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CLI tools, utilities, converters, workflow automation, and productivity helpers
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Showing 17,593–17,616 of 49,285 skills
- Predicting The Time Until A Vehicle Changes The Lane Using Lstmbased Recurrent Neural Networks**arXiv ID:** 2102.01431 **Authors:** Florian Wirthmüller, Marvin Klimke, Julian Schlechtriemen, Jochen Hipp, Manfred Reichert **Published:** 2021-02-02T11:04:22Z **Abstract:** To plan safe and comfortable trajectories for automated vehicles on highways, accurate predictions of traffic situations are needed. So far, a lot of research effort has been spent on detecting lane change maneuvers rather than on estimating the point in time a lane change actually happens. In practice, however, this t...Votes: 0GitHub stars: 3
- Perspective Purposeful Failure In Artificial Life And Artificial Intelligence**arXiv ID:** 2102.12076 **Authors:** Lana Sinapayen **Published:** 2021-02-24T05:43:44Z **Abstract:** Complex systems fail. I argue that failures can be a blueprint characterizing living organisms and biological intelligence, a control mechanism to increase complexity in evolutionary simulations, and an alternative to classical fitness optimization. Imitating biological successes in Artificial Life and Artificial Intelligence can be misleading; imitating failures offers a path towards unders...Votes: 0GitHub stars: 3
- Performance Evaluation Results Of Evolutionary Clustering Algorithm Star For Clustering Heterogeneous Datasets**arXiv ID:** 2105.02810 **Authors:** Bryar A. Hassan, TarikA. Rashid, Seyedali Mirjalili **Published:** 2021-04-30T08:17:19Z **Abstract:** This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ) , expectation ma...Votes: 0GitHub stars: 3
- Pce Qubo Dense Constraint DecodingPauli Correlation Encoding (PCE) methodology for solving densely constrained QUBO problems using qubit compression and problem-aware guided decoding. Combines quantum computing with bioinformatics for mRNA secondary structure prediction.Votes: 0GitHub stars: 3
- Pauli Detecting Quantum CodesQuantum error detecting codes using Pauli group variance geometry — going beyond stabilizer codes via algebraic structure of Pauli operators. Covers variance-based code construction, Pauli weight distribution analysis, higher-than-stabilizer detection rates, and trade-offs between detection capability and code rate. Use when working on: quantum error detection (not correction), non-stabilizer codes, Pauli group geometry, variance codes, or codes with detection rates exceeding stabilizer bound...Votes: 0GitHub stars: 3
- Partner Capability Estimation For Task Agnostic AdPartner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc TeamworkVotes: 0GitHub stars: 3
- Parallelizing Linear Recurrent Neural Nets Over Sequence Length**arXiv ID:** 1709.04057 **Authors:** Eric Martin, Chris Cundy **Published:** 2017-09-12T20:52:22Z **Abstract:** Recurrent neural networks (RNNs) are widely used to model sequential data but their non-linear dependencies between sequence elements prevent parallelizing training over sequence length. We show the training of RNNs with only linear sequential dependencies can be parallelized over the sequence length using the parallel scan algorithm, leading to rapid training on long sequences eve...Votes: 0GitHub stars: 3
- Paradoxical Noise Preference In Rnns**arXiv ID:** 2601.04539 **Authors:** Noah Eckstein, Manoj Srinivasan **Published:** 2026-01-08T03:11:51Z **Abstract:** In recurrent neural networks (RNNs) used to model biological neural networks, noise is typically introduced during training to emulate biological variability and regularize learning. The expectation is that removing the noise at test time should preserve or improve performance. Contrary to this intuition, we find that continuous-time RNNs (CTRNNs) often perform best at or ne...Votes: 0GitHub stars: 3
- Optimizing Coordinative Schedules For Tanker Terminals An Intelligent Large Spatialtemporal Datadriven Approach Part 2**arXiv ID:** 2204.03955 **Authors:** Deqing Zhai, Xiuju Fu, Xiao Feng Yin, Haiyan Xu, Wanbing Zhang, Ning Li **Published:** 2022-04-08T09:30:23Z **Abstract:** In this study, a novel coordinative scheduling optimization approach is proposed to enhance port efficiency by reducing weighted average turnaround time. The proposed approach is developed as a heuristic algorithm applied and investigated through different observation windows with weekly rolling horizon paradigm method. The experimenta...Votes: 0GitHub stars: 3
- Optimizing Coordinative Schedules For Tanker Terminals An Intelligent Large Spatialtemporal Datadriven Approach Part 1**arXiv ID:** 2204.03899 **Authors:** Deqing Zhai, Xiuju Fu, Xiao Feng Yin, Haiyan Xu, Wanbing Zhang, Ning Li **Published:** 2022-04-08T07:56:32Z **Abstract:** In this study, a novel coordinative scheduling optimization approach is proposed to enhance port efficiency by reducing average wait time and turnaround time. The proposed approach consists of enhanced particle swarm optimization (ePSO) as kernel and augmented firefly algorithm (AFA) as global optimal search. Two paradigm methods of th...Votes: 0GitHub stars: 3
- Optimising Communication Overhead In Federated Learning Using Nsgaii**arXiv ID:** 2204.02183 **Authors:** José Ángel Morell, Zakaria Abdelmoiz Dahi, Francisco Chicano, Gabriel Luque, Enrique Alba **Published:** 2022-04-01T18:06:20Z **Abstract:** Federated learning is a training paradigm according to which a server-based model is cooperatively trained using local models running on edge devices and ensuring data privacy. These devices exchange information that induces a substantial communication load, which jeopardises the functioning efficiency. The difficulty...Votes: 0GitHub stars: 3
- On The Principles Of Relu Networks With One Hidden Layer**arXiv ID:** 2411.06728 **Authors:** Changcun Huang **Published:** 2024-11-11T05:51:11Z **Abstract:** A neural network with one hidden layer or a two-layer network (regardless of the input layer) is the simplest feedforward neural network, whose mechanism may be the basis of more general network architectures. However, even to this type of simple architecture, it is also a ``black box''; that is, it remains unclear how to interpret the mechanism of its solutions obtained by the back-propagat...Votes: 0GitHub stars: 3
- On Nonlinear Operators For Geometric Deep Learning**arXiv ID:** 2207.03485 **Authors:** Grégoire Sergeant-Perthuis, Jakob Maier, Joan Bruna, Edouard Oyallon **Published:** 2022-07-06T06:45:33Z **Abstract:** This work studies operators mapping vector and scalar fields defined over a manifold $\mathcal{M}$, and which commute with its group of diffeomorphisms $\text{Diff}(\mathcal{M})$. We prove that in the case of scalar fields $L^p_ω(\mathcal{M,\mathbb{R}})$, those operators correspond to point-wise non-linearities, recovering and extending k...Votes: 0GitHub stars: 3
- On Convergence And Stability Of Gans**arXiv ID:** 1705.07215 **Authors:** Naveen Kodali, Jacob Abernethy, James Hays, Zsolt Kira **Published:** 2017-05-19T22:41:56Z **Abstract:** We propose studying GAN training dynamics as regret minimization, which is in contrast to the popular view that there is consistent minimization of a divergence between real and generated distributions. We analyze the convergence of GAN training from this new point of view to understand why mode collapse happens. We hypothesize the existence of undesir...Votes: 0GitHub stars: 3
- New Feature For Complex Network Based On Ant Colony Optimization For High Level Classification**arXiv ID:** 2008.12884 **Authors:** Josimar E. Chire-Saire **Published:** 2020-08-29T00:22:43Z **Abstract:** Low level classification extracts features from the elements, i.e. physical to use them to train a model for a later classification. High level classification uses high level features, the existent patterns, relationship between the data and combines low and high level features for classification. High Level features can be got from Complex Network created over the data. Local and gl...Votes: 0GitHub stars: 3
- Neuroncentric Hebbian Learning**arXiv ID:** 2403.12076 **Authors:** Andrea Ferigo, Elia Cunegatti, Giovanni Iacca **Published:** 2024-02-16T17:38:28Z **Abstract:** One of the most striking capabilities behind the learning mechanisms of the brain is the adaptation, through structural and functional plasticity, of its synapses. While synapses have the fundamental role of transmitting information across the brain, several studies show that it is the neuron activations that produce changes on synapses. Yet, most plasticity mo...Votes: 0GitHub stars: 3
- Neural Constraint Satisfaction Hierarchical Abstraction For Combinatorial Generalization In Object Rearrangement**arXiv ID:** 2303.11373 **Authors:** Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang **Published:** 2023-03-20T18:19:36Z **Abstract:** Object rearrangement is a challenge for embodied agents because solving these tasks requires generalizing across a combinatorially large set of configurations of entities and their locations. Worse, the representations of these entities are unknown and must be inferred from sensory percepts. We present a hierarch...Votes: 0GitHub stars: 3
- Nahid Aibased Algorithm For Operating Fullyautomatic Surgery**arXiv ID:** 2401.08584 **Authors:** Sina Saadati **Published:** 2023-11-03T12:35:07Z **Abstract:** In this paper, for the first time, a method is presented that can provide a fully automated surgery based on software and computer vision techniques. Then, the advantages and challenges of computerization of medical surgery are examined. Finally, the surgery related to isolated ovarian endometriosis disease has been examined, and based on the presented method, a more detailed algorithm is pres...Votes: 0GitHub stars: 3
- Multivariate Time Series Anomaly Detection With Few Positive Samples**arXiv ID:** 2207.00705 **Authors:** Feng Xue, Weizhong Yan **Published:** 2022-07-02T00:58:52Z **Abstract:** Given the scarcity of anomalies in real-world applications, the majority of literature has been focusing on modeling normality. The learned representations enable anomaly detection as the normality model is trained to capture certain key underlying data regularities under normal circumstances. In practical settings, particularly industrial time series anomaly detection, we often enco...Votes: 0GitHub stars: 3
- Multitask Learning On Networks**arXiv ID:** 2112.04891 **Authors:** Andrea Ponti **Published:** 2021-12-07T09:13:10Z **Abstract:** The multi-task learning (MTL) paradigm can be traced back to an early paper of Caruana (1997) in which it was argued that data from multiple tasks can be used with the aim to obtain a better performance over learning each task independently. A solution of MTL with conflicting objectives requires modelling the trade-off among them which is generally beyond what a straight linear combination can...Votes: 0GitHub stars: 3
- Multiobjective Qualitydiversity For Crystal Structure Prediction**arXiv ID:** 2403.17164 **Authors:** Hannah Janmohamed, Marta Wolinska, Shikha Surana, Thomas Pierrot, Aron Walsh, Antoine Cully **Published:** 2024-03-25T20:29:04Z **Abstract:** Crystal structures are indispensable across various domains, from batteries to solar cells, and extensive research has been dedicated to predicting their properties based on their atomic configurations. However, prevailing Crystal Structure Prediction methods focus on identifying the most stable solutions that lie a...Votes: 0GitHub stars: 3
- Multilabel Classification Method Based On Extreme Learning Machines**arXiv ID:** 1608.08435 **Authors:** Rajasekar Venkatesan, Meng Joo Er **Published:** 2016-08-30T13:08:06Z **Abstract:** In this paper, an Extreme Learning Machine (ELM) based technique for Multi-label classification problems is proposed and discussed. In multi-label classification, each of the input data samples belongs to one or more than one class labels. The traditional binary and multi-class classification problems are the subset of the multi-label problem with the number of labels corr...Votes: 0GitHub stars: 3
- Motion Planning Of An Autonomous Mobile Robot Using Artificial Neural Network**arXiv ID:** 1207.4931 **Authors:** G. N. Tripathi, V. Rihani **Published:** 2012-07-20T12:15:12Z **Abstract:** The paper presents the electronic design and motion planning of a robot based on decision making regarding its straight motion and precise turn using Artificial Neural Network (ANN). The ANN helps in learning of robot so that it performs motion autonomously. The weights calculated are implemented in microcontroller. The performance has been tested to be excellent.Votes: 0GitHub stars: 3
- Metis Memory Foundation ModelMetis: Memory Foundation ModelVotes: 0GitHub stars: 3