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 17,617–17,640 of 49,285 skills
- Meta Continual Learning**arXiv ID:** 1806.06928 **Authors:** Risto Vuorio, Dong-Yeon Cho, Daejoong Kim, Jiwon Kim **Published:** 2018-06-11T06:49:54Z **Abstract:** Using neural networks in practical settings would benefit from the ability of the networks to learn new tasks throughout their lifetimes without forgetting the previous tasks. This ability is limited in the current deep neural networks by a problem called catastrophic forgetting, where training on new tasks tends to severely degrade performance on previo...Votes: 0GitHub stars: 3
- Memory Networks Towards Fully Biologically Plausible Learning**arXiv ID:** 2409.17282 **Authors:** Jacobo Ruiz, Manas Gupta **Published:** 2024-09-18T06:01:35Z **Abstract:** The field of artificial intelligence faces significant challenges in achieving both biological plausibility and computational efficiency, particularly in visual learning tasks. Current artificial neural networks, such as convolutional neural networks, rely on techniques like backpropagation and weight sharing, which do not align with the brain's natural information processing metho...Votes: 0GitHub stars: 3
- Manifold Mixup Better Representations By Interpolating Hidden States**arXiv ID:** 1806.05236 **Authors:** Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, Aaron Courville, David Lopez-Paz, Yoshua Bengio **Published:** 2018-06-13T19:32:59Z **Abstract:** Deep neural networks excel at learning the training data, but often provide incorrect and confident predictions when evaluated on slightly different test examples. This includes distribution shifts, outliers, and adversarial examples. To address these issues, we propose Manifold Mix...Votes: 0GitHub stars: 3
- Machine Unlearning Using Forgetting Neural Networks**arXiv ID:** 2410.22374 **Authors:** Amartya Hatua, Trung T. Nguyen, Filip Cano, Andrew H. Sung **Published:** 2024-10-29T02:52:26Z **Abstract:** Modern computer systems store vast amounts of personal data, enabling advances in AI and ML but risking user privacy and trust. For privacy reasons, it is sometimes desired for an ML model to forget part of the data it was trained on. In this paper, we introduce a novel unlearning approach based on Forgetting Neural Networks (FNNs), a neuroscience-...Votes: 0GitHub stars: 3
- Machine Learning Classification Of Nonmarkovian Noise Disturbing Quantum Dynamics**arXiv ID:** 2101.03221 **Authors:** Stefano Martina, Stefano Gherardini, Filippo Caruso **Published:** 2021-01-08T20:56:56Z **Abstract:** In this paper machine learning and artificial neural network models are proposed for the classification of external noise sources affecting a given quantum dynamics. For this purpose, we train and then validate support vector machine, multi-layer perceptron and recurrent neural network models with different complexity and accuracy, to solve supervised bin...Votes: 0GitHub stars: 3
- Logic Explained Networks**arXiv ID:** 2108.05149 **Authors:** Gabriele Ciravegna, Pietro Barbiero, Francesco Giannini, Marco Gori, Pietro Lió, Marco Maggini, Stefano Melacci **Published:** 2021-08-11T10:55:42Z **Abstract:** The large and still increasing popularity of deep learning clashes with a major limit of neural network architectures, that consists in their lack of capability in providing human-understandable motivations of their decisions. In situations in which the machine is expected to support the decision...Votes: 0GitHub stars: 3
- Localizing Catastrophic Forgetting In Neural Networks**arXiv ID:** 1906.02568 **Authors:** Felix Wiewel, Bin Yang **Published:** 2019-06-06T13:18:03Z **Abstract:** Artificial neural networks (ANNs) suffer from catastrophic forgetting when trained on a sequence of tasks. While this phenomenon was studied in the past, there is only very limited recent research on this phenomenon. We propose a method for determining the contribution of individual parameters in an ANN to catastrophic forgetting. The method is used to analyze an ANNs response to thr...Votes: 0GitHub stars: 3
- Llmet Enabling Cross Layer Evaluation Of EmergingLLMET: Enabling Cross-Layer Evaluation of Emerging M3D Memories for Energy-Efficient LLM ServingVotes: 0GitHub stars: 3
- Lightweight Neural Networks**arXiv ID:** 1712.05695 **Authors:** Altaf H. Khan **Published:** 2017-12-15T14:56:05Z **Abstract:** Most of the weights in a Lightweight Neural Network have a value of zero, while the remaining ones are either +1 or -1. These universal approximators require approximately 1.1 bits/weight of storage, posses a quick forward pass and achieve classification accuracies similar to conventional continuous-weight networks. Their training regimen focuses on error reduction initially, but later emphas...Votes: 0GitHub stars: 3
- Learning With Molecules Beyond Graph Neural Networks**arXiv ID:** 2011.03488 **Authors:** Gustav Sourek, Filip Zelezny, Ondrej Kuzelka **Published:** 2020-11-06T17:42:42Z **Abstract:** We demonstrate a deep learning framework which is inherently based in the highly expressive language of relational logic, enabling to, among other things, capture arbitrarily complex graph structures. We show how Graph Neural Networks and similar models can be easily covered in the framework by specifying the underlying propagation rules in the relational logic....Votes: 0GitHub stars: 3
- Learning To Execute**arXiv ID:** 1410.4615 **Authors:** Wojciech Zaremba, Ilya Sutskever **Published:** 2014-10-17T01:35:12Z **Abstract:** Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are widely used because they are expressive and are easy to train. Our interest lies in empirically evaluating the expressiveness and the learnability of LSTMs in the sequence-to-sequence regime by training them to evaluate short computer programs, a domain that has traditionally been seen as too compl...Votes: 0GitHub stars: 3
- Learning Theorem Proving Components**arXiv ID:** 2107.10034 **Authors:** Karel Chvalovský, Jan Jakubův, Miroslav Olšák, Josef Urban **Published:** 2021-07-21T12:00:05Z **Abstract:** Saturation-style automated theorem provers (ATPs) based on the given clause procedure are today the strongest general reasoners for classical first-order logic. The clause selection heuristics in such systems are, however, often evaluating clauses in isolation, ignoring other clauses. This has changed recently by equipping the E/ENIGMA system with ...Votes: 0GitHub stars: 3
- Learning Optimal And Nearoptimal Lexicographic Preference Lists**arXiv ID:** 1909.09072 **Authors:** Ahmed Moussa, Xudong Liu **Published:** 2019-09-19T16:10:46Z **Abstract:** We consider learning problems of an intuitive and concise preference model, called lexicographic preference lists (LP-lists). Given a set of examples that are pairwise ordinal preferences over a universe of objects built of attributes of discrete values, we want to learn (1) an optimal LP-list that decides the maximum number of these examples, or (2) a near-optimal LP-list that dec...Votes: 0GitHub stars: 3
- Learning Conjecturing From Scratch**arXiv ID:** 2503.01389 **Authors:** Thibault Gauthier, Josef Urban **Published:** 2025-03-03T10:39:38Z **Abstract:** We develop a self-learning approach for conjecturing of induction predicates on a dataset of 16197 problems derived from the OEIS. These problems are hard for today's SMT and ATP systems because they require a combination of inductive and arithmetical reasoning. Starting from scratch, our approach consists of a feedback loop that iterates between (i) training a neural transla...Votes: 0GitHub stars: 3
- Learn To Bind And Grow Neural Structures**arXiv ID:** 2011.10568 **Authors:** Azhar Shaikh, Nishant Sinha **Published:** 2020-11-21T09:40:26Z **Abstract:** Task-incremental learning involves the challenging problem of learning new tasks continually, without forgetting past knowledge. Many approaches address the problem by expanding the structure of a shared neural network as tasks arrive, but struggle to grow optimally, without losing past knowledge. We present a new framework, Learn to Bind and Grow, which learns a neural architec...Votes: 0GitHub stars: 3
- Layup Asynchronous Decentralized Gradient Descent With Layerwise Updates**arXiv ID:** 2410.05985 **Authors:** Cabrel Teguemne Fokam, Marcel Nieveler, Lukas König, Khaleelulla Khan Nazeer, David Kappel, Anand Subramoney **Published:** 2024-10-08T12:32:36Z **Abstract:** The increasing size of deep learning models has made distributed training across multiple devices essential. Synchronous, centralized methods incur large communication and synchronization overheads. Communication efficient algorithms can reduce these overheads, but often require extra buffers, remai...Votes: 0GitHub stars: 3
- Lattice Recurrent Unit Improving Convergence And Statistical Efficiency For Sequence Modeling**arXiv ID:** 1710.02254 **Authors:** Chaitanya Ahuja, Louis-Philippe Morency **Published:** 2017-10-06T01:52:14Z **Abstract:** Recurrent neural networks have shown remarkable success in modeling sequences. However low resource situations still adversely affect the generalizability of these models. We introduce a new family of models, called Lattice Recurrent Units (LRU), to address the challenge of learning deep multi-layer recurrent models with limited resources. LRU models achieve this goa...Votes: 0GitHub stars: 3
- Latent Multitask Architecture Learning**arXiv ID:** 1705.08142 **Authors:** Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, Anders Søgaard **Published:** 2017-05-23T08:58:09Z **Abstract:** Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks. In practice, however, MTL involves searching an enormous space of possible parameter sharing architectures to find (a) the layers or subspaces that benefit from sharing, (b) the appropriate amount of sharing, and (c...Votes: 0GitHub stars: 3
- Kolmogorovarnold Networks For Metal Surface Defect Classification**arXiv ID:** 2501.06389 **Authors:** Maciej Krzywda, Mariusz Wermiński, Szymon Łukasik, Amir H. Gandomi **Published:** 2025-01-10T23:58:30Z **Abstract:** This paper presents the application of Kolmogorov-Arnold Networks (KAN) in classifying metal surface defects. Specifically, steel surfaces are analyzed to detect defects such as cracks, inclusions, patches, pitted surfaces, and scratches. Drawing on the Kolmogorov-Arnold theorem, KAN provides a novel approach compared to conventional multil...Votes: 0GitHub stars: 3
- Knowledgedriven Modulation Of Neural Networks With Attention Mechanism For Next Activity Prediction**arXiv ID:** 2312.08847 **Authors:** Ivan Donadello, Jonghyeon Ko, Fabrizio Maria Maggi, Jan Mendling, Francesco Riva, Matthias Weidlich **Published:** 2023-12-14T12:02:35Z **Abstract:** Predictive Process Monitoring (PPM) aims at leveraging historic process execution data to predict how ongoing executions will continue up to their completion. In recent years, PPM techniques for the prediction of the next activities have matured significantly, mainly thanks to the use of Neural Networks (NNs...Votes: 0GitHub stars: 3
- Kernel Implicit Variational Inference**arXiv ID:** 1705.10119 **Authors:** Jiaxin Shi, Shengyang Sun, Jun Zhu **Published:** 2017-05-29T11:11:35Z **Abstract:** Recent progress in variational inference has paid much attention to the flexibility of variational posteriors. One promising direction is to use implicit distributions, i.e., distributions without tractable densities as the variational posterior. However, existing methods on implicit posteriors still face challenges of noisy estimation and computational infeasibility when...Votes: 0GitHub stars: 3
- Karushkuhntucker Conditiontrained Neural Networks Kkt Nets**arXiv ID:** 2410.15973 **Authors:** Shreya Arvind, Rishabh Pomaje, Rajshekhar V Bhat **Published:** 2024-10-21T12:59:58Z **Abstract:** This paper presents a novel approach to solving convex optimization problems by leveraging the fact that, under certain regularity conditions, any set of primal or dual variables satisfying the Karush-Kuhn-Tucker (KKT) conditions is necessary and sufficient for optimality. Similar to Theory-Trained Neural Networks (TTNNs), the parameters of the convex optimi...Votes: 0GitHub stars: 3
- Items Or Relations What Do Artificial Neural Networks Learn**arXiv ID:** 2404.12401 **Authors:** Renate Krause, Stefan Reimann **Published:** 2024-04-15T08:11:45Z **Abstract:** What has an Artificial Neural Network (ANN) learned after being successfully trained to solve a task - the set of training items or the relations between them? This question is difficult to answer for modern applied ANNs because of their enormous size and complexity. Therefore, here we consider a low-dimensional network and a simple task, i.e., the network has to reproduce a s...Votes: 0GitHub stars: 3
- Invariant Layers For Graphs With Nodes Of Different Types**arXiv ID:** 2302.13551 **Authors:** Dmitry Rybin, Ruoyu Sun, Zhi-Quan Luo **Published:** 2023-02-27T07:10:33Z **Abstract:** Neural networks that satisfy invariance with respect to input permutations have been widely studied in machine learning literature. However, in many applications, only a subset of all input permutations is of interest. For heterogeneous graph data, one can focus on permutations that preserve node types. We fully characterize linear layers invariant to such permutations...Votes: 0GitHub stars: 3