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Code review, refactoring, debugging, maintainability, and engineering quality
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- Graph Regularized Eeg EmotionGraph-regularized deep learning framework for EEG-based emotion recognition with psychologically-grounded label structure. Introduces Graph Label Smoothing, Graph Laplacian Commuting Distance, and Sliced Wasserstein Distance regularization strategies. Use when working with EEG emotion classification, affective BCI, SEED datasets, emotion topology, or graph-regularized neural networks for affective computing.Votes: 0GitHub stars: 3
- Gram Dual Use Knowledge ControlGradient-Routed Auxiliary Modules (GRAM) methodology for surgical control of dual-use knowledge in AI models. Enables removable knowledge compartments without retraining separate models.Votes: 0GitHub stars: 3
- Global Mean Amplitude Snn CimGlobal mean-amplitude feedback-enhanced spiking neural network coherent ising machine (GFSNN-CIM) with physics-driven amplitude stabilization. Solves Max-Cut with 27% improvement vs conventional SNN-CIM, validated on traffic assignment problems. Based on Jiang, Ma, Wang & Wang (arXiv: 2509.13917). Use when solving combinatorial optimization with spiking neural networks, implementing coherent ising machines, or applying mean-amplitude feedback stabilization to SNN optimizers.Votes: 0GitHub stars: 3
- Vectorized Conditional Neural Fields A Framework For Solving Timedependent Parametric Partial Differential Equations**arXiv ID:** 2406.03919 **Authors:** Jan Hagnberger, Marimuthu Kalimuthu, Daniel Musekamp, Mathias Niepert **Published:** 2024-06-06T10:02:06Z **Abstract:** Transformer models are increasingly used for solving Partial Differential Equations (PDEs). Several adaptations have been proposed, all of which suffer from the typical problems of Transformers, such as quadratic memory and time complexity. Furthermore, all prevalent architectures for PDE solving lack at least one of several desirable pr...Votes: 0GitHub stars: 3
- Streaming Attention Space OptimizationSpace-efficient streaming attention approximation using tight bounds for KV cache compression in transformer architectures.Votes: 0GitHub stars: 3
- Sqaud Sub Quadratic Attention DistillationSQuad: Sub-quadratic attention distillation for video.Votes: 0GitHub stars: 3
- Open Mopd Multi Teacher On Policy DistillationOpen-MOPD framework for diagnosing and fixing capability imbalance in multi-teacher on-policy distillation. Use when consolidating domain-specialized RL experts into a single generalist student with balanced cross-domain performance.Votes: 0GitHub stars: 3
- Lionmuon OptimizerLionMuon optimizer methodology - alternating between spectral (Muon) and sign-based (Lion) updates on a fixed period for compute-efficient large-scale trainingVotes: 0GitHub stars: 3
- Guided Evolutionary Neural Architecture Search With Efficient Performance Estimation**arXiv ID:** 2208.06475 **Authors:** Vasco Lopes, Miguel Santos, Bruno Degardin, Luís A. Alexandre **Published:** 2022-07-22T10:58:32Z **Abstract:** Neural Architecture Search (NAS) methods have been successfully applied to image tasks with excellent results. However, NAS methods are often complex and tend to converge to local minima as soon as generated architectures seem to yield good results. This paper proposes GEA, a novel approach for guided NAS. GEA guides the evolution by exploring t...Votes: 0GitHub stars: 3
- Gc Opd Group Calibrated On Policy DistillationGroup-Calibrated On-Policy Distillation (GC-OPD) for long-context reasoning that combines verifier rewards with token-level teacher guidance. Use when training students on long-context evidence-aggregation tasks where global task constraints matter.Votes: 0GitHub stars: 3
- Effective Rank Qnn ExpressivityMethodology for measuring and maximizing Quantum Neural Network (QNN) expressivity using effective rank (kappa). Introduces a quantitative measure capturing the number of effectively independent variational parameters in parameterized quantum circuits. Use when: designing QNN architectures, analyzing barren plateaus, optimizing variational quantum circuits, measuring quantum model capacity, or studying expressivity-entanglement relationships. Triggered by: QNN expressivity, effective rank qua...Votes: 0GitHub stars: 3
- Deep Learning Generalizes Because The Parameterfunction Map Is Biased Towards Simple Functions**arXiv ID:** 1805.08522 **Authors:** Guillermo Valle-Pérez, Chico Q. Camargo, Ard A. Louis **Published:** 2018-05-22T11:51:36Z **Abstract:** Deep neural networks (DNNs) generalize remarkably well without explicit regularization even in the strongly over-parametrized regime where classical learning theory would instead predict that they would severely overfit. While many proposals for some kind of implicit regularization have been made to rationalise this success, there is no consensus for th...Votes: 0GitHub stars: 3
- Arxiv 2608 20061 Let S Scale Step By Step Compute Efficient HyperpaLet's Scale Step by Step: Compute-Efficient Hyperparameter Transfer for Large-Scale Mixture-of-Experts (arXiv: 2608.20061)Votes: 0GitHub stars: 3
- Applying Guidance In A Limited Interval Improves Sample And Distribution Quality In Diffusion Models**arXiv ID:** 2404.07724 **Authors:** Tuomas Kynkäänniemi, Miika Aittala, Tero Karras, Samuli Laine, Timo Aila, Jaakko Lehtinen **Published:** 2024-04-11T13:16:47Z **Abstract:** Guidance is a crucial technique for extracting the best performance out of image-generating diffusion models. Traditionally, a constant guidance weight has been applied throughout the sampling chain of an image. We show that guidance is clearly harmful toward the beginning of the chain (high noise levels), largely unn...Votes: 0GitHub stars: 3
- Analog Interaction Systems AisAnalog Interaction Systems (AIS) methodology for energy-efficient generative modeling on neuromorphic hardware. Bridges gap between software-defined generative models and fixed physics-determined differential equations in analog circuits. Use when designing low-power generative models, implementing analog/neuromorphic computing for ML, optimizing energy-efficient AI hardware, or working with oscillator-based dynamical systems.Votes: 0GitHub stars: 3
- A Review Of Some Techniques For Inclusion Of Domainknowledge Into Deep Neural Networks**arXiv ID:** 2107.10295 **Authors:** Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja, Ashwin Srinivasan **Published:** 2021-07-21T18:18:02Z **Abstract:** We present a survey of ways in which existing scientific knowledge are included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding data using human-machine collaboration. In many such ins...Votes: 0GitHub stars: 3
- Genco Unified Neural Solver Grid AnalysisGENCO unified neural solver for power grid analysis.Votes: 0GitHub stars: 3
- Fsd Rm Small Data RepresentationFSD-RM for small-data representation learning with NAS.Votes: 0GitHub stars: 3
- Flare Plus Plus Low Rank Attention RoutingFLARE++ for dynamic low-rank attention routing.Votes: 0GitHub stars: 3
- Comparative Analysis Of Neural Network Architectures For Shortterm Forex Forecasting**arXiv ID:** 2405.08045 **Authors:** Theodoros Zafeiriou, Dimitris Kalles **Published:** 2024-05-13T14:51:02Z **Abstract:** The present document delineates the analysis, design, implementation, and benchmarking of various neural network architectures within a short-term frequency prediction system for the foreign exchange market (FOREX). Our aim is to simulate the judgment of the human expert (technical analyst) using a system that responds promptly to changes in market conditions, thus enab...Votes: 0GitHub stars: 3
- Federated Quantum MedicalFederated quantum learning methodology for privacy-preserving medical diagnosis. Combines federated learning (FL) with quantum neural networks (QNN) for early disease detection without sharing sensitive patient data across institutions. Use when: building privacy-preserving medical AI, federated quantum learning, cross-institutional medical data collaboration, early disease detection with quantum models, diabetic retinopathy detection, medical image privacy, FQPDR methodology, quantum federat...Votes: 0GitHub stars: 3
- Explicit Operator Neural ComputationMathematical correspondence between state space models (SSMs) and exactly solvable nonlinear oscillator networks. Bridges modern neural architectures with theoretical physics models for understanding end-to-end neural computation.Votes: 0GitHub stars: 3
- Evolutionary Le Chateliers Principle Timescale SeparationEvolutionary Le Chatelier's Principle methodology for understanding phenotypic plasticity and genetic assimilation through timescale separation in the Price equation, providing a physical mechanism for how rapid phenotypic responses precede slower genetic change.Votes: 0GitHub stars: 3
- Ethics Ai Life Sciences Universality DiversityFramework for AI ethics in life sciences based on human brain architecture, global neuronal workspace, and reward cycles of wanting-liking-satiety rather than maximizationVotes: 0GitHub stars: 3