5,439,308 AI resources indexedWorking toward the world’s largest AI resource index.
Publications about AI methods and applications.
Research paper by Zhengbo Zhou and collaborators. arXiv record dated 2025-10-21. Research area: Computer vision.
Research paper by Shubhi Bansal and collaborators. Published 2024-10-03. Topics: State Space Model, Mamba, transformers, quadratic computational complexity, long-range dependencies.
Research paper by Moein Heidari and collaborators. Published 2025-02-01. Topics: U-Net, ViT, Mamba State Space Model, Extended Long-Short Term Memory, xLSTM.
Research paper by Mugunthan Shandirasegaran and collaborators. arXiv record dated 2026-02-13. Research area: Machine learning.
Research paper by Alice Zhang and collaborators. arXiv record dated 2025-07-29. Research area: Machine learning.
Research paper by Shriyank Somvanshi and collaborators. arXiv record dated 2026-08-10. Research area: Machine learning.
Research paper by Daerobby and collaborators, published 2026 in Jurnal Manajemen dan Teknik (JuMat). Research area: Sentiment Analysis and Opinion Mining.
Research paper by Mehmet Hamza Erol and collaborators. Published 2024-06-05. Topics: Audio Spectrogram Transformers (ASTs), self-attention, quadratic scaling, state state models (SSMs), Audio Mamba (AuM).
Research paper by Fa-Ting Hong and collaborators. Published 2025-04-03. Topics: video diffusion framework, parallel mamba structure, multi-signals control, single-signal control, gate mechanism.
It analyzes the limitations of traditional Transformer models (such as quadratic computational complexity and high energy consumption) and explores promising alternative architectures like State Space Models (SSMs) and Mamba. The paper also investigates techniques for energy-efficient AI (including quantization, pruning, knowledge distillation, and neuromorphic computing) alongside methods for multimodal fusion—integrating text, image, audio, and sensor data into unified AI systems. Overall, it argues that the future of AI lies in developing scalable, sustainable, and efficient models that move beyond a single dominant architecture
Research paper by Shengkun Tang and collaborators. arXiv record dated 2025-10-23. Research area: Language technology.
Research paper by Quentin Anthony and collaborators. Published 2024-02-01. Topics: state-space models, SSM, Mamba, mixture-of-experts, MoE.
Try a broader search or clear some filters.
Platform labels describe intended use. Read each resource's author credits, requirements and available access methods.