All categories
Tools & Automation
CLI tools, utilities, converters, workflow automation, and productivity helpers
- 49,285
- 2,054
Security grades appear on each card once the skill has been scanned. Newly imported skills may briefly show without a grade until the backfill job runs.
Open in full browserBrowse tools & automation skills
Showing 17,449–17,472 of 49,285 skills
- Arxiv 2608 13521v1 Exponential Quantum Advantage For Learning Signals**arXiv ID:** 2608.13521v1 **Authors:** Ishaan Kannan, Sridhar Prabhu, Saeed A. Khan, Mandar M. Sohoni, Xingrui Song, Saswata Roy, Alen Senanian, Valla Fatemi, Peter L. McMahon, Jordan Cotler **URL:** http://arxiv.org/abs/2608.13521v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 12936v1 Autoqureo A Framework For Automated Quantum Resour**arXiv ID:** 2608.12936v1 **Authors:** Harshkumar Oza, Aritra Sarkar, Syed Naqi Abbas, Rahul Bhowmick, Aryan Prakash, Prateek P Kulkarni, Krishna Kumar Sabapathy **URL:** http://arxiv.org/abs/2608.12936v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 06294v1 Quantimedai Quantum Enhanced Time Series Model Gui**arXiv ID:** 2608.06294v1 **Authors:** Mutasim Fuad Sarker, Adiba Rahman Namira, Wafa Binte Alam, Md Adnan Arefeen, Mahzabeen Emu, Sumaiya Tabassum Nimi **URL:** http://arxiv.org/abs/2608.06294v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 05995v1 A Unified Risk View Of Uncertainty Posterior RiskA Unified Risk View of Uncertainty: Posterior Risk for Disentanglement and Evaluation Beyond Proxies (arXiv: 2608.05995v1)Votes: 0GitHub stars: 3
- Arxiv 2608 05995 A Unified Risk View Of Uncertainty Posterior RiskA Unified Risk View of Uncertainty: Posterior Risk for Disentanglement and Evaluation Beyond Proxies (arXiv: 2608.05995)Votes: 0GitHub stars: 3
- Arxiv 2608 05819v1 Learning To Rank Tensor Network Contraction Plans**arXiv ID:** 2608.05819v1 **Authors:** Alfred M. Pastor, Maribel Castillo, Jose M. Badia **URL:** http://arxiv.org/abs/2608.05819v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 05686v1 Provably Efficient Self Calibrating Quantum Fault**arXiv ID:** 2608.05686v1 **Authors:** Weiyuan Gong, Hong-Ye Hu **URL:** http://arxiv.org/abs/2608.05686v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 05646v1 Rasp Qaoa Resource Aware Per Instance Selection Fo**arXiv ID:** 2608.05646v1 **Authors:** Chih-Chung Hsu **URL:** http://arxiv.org/abs/2608.05646v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 05610v1 Lc Implicit Qaoa Active Workspace Capped Exact Obj**arXiv ID:** 2608.05610v1 **Authors:** Chih-Chung Hsu **URL:** http://arxiv.org/abs/2608.05610v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 05595v1 How Much Reconstruction Does Quantum Machine Learn**arXiv ID:** 2608.05595v1 **Authors:** Prabhjot Singh, Adel N. Toosi, Rajkumar Buyya **URL:** http://arxiv.org/abs/2608.05595v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Vector Policy Optimization Training For Diversity Improves Testtime Search**arXiv ID:** 2605.22817 **Authors:** Ryan Bahlous-Boldi, Isha Puri, Idan Shenfeld, Akarsh Kumar, Mehul Damani, Sebastian Risi, Omar Khattab, Zhang-Wei Hong, Pulkit Agrawal **Published:** 2026-05-21T17:59:26Z **Abstract:** Language models must now generalize out of the box to novel environments and work inside inference-scaling search procedures, such as AlphaEvolve, that select rollouts with a variety of task-specific reward functions. Unfortunately, the standard paradigm of LLM post-trainin...Votes: 0GitHub stars: 3
- Training Binary Neural Networks Without Floating Point Precision**arXiv ID:** 2310.19815 **Authors:** Federico Fontana **Published:** 2023-10-19T13:48:21Z **Abstract:** The main goal of this work is to improve the efficiency of training binary neural networks, which are low latency and low energy networks. The main contribution of this work is the proposal of two solutions comprised of topology changes and strategy training that allow the network to achieve near the state-of-the-art performance and efficient training. The time required for training and th...Votes: 0GitHub stars: 3
- Thermodynamic NetworksFramework for autonomous physics-based computation using non-equilibrium steady states in thermodynamic networks. Models computation as exchanges of conserved quantities between finite-size reservoirs relaxing toward equilibrium. Use when designing physical computing systems, thermodynamic engines, or autonomous molecular computation.Votes: 0GitHub stars: 3
- The Loss Landscape Of Overparameterized Neural Networks**arXiv ID:** 1804.10200 **Authors:** Y Cooper **Published:** 2018-04-26T17:58:45Z **Abstract:** We explore some mathematical features of the loss landscape of overparameterized neural networks. A priori one might imagine that the loss function looks like a typical function from $\mathbb{R}^n$ to $\mathbb{R}$ - in particular, nonconvex, with discrete global minima. In this paper, we prove that in at least one important way, the loss function of an overparameterized neural network does not loo...Votes: 0GitHub stars: 3
- Tensor Field Networks Rotation And Translationequivariant Neural Networks For 3d Point Clouds**arXiv ID:** 1802.08219 **Authors:** Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, Patrick Riley **Published:** 2018-02-22T18:17:31Z **Abstract:** We introduce tensor field neural networks, which are locally equivariant to 3D rotations, translations, and permutations of points at every layer. 3D rotation equivariance removes the need for data augmentation to identify features in arbitrary orientations. Our network uses filters built from spherical harmonics;...Votes: 0GitHub stars: 3
- Stl A Signed And Truncated Logarithm Activation Function For Neural Networks**arXiv ID:** 2307.16389 **Authors:** Yuanhao Gong **Published:** 2023-07-31T03:41:14Z **Abstract:** Activation functions play an essential role in neural networks. They provide the non-linearity for the networks. Therefore, their properties are important for neural networks' accuracy and running performance. In this paper, we present a novel signed and truncated logarithm function as activation function. The proposed activation function has significantly better mathematical properties, such ...Votes: 0GitHub stars: 3
- Shallow Unorganized Neural Networks Using Smart Neuron Model For Visual Perception**arXiv ID:** 1907.09050 **Authors:** Richard Jiang, Danny Crookes **Published:** 2019-07-21T23:09:35Z **Abstract:** The recent success of Deep Neural Networks (DNNs) has revealed the significant capability of neural computing in many challenging applications. Although DNNs are derived from emulating biological neurons, there still exist doubts over whether or not DNNs are the final and best model to emulate the mechanism of human intelligence. In particular, there are two discrepancies betwe...Votes: 0GitHub stars: 3
- Qpinn Integro Fractional PdeQuantum Physics-Informed Neural Networks for solving integro-differential equations (IDEs) and fractional integro-partial differential equations (FIPDEs) using variational quantum circuits with affine feature maps.Votes: 0GitHub stars: 3
- Nonlinear Separation Principle Applications Neural NetworksNonlinear separation principle with applications to neural networks, control and learning. Extends classical separation principle to nonlinear systems, enabling independent design of state observers and controllers for neural network-based systems. Activation: separation principle, neural network control, nonlinear observers, state estimation, control theoryVotes: 0GitHub stars: 3
- Multiparameter Hamiltonian EstimationOptimal multiparameter estimation for quantum systems using the unified Cramér-Rao bound framework. Use when: (1) estimating functions of multiple parameters in quantum Hamiltonians, (2) designing quantum sensing protocols, (3) analyzing precision limits for non-commuting generators, (4) optimizing quantum metrology with multiple parameters. Based on arXiv:2605.04136.Votes: 0GitHub stars: 3
- Multiobjective Evolutionary Design Of Composite Datadriven Models**arXiv ID:** 2103.01301 **Authors:** Iana S. Polonskaia, Nikolay O. Nikitin, Ilia Revin, Pavel Vychuzhanin, Anna V. Kalyuzhnaya **Published:** 2021-03-01T20:45:24Z **Abstract:** In this paper, a multi-objective approach for the design of composite data-driven mathematical models is proposed. It allows automating the identification of graph-based heterogeneous pipelines that consist of different blocks: machine learning models, data preprocessing blocks, etc. The implemented approach is based...Votes: 0GitHub stars: 3
- Llm For The Development Of Fcm**arXiv ID:** 2607.04983 **Authors:** Alexis Kafantaris **Published:** 2026-07-06T12:18:54Z **Abstract:** This article is about the development of a fuzzy cognitive map using a local large language model. In the light of recent advances it is evident that large language models, and even local large language models are capable of extracting quantities from textual data. In other words, a local LLM like Qwen2.5-32B, or probably larger, can accept entities as prompt input and determine relevant ...Votes: 0GitHub stars: 3
- Learning Multimodal Graphtograph Translation For Molecular Optimization**arXiv ID:** 1812.01070 **Authors:** Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola **Published:** 2018-12-03T20:28:09Z **Abstract:** We view molecular optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available corpus of paired molecules. Since molecules can be optimized in different ways, there are multiple viable translations for each input graph. A key challenge is therefore t...Votes: 0GitHub stars: 3
- Gradient Free Riemannian Langevin SamplerWe address the problem of efficiently sampling multimodal probability distributions, where standard Markov Chain Monte Carlo methods often suffer from poor mixing and mode trapping. To mitigate these. Based on arXiv:2607.07519.Votes: 0GitHub stars: 3