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CLI tools, utilities, converters, workflow automation, and productivity helpers
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Showing 17,209–17,232 of 49,285 skills
- Arxiv 2609 08725v1 Baff Bid Aware Filter Family For Mitigating Traini**arXiv ID:** 2609.08725v1 **Authors:** Jeonglyul Oh, Ikkyu Choi, Inseop Youn, Youngjae Kim **URL:** http://arxiv.org/abs/2609.08725v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 02106v1 Git4data Database Native Version Control For Ai Ag**arXiv ID:** 2609.02106v1 **Authors:** Hongshen Gou, Zuyu Zhang, Yuze Sun, Peng Xu, Feng Tian, Long Wang, Jianguo Wang **URL:** http://arxiv.org/abs/2609.02106v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 30889v1 Safety Screening For Voltage Control In Active Dis**arXiv ID:** 2608.30889v1 **Authors:** Sarra Bouchkati, Petros Ellinas, Adriana Geisler, Steffen Kortmann, Johanna Vorwerk, Spyros Chatzivasiliadis, Andreas Ulbig **URL:** http://arxiv.org/abs/2608.30889v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 26788v1 Decoupling Planning And Control For Instructable A**arXiv ID:** 2608.26788v1 **Authors:** Zineng Tang, Kelsey R. Allen, Sjoerd van Steenkiste, Ishita Dasgupta, Alane Suhr **URL:** http://arxiv.org/abs/2608.26788v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 25777v1 Locallstc A Long Short Term Control Architecture F**arXiv ID:** 2608.25777v1 **Authors:** Weiming Li, Helen Paik, Yulei Sui **URL:** http://arxiv.org/abs/2608.25777v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 22765v1 Learning To Control Coupled Dynamics Environments**arXiv ID:** 2608.22765v1 **Authors:** Ege C. Kaya, Aliasghar Pourghani, Mahsa Ghasemi, Vijay Gupta, Abolfazl Hashemi **URL:** http://arxiv.org/abs/2608.22765v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 12974v1 Comment On Modeling Rapid Language Learning By Dis**arXiv ID:** 2608.12974v1 **Authors:** Orr Well, Idan Tarshish, Nur Lan, Roni Katzir **URL:** http://arxiv.org/abs/2608.12974v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 06240v1 Prism Distribution Gated Flow Matching For ControlPRISM: Distribution-Gated Flow Matching for Controllable Unpaired Image Translation (arXiv: 2608.06240v1)Votes: 0GitHub stars: 3
- Arxiv 2608 06154v1 Visual Grounding In Zero Shot Vision Language ContVisual Grounding in Zero-Shot Vision-Language Control (arXiv: 2608.06154v1)Votes: 0GitHub stars: 3
- Arxiv 2608 05783v1 Grom Gradient Free Rapid One Shot Machine Unlearni**arXiv ID:** 2608.05783v1 **Authors:** Paweł Batorski, Przemysław Spurek, Paul Swoboda **URL:** http://arxiv.org/abs/2608.05783v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2606 14299 What Drives Test Time Adaptation For Clip A ControWhat Drives Test-Time Adaptation for CLIP? A Controlled Empirical Study from an Update Perspective (arXiv: 2606.14299)Votes: 0GitHub stars: 3
- Arxiv 0308025 Controlled Hierarchical Filtering Model Of NeocortControlled hierarchical filtering: Model of neocortical sensory processing (arXiv: 0308025)Votes: 0GitHub stars: 3
- A Physics Informed Framework For Pid Tuning Of CheA Physics-Informed Framework for PID Tuning of Chemical Processes Using Large Language Model AgentsVotes: 0GitHub stars: 3
- Variational Rejection Sampling**arXiv ID:** 1804.01712 **Authors:** Aditya Grover, Ramki Gummadi, Miguel Lazaro-Gredilla, Dale Schuurmans, Stefano Ermon **Published:** 2018-04-05T07:53:41Z **Abstract:** Learning latent variable models with stochastic variational inference is challenging when the approximate posterior is far from the true posterior, due to high variance in the gradient estimates. We propose a novel rejection sampling step that discards samples from the variational posterior which are assigned low likelihoo...Votes: 0GitHub stars: 3
- Utilizing A Digital Swarm Intelligence Platform To Improve Consensus Among Radiologists And Exploring Its Applications**arXiv ID:** 2107.07341 **Authors:** Rutwik Shah, Bruno Astuto, Tyler Gleason, Will Fletcher, Justin Banaga, Kevin Sweetwood, Allen Ye, Rina Patel, Kevin McGill, Thomas Link, Jason Crane, Valentina Pedoia, Sharmila Majumdar **Published:** 2021-06-26T06:52:06Z **Abstract:** Radiologists today play a key role in making diagnostic decisions and labeling images for training A.I. algorithms. Low inter-reader reliability (IRR) can be seen between experts when interpreting challenging cases. While ...Votes: 0GitHub stars: 3
- Towards Graph Neural Networks For Provably Solving Convex Optimization Problems**arXiv ID:** 2502.02446 **Authors:** Chendi Qian, Christopher Morris **Published:** 2025-02-04T16:11:41Z **Abstract:** Recently, message-passing graph neural networks (MPNNs) have shown potential for solving combinatorial and continuous optimization problems due to their ability to capture variable-constraint interactions. While existing approaches leverage MPNNs to approximate solutions or warm-start traditional solvers, they often lack guarantees for feasibility, particularly in convex opt...Votes: 0GitHub stars: 3
- Semantically Decomposing The Latent Spaces Of Generative Adversarial Networks**arXiv ID:** 1705.07904 **Authors:** Chris Donahue, Zachary C. Lipton, Akshay Balsubramani, Julian McAuley **Published:** 2017-05-22T18:00:02Z **Abstract:** We propose a new algorithm for training generative adversarial networks that jointly learns latent codes for both identities (e.g. individual humans) and observations (e.g. specific photographs). By fixing the identity portion of the latent codes, we can generate diverse images of the same subject, and by fixing the observation portion, ...Votes: 0GitHub stars: 3
- Semantic Bottleneck For Computer Vision Tasks**arXiv ID:** 1811.02234 **Authors:** Maxime Bucher, Stéphane Herbin, Frédéric Jurie **Published:** 2018-11-06T09:01:02Z **Abstract:** This paper introduces a novel method for the representation of images that is semantic by nature, addressing the question of computation intelligibility in computer vision tasks. More specifically, our proposition is to introduce what we call a semantic bottleneck in the processing pipeline, which is a crossing point in which the representation of the image is...Votes: 0GitHub stars: 3
- Scaling Mapelites To Deep Neuroevolution**arXiv ID:** 2003.01825 **Authors:** Cédric Colas, Joost Huizinga, Vashisht Madhavan, Jeff Clune **Published:** 2020-03-03T23:02:37Z **Abstract:** Quality-Diversity (QD) algorithms, and MAP-Elites (ME) in particular, have proven very useful for a broad range of applications including enabling real robots to recover quickly from joint damage, solving strongly deceptive maze tasks or evolving robot morphologies to discover new gaits. However, present implementations of MAP-Elites and other QD ...Votes: 0GitHub stars: 3
- Scalable Recollections For Continual Lifelong Learning**arXiv ID:** 1711.06761 **Authors:** Matthew Riemer, Tim Klinger, Djallel Bouneffouf, Michele Franceschini **Published:** 2017-11-17T23:00:11Z **Abstract:** Given the recent success of Deep Learning applied to a variety of single tasks, it is natural to consider more human-realistic settings. Perhaps the most difficult of these settings is that of continual lifelong learning, where the model must learn online over a continuous stream of non-stationary data. A successful continual lifelong le...Votes: 0GitHub stars: 3
- Qualitydiversity Optimization As Multiobjective Optimization**arXiv ID:** 2602.00478 **Authors:** Xi Lin, Ping Guo, Yilu Liu, Qingfu Zhang, Jianyong Sun **Published:** 2026-01-31T03:01:13Z **Abstract:** The Quality-Diversity (QD) optimization aims to discover a collection of high-performing solutions that simultaneously exhibit diverse behaviors within a user-defined behavior space. This paradigm has stimulated significant research interest and demonstrated practical utility in domains including robot control, creative design, and adversarial sample g...Votes: 0GitHub stars: 3
- Post Training At The Edge Of Detectability A GamePost-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-TuningVotes: 0GitHub stars: 3
- Parallel Training Of Deep Networks With Local Updates**arXiv ID:** 2012.03837 **Authors:** Michael Laskin, Luke Metz, Seth Nabarro, Mark Saroufim, Badreddine Noune, Carlo Luschi, Jascha Sohl-Dickstein, Pieter Abbeel **Published:** 2020-12-07T16:38:45Z **Abstract:** Deep learning models trained on large data sets have been widely successful in both vision and language domains. As state-of-the-art deep learning architectures have continued to grow in parameter count so have the compute budgets and times required to train them, increasing the need...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