[ICLR2022] Efficient Split-Mix federated learning for in-situ model customization during both training and testing time
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Updated
Apr 12, 2023 - Python
[ICLR2022] Efficient Split-Mix federated learning for in-situ model customization during both training and testing time
Benchmarking machine learning inferencing on embedded hardware.
Source code of “Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing” (CAIAC 2025)
An editable, auditable 807K-param byte-level LLM: CRUD single facts with provable per-edit locality, and abstain when unsure instead of guessing. CPU, offline.
PXG-Tiny Ai - pixel sprites game asset generator. A 483K-param open-weights model that turns plain English prompts into 16×16 pixel-art sprites — ~1 MB NumPy weights, CPU-only, fully offline.
Tiny CNN cat/dog classifier for RISC-V edge AI, with PyTorch training, quantized firmware export, and Renode simulation benchmarks.
A new communication paradigm proposal for restricted bandwidth and fragile channel conditions in NTN.
Ingenuity is an optimized inference engine and benchmarking tool for TinyML models on embedded IoT devices.
Generalized Kuramoto coupling topology for language semantics — distill, converge, decode
Sub-3K parameter MNIST benchmark - 98%+ accuracy with tiny MLP + wavelet scattering transform. Tiny machine learning research.
TRM-inspired tiny recursive reasoning network — 44k params solving Sudoku on CPU with deep supervision, honest baseline comparison (PyTorch + FastAPI)
Tiny open-source model (1.2 MB) that understands percentage questions in English, Portuguese and Spanish and returns the right calculation. Runs in the browser in ~1 ms.
Goal: a language model that fits on a real 3.5" floppy. 1 474 560 bytes is empty FAT12 geometry, not a packed model. Sub-bit core codes vs scalar quantization; quality not claimed.
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