DevOps & Cloud

Deployment, cloud, containers, CI/CD, monitoring, and infrastructure

skills in category: 20,291
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Showing 7,273–7,296 of 20,291 skills

Strategies For Optimizing Endtoend Artificial Intelligence Pipelines On Intel Xeon ProcessorsA**arXiv ID:** 2211.00286 **Authors:** Meena Arunachalam, Vrushabh Sanghavi, Yi A Yao, Yi A Zhou, Lifeng A Wang, Zongru Wen, Niroop Ammbashankar, Ning W Wang, Fahim Mohammad **Published:** 2022-11-01T05:45:04Z **Abstract:** End-to-end (E2E) artificial intelligence (AI) pipelines are composed of several stages including data preprocessing, data ingestion, defining and training the model, hyperparameter optimization, deployment, inference, postprocessing, followed by downstream analyses. To obta...devopsbashperformanceVotes: 0GitHub stars: 3
Split Federated Learning Resource OptimizationAEfficient SFL optimization with polynomial-time splitting.devopsgoperformanceVotes: 0GitHub stars: 3
Slorr In Training Low Rank RegularizationALow-rank factorization for neural network compression that directly regularizes weight matrices using GPU-friendly approximations. Stateless, architecture-preserving, with less than 8% training overhead. Evaluated on ImageNet and LLM pretraining at 135M and 560M scales. Use when working with low-rank-regularization, model-compression, neural-network-compression.devopspythongoVotes: 0GitHub stars: 3
Scalable Qst Neural ArchitecturesABenchmarking neural network architectures for scalable Quantum State Tomography with memristor-based acceleration patternsdevopsgoperformanceVotes: 0GitHub stars: 3
Scalable Memristive Reservoir ComputingAScalable Memristive-Friendly Reservoir Computing for time series classification using Memristive-Friendly Echo State Networks (MF-ESN). Combines memristive device physics with reservoir computing for efficient time-series classification. Trigger words: memristive reservoir computing, MF-ESN, echo state network, memristor time series, hardware reservoir, memristive ESN, time series classification, reservoir computing classification.devopspythonperformanceVotes: 0GitHub stars: 3
Reversible Sparse Moe Single NodeATrain hundred-billion-parameter sparse MoE models on single nodes using reversible recurrence stacks and state-preserving growth principles with TQP optimizer strategy.devopsnodeexpressVotes: 0GitHub stars: 3
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Qif Superior Lif Gradient DescentAQuadratic Integrate-and-Fire (QIF) neurons outperform LIF in spike-based gradient descent trainingdevopspythonnodeVotes: 0GitHub stars: 3
Persona Pruner Lightweight RoleplayingAPersona-Pruner methodology for sculpting lightweight language models for role-playing tasks. Enables efficient pruning of LMs while preserving persona-consistent stylized interactions. Use when: model pruning for role-playing, lightweight character chatbots, persona-preserving model compression, efficient LM distillation.devopspythonVotes: 0GitHub stars: 3
Partial Fusion Neural NetworksAPartial fusion of neural networks interpolating between ensembles and weight aggregation via neuron-level similarity matching and partial optimal transport. Frames partial fusion as generalized pruning where neurons are deleted or linearly combined.devopsgogitVotes: 0GitHub stars: 3
Meta Learning In Context Brain DecodingAMeta-learning In-Context approach for training-free cross-subject brain decoding. Enables zero-calibration BCI through context-based meta-learning. Triggers: meta-learning, brain decoding, cross-subject, training-free, in-context learning, zero-calibration BCI.devopspythonapiVotes: 0GitHub stars: 3
Kernel Hopfield Event Driven RetrievalAEvent-driven asynchronous retrieval in Kernel Logistic Regression (KLR) Hopfield networks for neuromorphic associative memory. Covers asynchronous update dynamics, large-margin attractor energy landscapes, and sparse event-driven computation. Use when: (1) building neuromorphic associative memory systems, (2) optimizing Hopfield network retrieval for energy efficiency, (3) analyzing event-driven neural computation, (4) studying kernel-based associative memories, or (5) comparing synchronous v...devopsVotes: 0GitHub stars: 3
Evolutionary Multiobjective Fusion Of Deepfake Speech DetectorsA**arXiv ID:** 2604.01330 **Authors:** Vojtěch Staněk, Martin Perešíni, Lukáš Sekanina, Anton Firc, Kamil Malinka **Published:** 2026-04-01T19:17:59Z **Abstract:** While deepfake speech detectors built on large self-supervised learning (SSL) models achieve high accuracy, employing standard ensemble fusion to further enhance robustness often results in oversized systems with diminishing returns. To address this, we propose an evolutionary multi-objective score fusion framework that jointly mini...devopsperformanceVotes: 0GitHub stars: 3
Deep Learning Closed Loop Tms BciADeep learning in brain-computer interfaces with closed-loop transcranial magnetic stimulation. Combines real-time EEG processing with adaptive TMS for neurological therapy. (arXiv:2604.11608, 2026-04-12)devopsgogitVotes: 0GitHub stars: 3
Beyond Backpropagation Monte Carlo Train Deep NetworksAShows that simple Monte Carlo random mutation can train deep neural networks without gradients. No batch normalization or residual connections needed. Supports pure pruning training, discrete weights, and unconventional transfer functions. Demonstrated on 20+ layer networks and Transformer architectures. Use when working with gradient-free-training, monte-carlo-method, deep-neural-network-training.devopspythongoVotes: 0GitHub stars: 3
Unified Multimodal Financial Ai FrameworkAUnified multi-modal framework integrating PPO robo-advisory, HFT prediction, in-context investment advisory, game-theoretic banking, and cross-modal sentiment analysis. Use when: unified financial AI systems, multi-domain financial AI, robo-advisory optimization, high-frequency trading, competitive banking strategy, cross-modal financial sentiment.devopsVotes: 0GitHub stars: 3
Federated Cognitive Digital Twins Edge CloudAFederated Cognitive Digital Twin (FCDT) architecture methodology combining federation and cognition within a unified approach for distributed Cyber-Physical Systems (CPSs).devopsgitsecurityVotes: 0GitHub stars: 3
Fdnml Cognitive Fatigue DetectionAFractional Dynamical Networks-based Machine Learning (FDNML) for EEG cognitive fatigue detection using coupled fractional-order differential equations, multifractal analysis, and Wasserstein distance metrics. Activation: cognitive fatigue, fractional dynamics, EEG fatigue, non-Markovian brain modeling, multifractal analysis, state transition detection.devopsgoperformanceVotes: 0GitHub stars: 3
Expert Analysis Evaluation Quantum PortfolioAExpert Analysis Evaluation framework bridging computational optimization and practical viability in quantum portfolio optimization. Financial professionals assess economic soundness and market feasibility of quantum-optimized portfolios beyond algorithmic metrics. Based on arXiv:2507.20532v1.devopsgoperformanceVotes: 0GitHub stars: 3
Event Based Neural Decoding NeuroprostheticAEvent-based neural decoding framework using spiking GRU with sparse graded spikes for efficient on-device motor control. Achieves >90% decoding accuracy with <1mW power consumption on neuromorphic hardware for neuroprosthetic applications.devopspythonperformanceVotes: 0GitHub stars: 3
Era Entropy Token Pruning MllmAERA (Entropy-guided Visual Token Pruning with Rectified Attention) for efficient Multimodal Large Language ModelsdevopspythongoVotes: 0GitHub stars: 3
Equilibrium Propagation Lif SnnAEquilibrium Propagation (EP) with Predictive Learning in Leaky Integrate-and-Fire Spiking Neural Networks. Biologically plausible alternative to backpropagation for training SNNs using energy-based two-phase learning. Use when training SNNs without backpropagation through time, implementing biologically realistic learning rules, or applying equilibrium-based optimization to spiking neuron networks.devopsgoperformanceVotes: 0GitHub stars: 3
Equation Free Digital TwinsAEquation-free digital twin framework using Koopman operator theory and Hankel-matrix embeddings for real-time structural state reconstruction without physical models. Use when: (1) building digital twins for complex engineering structures, (2) virtual sensing from partial observations, (3) Koopman-based system identification, (4) real-time monitoring of nonlinear structural dynamics.devopsgitVotes: 0GitHub stars: 3
Emrformer Neuromorphic AmrAEMRFormer: End-to-End Radar and Communication Modulation Recognition with Neuromorphic Computing using spike-driven transformers on KA200 neuromorphic chip. Achieves SOTA accuracy with 90%+ energy reduction. (arXiv: 2606.24075)devopsgoperformanceVotes: 0GitHub stars: 3
Embedded Quantum Machine LearningAFeasibility analysis and hybrid architecture design for embedding quantum machine learning workloads in resource-constrained embedded systems. Explores the intersection of quantum computing and edge/embedded deployment. Use for: embedded quantum ML feasibility, edge quantum computing, hybrid quantum-classical embedded architectures, quantum workload optimization for constrained systems. Triggered by: embedded quantum ML, edge quantum computing, quantum embedded systems, hybrid quantum embedde...devopspythonVotes: 0GitHub stars: 3