Tag: transformer models
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Cascade Token Selection Boosts Transformer Attention Speed
Discover how cascade token selection accelerates transformer attention by reducing computation costs up to 63% with Activation Decorrelation Attention.
Gated Subspace Inference: Boost Transformer Speed 10x
Accelerate transformer inference up to 10.5x with gated subspace inference—no retraining or architecture changes needed, maintaining high output quality.
Fast, High-Quality Plan Generation with Self-Improvement AI
Discover how self-improving AI models generate fast, high-quality plans with up to 30% shorter lengths and scalable performance across multiple domains.
1BT: Efficient EEG Transformer for Cognitive Workload
Discover 1BT, a compact EEG-based transformer model for accurate, real-time cognitive workload assessment with low computational cost.
ASTERIS: Advanced Denoising Boosts Astronomical Detection
ASTERIS, a self-supervised spatiotemporal denoising algorithm, improves astronomical imaging detection limits by 1 magnitude with high accuracy.