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Bighead's Algorithm Notes
Bighead's Algorithm Notes
Mar 5, 2026 · Artificial Intelligence

AB‑SSM: Adaptive Bidirectional State‑Space Model for High‑Frequency Portfolio Management

The paper introduces AB‑SSM, an adaptive bidirectional state‑space model that incorporates a time‑varying linear structure and a bidirectional layer to capture market non‑stationarity and asset correlations, and demonstrates through extensive US, China, and crypto experiments that it outperforms traditional, deep‑learning, and DRL baselines in profit‑risk trade‑offs, efficiency, and scalability.

Deep Reinforcement Learningadaptive linear time-varyingbidirectional SSM
0 likes · 12 min read
AB‑SSM: Adaptive Bidirectional State‑Space Model for High‑Frequency Portfolio Management
AI Frontier Lectures
AI Frontier Lectures
Jul 24, 2025 · Artificial Intelligence

State Space Models vs Transformers: Uncovering the Real Trade‑offs in Sequence Modeling

This article analyzes the fundamental differences between state space models (SSM) and Transformer architectures, highlighting their three core components, training efficiency, memory handling, tokenization impact, and empirical performance trade‑offs, and argues why SSMs can outperform Transformers on many sequence tasks.

AI ArchitectureSequence ModelingTransformers
0 likes · 19 min read
State Space Models vs Transformers: Uncovering the Real Trade‑offs in Sequence Modeling
NewBeeNLP
NewBeeNLP
Mar 4, 2024 · Artificial Intelligence

A Curated Tour of Mamba Papers: 25 Cutting‑Edge State‑Space Model Innovations

This article presents a GitHub‑hosted collection of 25 recent research papers on Mamba and its variants, summarizing each work’s core contributions across sequence modeling, vision, medical imaging, graph analysis, and multimodal tasks, and highlighting their performance gains over prior methods.

MambaSequence Modelingcomputer vision
0 likes · 13 min read
A Curated Tour of Mamba Papers: 25 Cutting‑Edge State‑Space Model Innovations