes**arXiv ID:** 2501.17170 **Authors:** Jethro Odeyemi, Wenjun Zhang **Published:** 2025-01-21T23:13:01Z **Abstract:** In this paper, we evaluate the performance of four randomized optimization algorithms: Randomized Hill Climbing (RHC), Simulated Annealing (SA), Genetic Algorithms (GA), and MIMIC (Mutual Information Maximizing Input Clustering), across three distinct types of problems: binary, permutation, and combinatorial. We systematically compare these algorithms using a set of benchmark f...Votes: 0GitHub stars: 3
- Beyond Memory Leaderboards Evaluating Scientific MDerived from arXiv:2607.16848 - Beyond Memory Leaderboards: Evaluating Scientific Memory as Budgeted Context RestorationVotes: 0GitHub stars: 3
- Beyond Monte Carlo Tree Search Playing Go With Deep Alternative Neural Network And Longterm Evaluation**arXiv ID:** 1706.04052 **Authors:** Jinzhuo Wang, Wenmin Wang, Ronggang Wang, Wen Gao **Published:** 2017-06-13T13:30:04Z **Abstract:** Monte Carlo tree search (MCTS) is extremely popular in computer Go which determines each action by enormous simulations in a broad and deep search tree. However, human experts select most actions by pattern analysis and careful evaluation rather than brute search of millions of future nteractions. In this paper, we propose a computer Go system that follows ...Votes: 0GitHub stars: 3
- Beyond Uniform Scaling Exploring Depth Heterogeneity In Neural Architectures**arXiv ID:** 2402.12418 **Authors:** Akash Guna R. T, Arnav Chavan, Deepak Gupta **Published:** 2024-02-19T09:52:45Z **Abstract:** Conventional scaling of neural networks typically involves designing a base network and growing different dimensions like width, depth, etc. of the same by some predefined scaling factors. We introduce an automated scaling approach leveraging second-order loss landscape information. Our method is flexible towards skip connections a mainstay in modern vision trans...Votes: 0GitHub stars: 3
- Biologicallyplausible Learning Algorithms Can Scale To Large Datasets**arXiv ID:** 1811.03567 **Authors:** Will Xiao, Honglin Chen, Qianli Liao, Tomaso Poggio **Published:** 2018-11-08T17:43:59Z **Abstract:** The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways. To address this "weight transport problem" (Grossberg, 1987), two more biologically plausible algorithms, proposed by Liao et al. (2016) and Lillicr...Votes: 0GitHub stars: 3
- Biomaker Ca A Biome Maker Project Using Cellular Automata**arXiv ID:** 2307.09320 **Authors:** Ettore Randazzo, Alexander Mordvintsev **Published:** 2023-07-18T15:03:40Z **Abstract:** We introduce Biomaker CA: a Biome Maker project using Cellular Automata (CA). In Biomaker CA, morphogenesis is a first class citizen and small seeds need to grow into plant-like organisms to survive in a nutrient starved environment and eventually reproduce with variation so that a biome survives for long timelines. We simulate complex biomes by means of CA rules in 2...Votes: 0GitHub stars: 3
- Biomimetic Machine Learning Approach For Prediction Of Mechanical Properties Of Additive Friction Stir Deposited Aluminum Alloys Based Walled Structures**arXiv ID:** 2408.05237 **Authors:** Akshansh Mishra **Published:** 2024-08-05T13:27:54Z **Abstract:** This study presents a novel approach to predicting mechanical properties of Additive Friction Stir Deposited (AFSD) aluminum alloy walled structures using biomimetic machine learning. The research combines numerical modeling of the AFSD process with genetic algorithm-optimized machine learning models to predict von Mises stress and logarithmic strain. Finite element analysis was employed to...Votes: 0GitHub stars: 3
- Biophysical Hh Model Extracellular NeurostimulationLearning biophysical Hodgkin-Huxley models from extracellular MEA measurements using differentiable simulation and simulation-based inference. Enables precise neurostimulation prediction from minutes of recording instead of hours of stimulus testing.Votes: 0GitHub stars: 3
- Biophysical Hh Model NeurostimulationLearning biophysical Hodgkin-Huxley models from extracellular MEA data for precise neurostimulation predictionVotes: 0GitHub stars: 3
- Bitwise Neural Networks**arXiv ID:** 1601.06071 **Authors:** Minje Kim, Paris Smaragdis **Published:** 2016-01-22T16:59:01Z **Abstract:** Based on the assumption that there exists a neural network that efficiently represents a set of Boolean functions between all binary inputs and outputs, we propose a process for developing and deploying neural networks whose weight parameters, bias terms, input, and intermediate hidden layer output signals, are all binary-valued, and require only basic bit logic for the feedforwa...Votes: 0GitHub stars: 3
- Bm3d Vs 2layer Onn**arXiv ID:** 2103.03060 **Authors:** Junaid Malik, Serkan Kiranyaz, Mehmet Yamac, Moncef Gabbouj **Published:** 2021-03-04T14:37:23Z **Abstract:** Despite their recent success on image denoising, the need for deep and complex architectures still hinders the practical usage of CNNs. Older but computationally more efficient methods such as BM3D remain a popular choice, especially in resource-constrained scenarios. In this study, we aim to find out whether compact neural networks can learn to p...Votes: 0GitHub stars: 3
- Boosting Ant Colony Optimization Via Solution Prediction And Machine Learning**arXiv ID:** 2008.04213 **Authors:** Yuan Sun, Sheng Wang, Yunzhuang Shen, Xiaodong Li, Andreas T. Ernst, Michael Kirley **Published:** 2020-07-29T13:03:37Z **Abstract:** This paper introduces an enhanced meta-heuristic (ML-ACO) that combines machine learning (ML) and ant colony optimization (ACO) to solve combinatorial optimization problems. To illustrate the underlying mechanism of our ML-ACO algorithm, we start by describing a test problem, the orienteering problem. In this problem, the o...Votes: 0GitHub stars: 3
- Boosting Brain To Image Tribe V2TRIBE v2 数据增强提升脑到图像解码性能方法论。使用大规模预训练编码模型生成合成fMRI数据，在小数据集上实现显著性能提升。Votes: 0GitHub stars: 3
- Bosonic Stellar Rank QecStellar rank as resource measure for bosonic quantum error correction. Designs and benchmarks bosonic codes under finite non-Gaussian resources, revealing noise-adapted code structures and concrete resource thresholds.Votes: 0GitHub stars: 3
- Bounding And Counting Linear Regions Of Deep Neural Networks**arXiv ID:** 1711.02114 **Authors:** Thiago Serra, Christian Tjandraatmadja, Srikumar Ramalingam **Published:** 2017-11-06T19:06:12Z **Abstract:** We investigate the complexity of deep neural networks (DNN) that represent piecewise linear (PWL) functions. In particular, we study the number of linear regions, i.e. pieces, that a PWL function represented by a DNN can attain, both theoretically and empirically. We present (i) tighter upper and lower bounds for the maximum number of linear regio...Votes: 0GitHub stars: 3
- Brain2qwerty V2 Noninvasive DecodingBrain2Qwerty v2: non-invasive MEG sentence decodingVotes: 0GitHub stars: 3
- Breaking Free How To Hack Safety Guardrails In Blackbox Diffusion Models**arXiv ID:** 2402.04699 **Authors:** Shashank Kotyan, Po-Yuan Mao, Pin-Yu Chen, Danilo Vasconcellos Vargas **Published:** 2024-02-07T09:39:29Z **Abstract:** Deep neural networks can be exploited using natural adversarial samples, which do not impact human perception. Current approaches often rely on deep neural networks' white-box nature to generate these adversarial samples or synthetically alter the distribution of adversarial samples compared to the training distribution. In contrast, we ...Votes: 0GitHub stars: 3
- Bridge The Inference Gaps Of Neural Processes Via Expectation Maximization**arXiv ID:** 2501.03264 **Authors:** Qi Wang, Marco Federici, Herke van Hoof **Published:** 2025-01-04T03:28:21Z **Abstract:** The neural process (NP) is a family of computationally efficient models for learning distributions over functions. However, it suffers from under-fitting and shows suboptimal performance in practice. Researchers have primarily focused on incorporating diverse structural inductive biases, \textit{e.g.} attention or convolution, in modeling. The topic of inference subo...Votes: 0GitHub stars: 3
- Bridging Lstm Architecture And The Neural Dynamics During Reading**arXiv ID:** 1604.06635 **Authors:** Peng Qian, Xipeng Qiu, Xuanjing Huang **Published:** 2016-04-22T12:51:11Z **Abstract:** Recently, the long short-term memory neural network (LSTM) has attracted wide interest due to its success in many tasks. LSTM architecture consists of a memory cell and three gates, which looks similar to the neuronal networks in the brain. However, there still lacks the evidence of the cognitive plausibility of LSTM architecture as well as its working mechanism. In th...Votes: 0GitHub stars: 3
- Browser Based Arxiv ResearchBrowser-based arXiv research methodology for reliable paper discovery when API methods fail. Uses browser navigation to arXiv listing pages combined with JavaScript console extraction for structured paper metadata. Use when automated arXiv searches encounter connection issues, security blocks, SSL errors, or rate limiting.Votes: 0GitHub stars: 3
- Calibration Error Estimation Using Fuzzy Binning**arXiv ID:** 2305.00543 **Authors:** Geetanjali Bihani, Julia Taylor Rayz **Published:** 2023-04-30T18:06:14Z **Abstract:** Neural network-based decisions tend to be overconfident, where their raw outcome probabilities do not align with the true decision probabilities. Calibration of neural networks is an essential step towards more reliable deep learning frameworks. Prior metrics of calibration error primarily utilize crisp bin membership-based measures. This exacerbates skew in model proba...Votes: 0GitHub stars: 3
- Calibration Of Neural Networks**arXiv ID:** 2303.10761 **Authors:** Ruslan Vasilev, Alexander D'yakonov **Published:** 2023-03-19T20:27:51Z **Abstract:** Neural networks solving real-world problems are often required not only to make accurate predictions but also to provide a confidence level in the forecast. The calibration of a model indicates how close the estimated confidence is to the true probability. This paper presents a survey of confidence calibration problems in the context of neural networks and provides an em...Votes: 0GitHub stars: 3
- Camus A Framework To Build Formal Specifications For Deep Perception Systems Using Simulators**arXiv ID:** 1911.10735 **Authors:** Julien Girard-Satabin, Guillaume Charpiat, Zakaria Chihani, Marc Schoenauer **Published:** 2019-11-25T07:28:45Z **Abstract:** The topic of provable deep neural network robustness has raised considerable interest in recent years. Most research has focused on adversarial robustness, which studies the robustness of perceptive models in the neighbourhood of particular samples. However, other works have proved global properties of smaller neural networks. Yet,...Votes: 0GitHub stars: 3
- Can A Hebbianlike Learning Rule Be Avoiding The Curse Of Dimensionality In Sparse Distributed Data**arXiv ID:** 2208.12564 **Authors:** Maria Osório, Luís Sa-Couto, Andreas Wichert **Published:** 2022-07-20T17:08:10Z **Abstract:** It is generally assumed that the brain uses something akin to sparse distributed representations. These representations, however, are high-dimensional and consequently they affect classification performance of traditional Machine Learning models due to "the curse of dimensionality". In tasks for which there is a vast amount of labeled data, Deep Networks seem to...Votes: 0GitHub stars: 3
- Can Ai Agents Really Complete Rtl To Gds Lessons FDerived from arXiv:2607.17528 - Can AI Agents Really Complete RTL-to-GDS? Lessons from Benchmarking Tool-Interactive EDA WorkflowsVotes: 0GitHub stars: 3
- Can Induced Emotion Bias Llm Behaviors In SequentiCan Induced Emotion Bias LLM Behaviors in Sequential Decision Making? - As Large Language Models (LLMs) are increasingly deployed as autonomous agents in high-stakes domains, understanding contextual factors that may modul...Votes: 0GitHub stars: 3
- Cauchy Loss Function Robustness Under Gaussian And Cauchy Noise**arXiv ID:** 2302.07238 **Authors:** Thamsanqa Mlotshwa, Heinrich van Deventer, Anna Sergeevna Bosman **Published:** 2023-02-14T18:34:44Z **Abstract:** In supervised machine learning, the choice of loss function implicitly assumes a particular noise distribution over the data. For example, the frequently used mean squared error (MSE) loss assumes a Gaussian noise distribution. The choice of loss function during training and testing affects the performance of artificial neural networks (ANNs)...Votes: 0GitHub stars: 3
- Cg World A Large Scale World State Dataset And ProCG-World: A Large-Scale World-State Dataset and Protocol for World ModelsVotes: 0GitHub stars: 3
- Circuit Balancing Error MitigationCircuit balancing methodology for quantum error mitigation in unitary k-design circuits. Uses gate benchmarking + Pauli twirling to estimate and invert circuit-wide depolarization without two-qubit gate overhead. Tested on IBM Fez superconducting quantum computer. Based on arXiv:2606.03891 (Jun 2026).Votes: 0GitHub stars: 3
- Classifying Daily Activities Needs Posture Reconstructing Them Needs MotionSkill for understanding the dissociation between posture-based action classification and motion-dependent movement reconstruction in human visionVotes: 0GitHub stars: 3
- Closedform Continuoustime Neural Models**arXiv ID:** 2106.13898 **Authors:** Ramin Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, Daniela Rus **Published:** 2021-06-25T22:08:51Z **Abstract:** Continuous-time neural processes are performant sequential decision-makers that are built by differential equations (DE). However, their expressive power when they are deployed on computers is bottlenecked by numerical DE solvers. This limitation has significantly slowed down the scalin...Votes: 0GitHub stars: 3
- Closing The Lab To Store Gap A Data Efficient PostSkill generated from arXiv paper 2607.20345: Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail HumanoidsVotes: 0GitHub stars: 3
- Coarse To Fine Nonrigid Registration A Chain Of Scalespecific Neural Networks For Multimodal Image Alignment With Application To Remote Sensing**arXiv ID:** 1802.09816 **Authors:** Armand Zampieri, Guillaume Charpiat, Yuliya Tarabalka **Published:** 2018-02-27T10:47:06Z **Abstract:** We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of scale. We desi...Votes: 0GitHub stars: 3
- Cognitive Deep Machine Can Train Itself**arXiv ID:** 1612.00745 **Authors:** András Lőrincz, Máté Csákvári, Áron Fóthi, Zoltán Ádám Milacski, András Sárkány, Zoltán Tősér **Published:** 2016-12-02T16:49:07Z **Abstract:** Machine learning is making substantial progress in diverse applications. The success is mostly due to advances in deep learning. However, deep learning can make mistakes and its generalization abilities to new tasks are questionable. We ask when and how one can combine network outputs, when (i) details of the obse...Votes: 0GitHub stars: 3