|
China-OR-OR شركة الأدلة
|
شركة أخبار :
- fla-org flash-linear-attention - GitHub
This repo provides efficient implementations for emerging model architectures, with a focus on efficient sequence modeling (e g , linear attention, state space models, and their hybrids) All implementations are written purely in PyTorch and Triton, making them platform-agnostic Currently verified
- lucidrains linear-attention-transformer - GitHub
Transformer based on a variant of attention that is linear complexity in respect to sequence length - lucidrains linear-attention-transformer
- GitHub - MoonshotAI Kimi-Linear
Kimi Linear is a hybrid linear attention architecture that outperforms traditional full attention methods across various contexts, including long,, short, and reinforcement learning (RL) scaling regimes At it's core is Kimi Delta Attention (KDA)—a refined version of Gated DeltaNet that introduces a more efficient gating mechanism to optimize the use of finite-state RNN memory Kimi Linear
- PolaFormer: Polarity-aware Linear Attention for Vision . . . - GitHub
In this paper, we propose the polarity-aware linear attention mechanism that explicitly models both same-signed and opposite-signed query-key interactions, ensuring comprehensive coverage of relational information
- MHLA: Restoring Expressivity of Linear Attention via Token . . . - GitHub
MHLA: Restoring Expressivity of Linear Attention via Token-Level Multi-Head Kewei Zhang 1*, Ye Huang 1*, Yufan Deng 1, Jincheng Yu 2, Junsong Chen 2, Huan Ling 2, Enze Xie 2, Daquan Zhou 1 1 Peking University 2 NVIDIA ICLR 2026 MHLA is a universal high-efficiency linear attention operator
- Demystify Mamba in Vision: A Linear Attention Perspective
Based on these findings, we propose a Mamba-Like Linear Attention (MLLA) model by incorporating the merits of these two key designs into linear attention The resulting model outperforms various vision Mamba models in both image classification and high-resolution dense prediction tasks, while enjoying parallelizable computation and fast
- A Survey of Efficient Attention Methods - GitHub
Many linear attention methods incorporate forget gates and select gates Based on the presence of these gates, we can classify linear attention methods as follows: Naive Linear Attention (No Gates) 📝 The Table below summarizes naive attention methods 👇 Linear Attention with a Forget Gate 📝 This Table compares methods that use a forget gate 👇 Linear Attention with Forget and
- GitHub - SandAI-org MagiAttention: A Distributed Attention Towards . . .
MagiAttention is a next‑generation distributed attention mechanism—commonly called context‑parallel (CP)—that offers kernel‑level flexibility for diverse attention‑mask patterns while delivering linear scalability across distributed training setups It is especially well suited for workloads involving ultra-long contexts and heterogeneous masks, e g , autoregressive video
- Bridging the divide: Reconsidering softmax and linear attention
Nonetheless, the unsatisfactory performance of linear attention greatly limits its practical application in various scenarios In this paper, we take a step forward to close the gap between the linear and Softmax attention with novel theoretical analyses, which demystify the core factors behind the per formance deviations
|
|