All categories
DevOps & Cloud
Deployment, cloud, containers, CI/CD, monitoring, and infrastructure
- 20,289
- 846
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 devops & cloud skills
Showing 7,153–7,176 of 20,289 skills
- Rag Har Towards Cost Efficient Llm Based Human ActRAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge DeploymentVotes: 0GitHub stars: 3
- R3 Advertisement Compliance Rectification Via Group Relative ExperienceRigorous content moderation is crucial for online advertising but leads to millions of daily rejections. This scale renders manual rectification infeasible, particularly for video advertisements. Howe. Based on arXiv:2607.07318.Votes: 0GitHub stars: 3
- Physics Informed Llm Pid TuningA physics-informed framework that uses Large Language Model agents for PID tuning of chemical processes, combining closed-loop response features, control-engineering diagnoses, and physics-informed reinforcement learning.Votes: 0GitHub stars: 3
- Openai Privacy FilterMethodology for building PII detection and redaction models using bidirectional token-classification with span decoding and constrained Viterbi. Based on OpenAI Privacy Filter (1.5B params, 50M active, 128K context). Use when implementing privacy-preserving NLP pipelines, PII detection systems, or data redaction workflows.Votes: 0GitHub stars: 3
- Onnes Physics Grounded Llm Digital TwinOnnes methodology — physics-grounded digital twin simulator driving multi-agent LLM operations layer for cryogenic fault diagnosis. Combines forward physics model with learned noise fingerprint, enabling zero-shot fault classification via contrastive few-shot demonstrations and self-consistency voting.Votes: 0GitHub stars: 3
- Online Safety Monitoring LlmOnline safety monitoring methodology for LLMs at deployment time. Uses verifier signals from external models with calibrated thresholding via risk control to raise alarms when safety can no longer be assumed. Use when deploying LLMs and needing real-time safety monitoring, calibrating safety thresholds, or designing deployment-time guardrails. Simpler than sequential hypothesis testing approaches. Activation: LLM safety monitoring, deployment safety, real-time alarm, verifier thresholding, ri...Votes: 0GitHub stars: 3
- On Accelerating Edge Ai Optimizing Resourceconstrained Environments**arXiv ID:** 2501.15014 **Authors:** Jacob Sander, Achraf Cohen, Venkat R. Dasari, Brent Venable, Brian Jalaian **Published:** 2025-01-25T01:37:03Z **Abstract:** Resource-constrained edge deployments demand AI solutions that balance high performance with stringent compute, memory, and energy limitations. In this survey, we present a comprehensive overview of the primary strategies for accelerating deep learning models under such constraints. First, we examine model compression techniques-pru...Votes: 0GitHub stars: 3
- Mmoe Diffusion Transformer Expert DesignModernMOE (MMOE) methodology for modernizing diffusion transformers with efficient expert design. Adapts routed experts, shared and lightweight experts, gate-residual routing, and attention-residual information reuse to AIGC generation.Votes: 0GitHub stars: 3
- Maestro Pruning Bad Experts Mixture Of ExpertsMAESTRO: Markov-chain Approximated Expert Sparsification via Transition-based Routing for MoE structured pruning. Models expert activation as Ergodic Markov chains for globally-aware importance. Outperforms baselines by 10.61% at 50% compression. Lower cross-task variance. Activation: mixture-of-experts, expert pruning, MoE deployment, structured pruning, language model efficiency.Votes: 0GitHub stars: 3
- Llm Judge Theory Agnostic Personality RecognitionJAM: theory-agnostic framework for personality recognition using LLM-as-a-Judge for adaptive metric alignment. Attention-Pooled Graph Prototypical Network with Cross-Theory Harmonization. LLM operates in before-the-loop and in-the-loop configurations for ambiguous sample identification. Use when working with llm-as-judge, personality-recognition, theory-agnostic.Votes: 0GitHub stars: 3
- Llama CppRuns LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.Votes: 0GitHub stars: 3
- Illusion Of Equivalency Quantization Effects LlmsShows that post-training quantization evaluation via accuracy/perplexity fails to capture behavioral changes. Introduces correctness agreement metric. Reveals non-linear breakpoints at low bit-widths. Query/key projections more sensitive than value/output. Activation: quantization, LLM deployment, behavioral change, correctness agreement, post-training quantization.Votes: 0GitHub stars: 3
- Dominotree Conditional Tree Structured Drafting Speculative DecodingTraining-free best-first draft tree for speculative decoding using Domino's conditional non-factorized correction. Achieves up to 6.6x speedup on Qwen3-4B and highest mean accept length (10.7 tokens/round). GPU-native CUDA-graph builder for efficient tree construction. Activation: speculative decoding, tree-structured drafting, Domino conditioning, LLM inference, block-diffusion.Votes: 0GitHub stars: 3
- Cascaded Transformer For Robust And Scalable Sla Decomposition Via Amortized Optimization**arXiv ID:** 2601.11859 **Authors:** Cyril Shih-Huan Hsu **Published:** 2026-01-17T01:01:53Z **Abstract:** The evolution toward 6G networks increasingly relies on network slicing to provide tailored, End-to-End (E2E) logical networks over shared physical infrastructures. A critical challenge is effectively decomposing E2E Service Level Agreements (SLAs) into domain-specific SLAs, which current solutions handle through computationally intensive, iterative optimization processes that incur sub...Votes: 0GitHub stars: 3
- Arxiv 2608 19994 Green Boa Determining The Environmental Break EvenGreen BOA: Determining the environmental break-even point for ML-based data compression (arXiv: 2608.19994)Votes: 0GitHub stars: 3
- Arxiv 2608 19882 Testnav Pareto Guided Search For Compositional RobTESTNAV: Pareto-Guided Search for Compositional Robustness Testing (arXiv: 2608.19882)Votes: 0GitHub stars: 3
- A Computer Vision Enabled Damage Detection Model With Improved Yolov5 Based On Transformer Prediction Head**arXiv ID:** 2303.04275 **Authors:** Arunabha M. Roy, Jayabrata Bhaduri **Published:** 2023-03-07T22:53:36Z **Abstract:** Objective:Computer vision-based up-to-date accurate damage classification and localization are of decisive importance for infrastructure monitoring, safety, and the serviceability of civil infrastructure. Current state-of-the-art deep learning (DL)-based damage detection models, however, often lack superior feature extraction capability in complex and noisy environments, ...Votes: 0GitHub stars: 3
- Winner Take All SpikingWinner-Take-All (WTA) Spiking Transformer for energy-efficient language modeling. Softmax-free, spike-driven self-attention (WSSA/CWSSA), encoder and decoder architectures. Activation: wta spiking transformer, winner-take-all spiking, softmax-free attention, spiking language model, wd-spikingformer, we-spikingformerVotes: 0GitHub stars: 3
- Vo2 Mott Oscillator Spiking NeuronsMonolithically integrated VO2 Mott oscillators for energy-efficient spiking neurons. Metal-insulator transition devices for neuromorphic hardware. Activation: VO2 oscillator, Mott neuron, metal-insulator transition, energy-efficient spiking.Votes: 0GitHub stars: 3
- Vo2 Mott Oscillator Spiking Neurons V2Monolithically integrated VO2 Mott phase-transition oscillators for compact, energy-efficient hardware implementation of spiking neurons. Compatible with large-scale CMOS integration for brain-inspired non-Boolean computing.Votes: 0GitHub stars: 3
- Vae Quantum EmbeddingVariational Autoencoder (VAE) framework for learning task-specific quantum embeddings of classical data. Compresses high-dimensional datasets (including ImageNet) into compact quantum representations (e.g., 13-qubit) while remaining reconstructable through a learned decoder. Achieves polynomial-measurement reconstruction (vs. full tomography for amplitude embeddings or circuit inversion for angle embeddings). Validated on IBM quantum hardware with stable embeddings under real device noise. Us...Votes: 0GitHub stars: 3
- Unispike Snn AccelerationUniSpike methodology for accelerating Spiking Neural Networks (SNNs) on many-core neuromorphic systems by eliminating address redundancy in packet-based spike communication. Combines destination-centric spike scheduling, lightweight runtime packet assembly hardware, and destination-aware SNN partitioning to reduce traffic, improve speed, and increase energy efficiency. Use when: (1) optimizing SNN inference on neuromorphic hardware, (2) designing spike communication protocols for many-core ne...Votes: 0GitHub stars: 3
- Unifying Von Neumann Hpc Neuromorphic Ebbrains统一冯诺依曼HPC与神经形态计算的EBRAINS工作流框架 - 透明跨平台执行SNN，trigger_words: neuromorphic computing. spiking neural network. SNN. HPC. EBRAINS. SpiNNaker. cross-platform. containerization. NESTMLVotes: 0GitHub stars: 3
- Tta Eeg Foundation ModelsTest-time adaptation (TTA) methods for EEG foundation models under real-world distribution shifts. Covers entropy minimization, batch norm adaptation, TENT, MEMO, CoTTA, EATA, SAR, and related methods applied to EEG signals across cross-device, cross-subject, cross-session, and cross-clinical-site settings. Addresses privacy-preserving adaptation without source data access.Votes: 0GitHub stars: 3