Meshtastic: Why is it so popular?
jonlennartaasenden.wordpress.comΒ·2h
πŸ•ΈοΈMesh Networking
A Rubyist's guide to big-O notation
honeybadger.ioΒ·2d
🌳Trie Structures
rxi/microui
github.comΒ·4h
πŸ“ΊTerminal UI
Understanding Prolly Tree Balance
dolthub.comΒ·1dΒ·
Discuss: Hacker News
🌲B-tree Variants
Balancing Privacy and Utility in Correlated Data: A Study of Bayesian Differential Privacy
arxiv.orgΒ·2h
πŸ›‘οΈDifferential Privacy
ColumnTransformer and Pipelines in Scikit-Learn: Clean, Scalable, and Powerful Preprocessing
dev.toΒ·10hΒ·
Discuss: DEV
🌊Streaming Compression
Machine Learning Fundamentals: active learning project
dev.toΒ·1dΒ·
Discuss: DEV
🧠Machine Learning
FREQuency ATTribution: Benchmarking Frequency-based Occlusion for Time Series Data
arxiv.orgΒ·3d
πŸ“ŠQuantization
🧠 Solving LeetCode Until I Become Top 1% β€” Day `30`
dev.toΒ·1dΒ·
Discuss: DEV
πŸ“Error Bounds
Advanced Path of Network Programming Deep Exploration from TCP to Application Layer Protocols（1750952117902400οΌ‰
dev.toΒ·14hΒ·
Discuss: DEV
🌐Network Protocols
TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understanding
arxiv.orgΒ·2h
πŸŒ€Hyperbolic Geometry
Learning to Be a Transformer to Pinpoint Anomalies
arxiv.orgΒ·2h
πŸ€–Grammar Induction
FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment
arxiv.orgΒ·1d
πŸ“ŠRate-Distortion Theory
15 Go Packages Worth Your Time
dev.toΒ·18hΒ·
Discuss: DEV
πŸ’§Liquid Types
Opportunistic Osteoporosis Diagnosis via Texture-Preserving Self-Supervision, Mixture of Experts and Multi-Task Integration
arxiv.orgΒ·1d
πŸ‘οΈOCR Enhancement
Angio-Diff: Learning a Self-Supervised Adversarial Diffusion Model for Angiographic Geometry Generation
arxiv.orgΒ·2d
πŸŒ€Riemannian Computing
Toward Decision-Oriented Prognostics: An Integrated Estimate-Optimize Framework for Predictive Maintenance
arxiv.orgΒ·2d
🌑️Preservation Entropy
Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification
arxiv.orgΒ·3d
πŸ“ŠLearned Metrics
From expected to actual: Kotlin doesn't reinvent, it reuses πŸ”„
dev.toΒ·6hΒ·
Discuss: DEV
πŸ’§Liquid Types
Causal discovery in deterministic discrete LTI-DAE systems
arxiv.orgΒ·1d
🧠Machine Learning