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Transformer是谷歌发表的论文《Attention Is All You Need》提出一种完全基于Attention的翻译架构
Response-free item difficulty modelling for multiple-choice items with fine-tuned transformers: Component-wise representation and multi-task learning
Arxiv
0+阅读 · 9月2日
Performance-Efficiency Tradeoffs in Transformers: An Approximation Theory Perspective
Arxiv
0+阅读 · 9月1日
Feature-Spectral Fragility in Segmentation: Dataset Dependence, Architecture-Specific Localization, and Spectral Correlates
Arxiv
0+阅读 · 8月29日
Can Video World Models Track Unobserved World States?
Arxiv
0+阅读 · 8月31日
The Grammar of Transformers: A Systematic Review of Interpretability Research on Syntactic Knowledge in Language Models
Arxiv
0+阅读 · 8月31日
One Pipeline, Many Transformers: Pattern-Specific Imputation Specialists for Tabular Missing Data
Arxiv
0+阅读 · 8月28日
UniCon-Former: Unified Convolution Transformer is All You Need for Hand Gesture Recognition
Arxiv
0+阅读 · 8月13日
CoDAT: Collaborative Dual-Attention Transformer with Low-Cost Temporal Modeling for Efficient Edge Action Recognition
Arxiv
0+阅读 · 8月7日
StratMamba: Strategic and Reactive Stream Partitioning for Path-Efficient LiDAR-Based Obstacle Avoidance
Arxiv
0+阅读 · 7月14日
Attention is All You Need to Defend Against Indirect Prompt Injection Attacks in LLMs
Arxiv
0+阅读 · 8月12日
Geometric Capacity of Transformers: A Tropical Geometry Perspective
Arxiv
0+阅读 · 7月21日
Awakening Diffusion Transformers: Eliciting Stronger Generation and Understanding via Massive Activation Modulation
Arxiv
0+阅读 · 7月3日
From Convolution to Transformer: A Comparative Study of U-Net Variants for Brain Tumor and Retinal Vessel Segmentation
Arxiv
0+阅读 · 6月20日
SOHET: Sequence Of Heterogeneous Events Transformer with Self-Supervised Pre-Training
Arxiv
0+阅读 · 6月19日
Pixel-Level Residual Diffusion Transformer: Scalable 3D CT Volume Generation
Arxiv
0+阅读 · 6月18日
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