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- Eeg Preprocessing ReliabilityEEG preprocessing reliability methodology for quantifying and mitigating preprocessing-induced prediction instability in EEG deep learning. Based on arXiv:2605.07212 (Hou et al., 2026). Use when: (1) evaluating EEG model robustness to preprocessing pipeline choices, (2) designing preprocessing-stable BCI/decoding systems, (3) analyzing counterfactory prediction flip rates (CFR), (4) implementing Preprocessing Uncertainty (PU) diagnostics, (5) applying NA-PGI regularization for graph-structure...Votes: 0GitHub stars: 3
- Eeg Microstate Variational EmbeddingInterpretable EEG microstate discovery via variational deep embedding with systematic architecture search and multi-quadrant evaluation. Uses deep variational methods for data-driven microstate identification instead of traditional k-means clustering on GFP peaks. Provides principled uncertainty quantification and scalable EEG analysis pipeline. Use when performing EEG microstate analysis, building interpretable EEG pipelines, or comparing microstate discovery methods. arXiv: 2605.10947 (cs.L...Votes: 0GitHub stars: 3
- Eeg Hopfield Emotion EnergyEnergy landscapes for quantifying brain network stability during emotional processing. Uses Hopfield network energy framework to analyze EEG dynamics, mapping emotional states to attractor basins in brain network energy landscapes. Provides a physics-based framework for understanding emotional stability and transitions. Activation: energy landscape, Hopfield network emotion, brain network stability, emotional processing EEG, attractor dynamics, affective neuroscience, 能量景观, 情绪脑网络, 吸引子动力学Votes: 0GitHub stars: 3
- Eeg Foundation Model Burst Suppression IcuFirst comprehensive evaluation of EEG Foundation Models (FMs) for burst suppression detection in reduced-montage ICU EEG without patient-specific calibration. **REVE-base achieves highest event-based F1-score (0.868)**, reducing burst-per-minute error by 52.1% and 36.2% compared to EEGNet and adaptive thresholding respectively. Demonstrates FMs enable scalable EEG monitoring in clinical settings.Votes: 0GitHub stars: 3
- Eeg Digital Twin Autonomous DrivingEEG-fused digital twin brain framework for autonomous driving in virtual scenarios. Combines biophysical brain models with EEG data and digital twin technology for driver state monitoring and vehicle control. Applies to: brain-computer interfaces, autonomous driving, driver monitoring, digital twin brains. Activation: eeg digital twin, autonomous driving brain, driver state monitoring, virtual scenario brain, EEG-fused brain model.Votes: 0GitHub stars: 3
- Eeg Criticality Deep Sleep Classification NeurofeedbackDeep Sleep Classification via EEG Signal Criticality using Detrended Fluctuation Analysis (DFA) for passive Brain-Computer Interface (pBCI) neurofeedback applications. Probabilistic decoding of EEG criticality features for state-dependent sleep improvement interventions.Votes: 0GitHub stars: 3
- Eeg Biomarker Robustness Cross PopulationCross-population framework for evaluating robustness and generalizability of EEG biomarkers in multi-site clinical settings. Addresses cross-subject and cross-platform variation for reliable Parkinson's disease detection. Keywords: EEG biomarkers, cross-population, generalization, multi-site, Parkinson's disease, clinical reliability.Votes: 0GitHub stars: 3
- Dual Timescale Memory Spiking Neuron AstrocyteDual-timescale memory mechanism in spiking neuron-astrocyte networks (SNAN). Combines STDP-based long-term memory with astrocytic short-term suppression for efficient navigation and working memory. Activation: dual timescale memory, SNAN, spiking neuron astrocyte network, astrocyte working memory, topological context memory, neuromorphic navigation.Votes: 0GitHub stars: 3
- Dual Timescale Memory AstrocyteDual-timescale memory mechanism in spiking neuron-astrocyte networks. Astrocytes provide slow-timescale modulation complementing fast spiking dynamics, enabling energy-efficient learning of environmental patterns and persistent memory traces.Votes: 0GitHub stars: 3
- Dual Pathway Robust Learning ControlDual-pathway architecture for provably robust learning-based control combining neural network feedforward estimation with conventional feedback correction. Use when designing learning-enabled control systems requiring both performance and robustness guarantees, particularly for robotics, aerospace, quantum control, or any system with out-of-distribution deployment concerns.Votes: 0GitHub stars: 3
- Dual Node Dgx Spark Distributed Llm TrainingDual-node DGX Spark LLM training over Tailscale.Votes: 0GitHub stars: 3
- Disaggregate Secure Aggregation FlDisAgg protocol for efficient secure aggregation in federated learning using distributed aggregator committees, eliminating homomorphic encryption overhead while preserving privacy.Votes: 0GitHub stars: 3
- Direct To Event Snn TransferDirect-to-Event (D2E) Spiking Neural Network Transfer methodology. Converts direct-coded SNNs trained with floating-point inputs into energy-efficient event-based representations using Self-Knowledge Distillation (SKD). Activation: SNN transfer, direct-to-event, D2E, event-based SNN, neuromorphic deployment, TTFS, self-knowledge distillation, SNN energy efficiency.Votes: 0GitHub stars: 3
- Detecting Reducing Scheming AiEvaluation methodology for detecting hidden misalignment ("scheming") in AI models and concrete methods for reducing deceptive behavior.Votes: 0GitHub stars: 3
- Spike Sparsity Deployment Cost JetsonAnalysis of when spike sparsity in Spiking Neural Networks translates to actual deployment cost savings on commodity edge-GPU platforms. Study of VS-WNO on Jetson Orin Nano with benchmarking methodology.Votes: 0GitHub stars: 3
- Selective Task Offloading For Maximum Inference Accuracy And Energy Efficient Realtime Iot Sensing Systems**arXiv ID:** 2402.16904 **Authors:** Abdelkarim Ben Sada, Amar Khelloufi, Abdenacer Naouri, Huansheng Ning, Sahraoui Dhelim **Published:** 2024-02-24T18:46:06Z **Abstract:** The recent advancements in small-size inference models facilitated AI deployment on the edge. However, the limited resource nature of edge devices poses new challenges especially for real-time applications. Deploying multiple inference models (or a single tunable model) varying in size and therefore accuracy and power co...Votes: 0GitHub stars: 3
- Pqc Tls DeploymentPost-quantum cryptography TLS deployment methodology. Automated configuration parsing and hybrid PQC (ML-KEM/ML-DSA) deployment patterns for enterprise infrastructure. Use when: deploying post-quantum cryptography, migrating TLS to PQC standards, auditing cryptographic configurations, implementing hybrid key exchanges (MLKEM + X25519), managing crypto-agility in enterprise web server stacks (Nginx, Apache, API gateways), performing cryptographic inventory and exposure assessment, or planning ...Votes: 0GitHub stars: 3
- Posthoc Domain Adaptation Via Guided Data Homogenization**arXiv ID:** 2104.03624 **Authors:** Kurt Willis, Luis Oala **Published:** 2021-04-08T09:18:48Z **Abstract:** Addressing shifts in data distributions is an important prerequisite for the deployment of deep learning models to real-world settings. A general approach to this problem involves the adjustment of models to a new domain through transfer learning. However, in many cases, this is not applicable in a post-hoc manner to deployed models and further parameter adjustments jeopardize safety...Votes: 0GitHub stars: 3
- Ofa2 A Multiobjective Perspective For The Onceforall Neural Architecture Search**arXiv ID:** 2303.13683 **Authors:** Rafael C. Ito, Fernando J. Von Zuben **Published:** 2023-03-23T21:30:29Z **Abstract:** Once-for-All (OFA) is a Neural Architecture Search (NAS) framework designed to address the problem of searching efficient architectures for devices with different resources constraints by decoupling the training and the searching stages. The computationally expensive process of training the OFA neural network is done only once, and then it is possible to perform multipl...Votes: 0GitHub stars: 3
- Noisetolerant Coresetbased Class Incremental Continual Learning**arXiv ID:** 2504.16763 **Authors:** Edison Mucllari, Aswin Raghavan, Zachary Alan Daniels **Published:** 2025-04-23T14:34:20Z **Abstract:** Many applications of computer vision require the ability to adapt to novel data distributions after deployment. Adaptation requires algorithms capable of continual learning (CL). Continual learners must be plastic to adapt to novel tasks while minimizing forgetting of previous tasks.However, CL opens up avenues for noise to enter the training pipeline a...Votes: 0GitHub stars: 3
- Grinqh Adaptive Quantization HierarchyGraded Input-based Quantization Hierarchy for efficient LLM generation. Dynamic precision assignment based on activation magnitudes as computational importance proxy. Unifies quantization and sparsification.Votes: 0GitHub stars: 3
- Faultaware Design And Training To Enhance Dnns Reliability With Zerooverhead**arXiv ID:** 2205.14420 **Authors:** Niccolò Cavagnero, Fernando Dos Santos, Marco Ciccone, Giuseppe Averta, Tatiana Tommasi, Paolo Rech **Published:** 2022-05-28T13:09:30Z **Abstract:** Deep Neural Networks (DNNs) enable a wide series of technological advancements, ranging from clinical imaging, to predictive industrial maintenance and autonomous driving. However, recent findings indicate that transient hardware faults may corrupt the models prediction dramatically. For instance, the radiat...Votes: 0GitHub stars: 3
- Evolutionary Architecture Search Through Grammarbased Sequence Alignment**arXiv ID:** 2512.04992 **Authors:** Adri Gómez Martín, Felix Möller, Steven McDonagh, Monica Abella, Manuel Desco, Elliot J. Crowley, Aaron Klein, Linus Ericsson **Published:** 2025-12-04T16:57:49Z **Abstract:** Neural architecture search (NAS) in expressive search spaces is a computationally hard problem, but it also holds the potential to automatically discover completely novel and performant architectures. To achieve this we need effective search algorithms that can identify powerful com...Votes: 0GitHub stars: 3
- Effective Pruning Task Trained RnnPrunes RNNs using noise fluctuations and rescaling.Votes: 0GitHub stars: 3