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Claude Skills by hiyenwong
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- Arxiv 2609 08234v1 Distribution Free Inference On The Number Of Chang**arXiv ID:** 2609.08234v1 **Authors:** Rohan Hore, Aaditya Ramdas **URL:** http://arxiv.org/abs/2609.08234v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 08381v1 Equivariance Breaks The Learning Rate**arXiv ID:** 2609.08381v1 **Authors:** Andrei Manolache, Mathias Niepert **URL:** http://arxiv.org/abs/2609.08381v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 08418v1 Feyospace V1 How The Cyber Mercury Seven Trained F**arXiv ID:** 2609.08418v1 **Authors:** Zongjie Li, Alan Z. W, John Nicolas J, Walter H. F, Scott Donald L, Gordon Y. P, Deke X **URL:** http://arxiv.org/abs/2609.08418v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 08537v1 The Exact Time Uniform Rate Frontier For Stochasti**arXiv ID:** 2609.08537v1 **Authors:** Ruijie Li, Kang Chen, Tianyu Wang **URL:** http://arxiv.org/abs/2609.08537v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 08772v1 It S All In The Way You Say It The Role Of Informa**arXiv ID:** 2609.08772v1 **Authors:** Andrea Apicella, Pasquale Arpaia, Matteo Orefice, Andrea Pollastro, Roberto Prevete **URL:** http://arxiv.org/abs/2609.08772v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 08832v1 Closing The Consistency Gap Self Evolving Agents T**arXiv ID:** 2609.08832v1 **Authors:** Evelyn Duesterwald, Benjamin Elder, Lilian Ngweta, Shashanka Ubaru, Malgorzata Zimon **URL:** http://arxiv.org/abs/2609.08832v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09011v1 Closed Form Of The Local Galactic Potential And St**arXiv ID:** 2609.09011v1 **Authors:** Indranil Das, Adam Kamoski, Dora Demiri, Brianna Isola, Hanieh Karimi, Dmitrii S. Zagorulia **URL:** http://arxiv.org/abs/2609.09011v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09048v1 The Audit Decides The Verdict Instrument Effects R**arXiv ID:** 2609.09048v1 **Authors:** Siddharth Vohra, Manikandan Ravikiran **URL:** http://arxiv.org/abs/2609.09048v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09094v1 The Surprising Effectiveness Of Approximate Value**arXiv ID:** 2609.09094v1 **Authors:** Raphael Boige, Amine Boumaza, Bruno Scherrer **URL:** http://arxiv.org/abs/2609.09094v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09589v1 A Function Space Approach To The Statistical Mecha**arXiv ID:** 2609.09589v1 **Authors:** Yizhou Zhang, Weichen Wu, Lun Du, Zhengjie Miao **URL:** http://arxiv.org/abs/2609.09589v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09656v1 Why Learning Rediscovers The Closed Form Diagonal**arXiv ID:** 2609.09656v1 **Authors:** Jeahn Han, Pyojin Kim **URL:** http://arxiv.org/abs/2609.09656v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09768v1 Fine Tuning A Kv Cache Concatenation Aware Model O**arXiv ID:** 2609.09768v1 **Authors:** Fumihiko Tachibana, Daisuke Miyashita, Jun Deguchi **URL:** http://arxiv.org/abs/2609.09768v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09848v1 Subgroup Membership Inference Audits Of Differenti**arXiv ID:** 2609.09848v1 **Authors:** Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar, Siew Kei Lam, Anil Anthony Bharath **URL:** http://arxiv.org/abs/2609.09848v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09856v1 With A Thermomix You Lose The Ability To Cook A Ki**arXiv ID:** 2609.09856v1 **Authors:** Nikol Rummel, Valentina Nachtigall, Ernesto Panadero **URL:** http://arxiv.org/abs/2609.09856v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 10210v1 Through The Looking Glass Directly Reading And Wri**arXiv ID:** 2609.10210v1 **Authors:** Mark Oskin **URL:** http://arxiv.org/abs/2609.10210v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 10296v1 The Semantic Bottleneck Leveraging Semantic Repres**arXiv ID:** 2609.10296v1 **Authors:** Gilad D. Landau, Dulhan Jayalath, Oiwi Parker Jones **URL:** http://arxiv.org/abs/2609.10296v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 10350v1 Cyber Financial Contagion Modeling The Propagation**arXiv ID:** 2609.10350v1 **Authors:** Alex Leytes **URL:** http://arxiv.org/abs/2609.10350v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 10525v1 Characterizing Language Generation In The Limit Fi**arXiv ID:** 2609.10525v1 **Authors:** Xiaoyu Li, Andi Han, Jiaojiao Jiang, Junbin Gao **URL:** http://arxiv.org/abs/2609.10525v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Building Endtoend Dialogue Systems Using Generative Hierarchical Neural Network Models**arXiv ID:** 1507.04808 **Authors:** Iulian V. Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, Joelle Pineau **Published:** 2015-07-17T00:21:39Z **Abstract:** We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we exten...Votes: 0GitHub stars: 3
- Certify Ed Multi Layer Verification Framework ExactExact diagonalization (ED) is a workhorse technique in computational quantum many-body physics, but published ED results are rarely accompanied by machine-checkable evidence of their numerical correctVotes: 0GitHub stars: 3
- Certify Ed VerificationMulti-layer verification framework methodology for computational pipelines. 13-layer defense-in-depth validation, multi-oracle consensus, tamper-evident certificates, error-injection self-testing.Votes: 0GitHub stars: 3
- Contextually Enhanced Esdrnn With Dynamic Attention For Shortterm Load Forecasting**arXiv ID:** 2212.09030 **Authors:** Slawek Smyl, Grzegorz Dudek, Paweł Pełka **Published:** 2022-12-18T07:42:48Z **Abstract:** In this paper, we propose a new short-term load forecasting (STLF) model based on contextually enhanced hybrid and hierarchical architecture combining exponential smoothing (ES) and a recurrent neural network (RNN). The model is composed of two simultaneously trained tracks: the context track and the main track. The context track introduces additional information to...Votes: 0GitHub stars: 3
- Cyberaid Ai Security FrameworkAI-driven multi-agent cybersecurity framework for financial services. Hybrid system combining LLM subagents with classical SIEM/XDR telemetry, privacy-preserving federation, and quantum-based authentication. Use when designing AI-powered security operations, building multi-agent SOC systems, or creating privacy-preserving collaborative defense platforms.Votes: 0GitHub stars: 3
- Deep Coral Correlation Alignment For Deep Domain Adaptation**arXiv ID:** 1607.01719 **Authors:** Baochen Sun, Kate Saenko **Published:** 2016-07-06T17:35:55Z **Abstract:** Deep neural networks are able to learn powerful representations from large quantities of labeled input data, however they cannot always generalize well across changes in input distributions. Domain adaptation algorithms have been proposed to compensate for the degradation in performance due to domain shift. In this paper, we address the case when the target domain is unlabeled, req...Votes: 0GitHub stars: 3
- Distributional Soft Bellman Operator Under The CraDerived from arXiv:2607.17897 - Distributional Soft Bellman Operator under the Cramér GeometryVotes: 0GitHub stars: 3
- Drift Anticipation With Forgetting To Improve Evolving Fuzzy System**arXiv ID:** 2101.02442 **Authors:** Clément Leroy, Eric Anquetil, Nathalie Girard **Published:** 2021-01-07T09:21:27Z **Abstract:** Working with a non-stationary stream of data requires for the analysis system to evolve its model (the parameters as well as the structure) over time. In particular, concept drifts can occur, which makes it necessary to forget knowledge that has become obsolete. However, the forgetting is subjected to the stability-plasticity dilemma, that is, increasing forget...Votes: 0GitHub stars: 3
- Eegsn Towards Efficient Lowlatency Decoding Of Eeg With Graph Spiking Neural Networks**arXiv ID:** 2304.07655 **Authors:** Xi Chen, Siwei Mai, Konstantinos Michmizos **Published:** 2023-04-15T23:30:17Z **Abstract:** A vast majority of spiking neural networks (SNNs) are trained based on inductive biases that are not necessarily a good fit for several critical tasks that require low-latency and power efficiency. Inferring brain behavior based on the associated electroenchephalography (EEG) signals is an example of how networks training and inference efficiency can be heavily im...Votes: 0GitHub stars: 3
- Efficient Rl Via Disentangled Environment And Agent Representations**arXiv ID:** 2309.02435 **Authors:** Kevin Gmelin, Shikhar Bahl, Russell Mendonca, Deepak Pathak **Published:** 2023-09-05T17:59:45Z **Abstract:** Agents that are aware of the separation between themselves and their environments can leverage this understanding to form effective representations of visual input. We propose an approach for learning such structured representations for RL algorithms, using visual knowledge of the agent, such as its shape or mask, which is often inexpensive to obt...Votes: 0GitHub stars: 3
- Erfact And Pserf Nonmonotonic Smooth Trainable Activation Functions**arXiv ID:** 2109.04386 **Authors:** Koushik Biswas, Sandeep Kumar, Shilpak Banerjee, Ashish Kumar Pandey **Published:** 2021-09-09T16:17:38Z **Abstract:** An activation function is a crucial component of a neural network that introduces non-linearity in the network. The state-of-the-art performance of a neural network depends also on the perfect choice of an activation function. We propose two novel non-monotonic smooth trainable activation functions, called ErfAct and Pserf. Experiments su...Votes: 0GitHub stars: 3
- Evolution Meets Diffusion Efficient Neural Architecture Generation**arXiv ID:** 2504.17827 **Authors:** Bingye Zhou, Caiyang Yu, Chenwei Tang **Published:** 2025-04-24T03:09:04Z **Abstract:** Neural Architecture Search (NAS) has gained widespread attention for its transformative potential in deep learning model design. However, the vast and complex search space of NAS leads to significant computational and time costs. Neural Architecture Generation (NAG) addresses this by reframing NAS as a generation problem, enabling the precise generation of optimal arch...Votes: 0GitHub stars: 3
- Fast Fourier Transformbased Spectral And Temporal Gradient Filtering For Differential Privacy**arXiv ID:** 2505.04468 **Authors:** Hyeju Shin, Vincent-Daniel, Kyudan Jung, Seongwon Yun **Published:** 2025-05-07T14:38:58Z **Abstract:** Differential Privacy (DP) has emerged as a key framework for protecting sensitive data in machine learning, but standard DP-SGD often suffers from significant accuracy loss due to injected noise. To address this limitation, we introduce the FFT-Enhanced Kalman Filter (FFTKF), a differentially private optimization method that improves gradient quality w...Votes: 0GitHub stars: 3
- Fedbud Joint Incentive Privacy OptimizationFederated learning has become a popular paradigm for privacy protection and edge-based machine learning. However, defending against differential attac... 触发词: 联邦学习, 控制系统.Votes: 0GitHub stars: 3
- Feedback Favors The Generalization Of Neural Odes**arXiv ID:** 2410.10253 **Authors:** Jindou Jia, Zihan Yang, Meng Wang, Kexin Guo, Jianfei Yang, Xiang Yu, Lei Guo **Published:** 2024-10-14T08:09:45Z **Abstract:** The well-known generalization problem hinders the application of artificial neural networks in continuous-time prediction tasks with varying latent dynamics. In sharp contrast, biological systems can neatly adapt to evolving environments benefiting from real-time feedback mechanisms. Inspired by the feedback philosophy, we presen...Votes: 0GitHub stars: 3
- Filtering Variational Objectives**arXiv ID:** 1705.09279 **Authors:** Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh **Published:** 2017-05-25T17:52:41Z **Abstract:** When used as a surrogate objective for maximum likelihood estimation in latent variable models, the evidence lower bound (ELBO) produces state-of-the-art results. Inspired by this, we consider the extension of the ELBO to a family of lower bounds defined by a particle filter's estim...Votes: 0GitHub stars: 3
- Hidden Tree Markov Networks Deep And Wide Learning For Structured Data**arXiv ID:** 1711.07784 **Authors:** Davide Bacciu **Published:** 2017-11-21T13:50:34Z **Abstract:** The paper introduces the Hidden Tree Markov Network (HTN), a neuro-probabilistic hybrid fusing the representation power of generative models for trees with the incremental and discriminative learning capabilities of neural networks. We put forward a modular architecture in which multiple generative models of limited complexity are trained to learn structural feature detectors whose outputs ar...Votes: 0GitHub stars: 3
- Ideological Sublations Resolution Of Dialectic In Populationbased Optimization**arXiv ID:** 1707.06992 **Authors:** S. Hossein Hosseini, Afshin Ebrahimi **Published:** 2017-07-21T17:53:04Z **Abstract:** A population-based optimization algorithm was designed, inspired by two main thinking modes in philosophy, both based on dialectic concept and thesis-antithesis paradigm. They impose two different kinds of dialectics. Idealistic and materialistic antitheses are formulated as optimization models. Based on the models, the population is coordinated for dialectical interact...Votes: 0GitHub stars: 3
- Kanerva Extending The Kanerva Machine With Differentiable Locally Block Allocated Latent Memory**arXiv ID:** 2103.03905 **Authors:** Jason Ramapuram, Yan Wu, Alexandros Kalousis **Published:** 2021-02-20T18:40:40Z **Abstract:** Episodic and semantic memory are critical components of the human memory model. The theory of complementary learning systems (McClelland et al., 1995) suggests that the compressed representation produced by a serial event (episodic memory) is later restructured to build a more generalized form of reusable knowledge (semantic memory). In this work we develop a ne...Votes: 0GitHub stars: 3
- Layerwise Geodistributed Computing Between Cloud And Iot**arXiv ID:** 2201.07215 **Authors:** Satoshi Kamo, Yiqiang Sheng **Published:** 2022-01-14T23:41:04Z **Abstract:** In this paper, we propose a novel architecture for a deep learning system, named k-degree layer-wise network, to realize efficient geo-distributed computing between Cloud and Internet of Things (IoT). The geo-distributed computing extends Cloud to the geographical verge of the network in the neighbor of IoT. The basic ideas of the proposal include a k-degree constraint and a lay...Votes: 0GitHub stars: 3
- Linear Constraints Learning For Spiking Neurons**arXiv ID:** 2103.12564 **Authors:** Huy Le Nguyen, Dominique Chu **Published:** 2021-03-10T13:54:05Z **Abstract:** We introduce a new supervised learning algorithm based to train spiking neural networks for classification. The algorithm overcomes a limitation of existing multi-spike learning methods: it solves the problem of interference between interacting output spikes during a learning trial. This problem of learning interference causes learning performance in existing approaches to decr...Votes: 0GitHub stars: 3
- Metageneralization For Multiparty Privacy Learning To Identify Anomaly Multimedia Traffic In Graynet**arXiv ID:** 2201.03027 **Authors:** Satoshi Nato, Yiqiang Sheng **Published:** 2022-01-09T14:51:34Z **Abstract:** Identifying anomaly multimedia traffic in cyberspace is a big challenge in distributed service systems, multiple generation networks and future internet of everything. This letter explores meta-generalization for a multiparty privacy learning model in graynet to improve the performance of anomaly multimedia traffic identification. The multiparty privacy learning model in graynet...Votes: 0GitHub stars: 3
- Metalearning With Hebbian Fast Weights**arXiv ID:** 1807.05076 **Authors:** Tsendsuren Munkhdalai, Adam Trischler **Published:** 2018-07-12T14:40:06Z **Abstract:** We unify recent neural approaches to one-shot learning with older ideas of associative memory in a model for metalearning. Our model learns jointly to represent data and to bind class labels to representations in a single shot. It builds representations via slow weights, learned across tasks through SGD, while fast weights constructed by a Hebbian learning rule impleme...Votes: 0GitHub stars: 3
- Mitigation Of Gender Bias In Automatic Facial Nonverbal Behaviors Generation**arXiv ID:** 2410.07274 **Authors:** Alice Delbosc, Magalie Ochs, Nicolas Sabouret, Brian Ravenet, Stephane Ayache **Published:** 2024-10-09T06:41:24Z **Abstract:** Research on non-verbal behavior generation for social interactive agents focuses mainly on the believability and synchronization of non-verbal cues with speech. However, existing models, predominantly based on deep learning architectures, often perpetuate biases inherent in the training data. This raises ethical concerns, dependi...Votes: 0GitHub stars: 3
- Ml4co Is Gcnn All You Need Graph Convolutional Neural Networks Produce Strong Baselines For Combinatorial Optimization Problems If Tuned And Trained Properly On Appropriate Data**arXiv ID:** 2112.12251 **Authors:** Amin Banitalebi-Dehkordi, Yong Zhang **Published:** 2021-12-22T22:40:13Z **Abstract:** The 2021 NeurIPS Machine Learning for Combinatorial Optimization (ML4CO) competition was designed with the goal of improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components with machine learning models. The competition's main scientific question was the following: is machine learning a viable option for improving traditional com...Votes: 0GitHub stars: 3
- Mode Regularized Generative Adversarial Networks**arXiv ID:** 1612.02136 **Authors:** Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, Wenjie Li **Published:** 2016-12-07T07:45:38Z **Abstract:** Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, which can easily make train...Votes: 0GitHub stars: 3
- Molecular Classification Using Hyperdimensional Graph Classification**arXiv ID:** 2403.12307 **Authors:** Pere Verges, Igor Nunes, Mike Heddes, Tony Givargis, Alexandru Nicolau **Published:** 2024-03-18T23:16:17Z **Abstract:** Our work introduces an innovative approach to graph learning by leveraging Hyperdimensional Computing. Graphs serve as a widely embraced method for conveying information, and their utilization in learning has gained significant attention. This is notable in the field of chemoinformatics, where learning from graph representations plays a...Votes: 0GitHub stars: 3
- More Consideration For The Perceptron**arXiv ID:** 2409.13854 **Authors:** Slimane Larabi **Published:** 2024-09-20T19:01:29Z **Abstract:** In this paper, we introduce the gated perceptron, an enhancement of the conventional perceptron, which incorporates an additional input computed as the product of the existing inputs. This allows the perceptron to capture non-linear interactions between features, significantly improving its ability to classify and regress on complex datasets. We explore its application in both linear and non...Votes: 0GitHub stars: 3
- Multi Class Vs Multi Label Bert For Cve To Cwe Mapping How Taxonomy StructureAssigning Common Weakness Enumeration (CWE) categories to Common Vulnerabilities and Exposures (CVE) records remains an important but largely manual step in vulnerability analysis. We study this task. Based on arXiv:2607.07573.Votes: 0GitHub stars: 3
- Multidimensional Cv Qkd ReconciliationMultidimensional reconciliation methodology for continuous-variable QKD with HDirac open-source simulation frameworkVotes: 0GitHub stars: 3
- Multitask Photonic Reservoir Computing Wavelength Division Multiplexing For Parallel Computing With A Silicon Microring Resonator**arXiv ID:** 2407.21189 **Authors:** Bernard J. Giron Castro, Christophe Peucheret, Darko Zibar, Francesco Da Ros **Published:** 2024-07-30T20:54:07Z **Abstract:** Nowadays, as the ever-increasing demand for more powerful computing resources continues, alternative advanced computing paradigms are under extensive investigation. Significant effort has been made to deviate from conventional Von Neumann architectures. In-memory computing has emerged in the field of electronics as a possible solu...Votes: 0GitHub stars: 3
- Object Representations As Fixed Points Training Iterative Refinement Algorithms With Implicit Differentiation**arXiv ID:** 2207.00787 **Authors:** Michael Chang, Thomas L. Griffiths, Sergey Levine **Published:** 2022-07-02T10:00:35Z **Abstract:** Iterative refinement -- start with a random guess, then iteratively improve the guess -- is a useful paradigm for representation learning because it offers a way to break symmetries among equally plausible explanations for the data. This property enables the application of such methods to infer representations of sets of entities, such as objects in physica...Votes: 0GitHub stars: 3