Readable implementation of Mamba 3 SSM model

A production-grade deep learning framework for zero-shot ECG classification that achieves state-of-the-art generalization through morphology-rhythm disentanglement and efficient long-range sequence modeling with Mamba/SSM.

Zero-Shot ECG Generalization using Morphology-Rhythm Disentanglement and Mamba State Space Models. Features a production-ready Clinical Dashboard

Docker containers for AI models on NVIDIA DGX Spark (GB10, SM121, aarch64). TurboQuant KV cache compression + mamba-ssm aarch64 build.

Deploy Mamba-SSM on NVIDIA Jetson with TensorRT support

A physics-informed Deep Learning framework (Mamba/Swin-UNet) for Sentinel-1 SAR imagery denoising and speckle suppression. Features unsupervised refinement and multi-task learning.

MambaQuant - A production-ready stock prediction tool based on Mamba SSM. Features yfinance integration for global markets (US/TW/Crypto), sliding window inference for T+1 prediction, and optimized CUDA pipelines.

Listening Between the Lines: An explainable multimodal framework for MCI detection from spontaneous speech. Leverages Selective State Space Models (Mamba) and Gated Fusion to integrate linguistic disfluencies and eGeMAPS biomarkers across multi-corpus benchmarks (Pitt, ADReSS, TAUKADIAL)

Computational phenomenology study of semantic satiation in neural networks. Comparing how GPT-2, BERT, and Mamba handle extreme repetition reveals causal models drift into hallucination while bidirectional models stay stable—suggesting attention directionality preserves semantic identity.

Comparative analysis of Mamba vs. Transformers trained from scratch. Benchmarking Mamba's linear O(N) scaling and constant-time inference against quadratic attention mechanisms.