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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
MG-Former: A Transformer-Based Framework for Music-Driven 3D Conducting Gesture Generation
Arxiv
0+阅读 · 5月2日
Investigation into In-Context Learning Capabilities of Transformers
Arxiv
0+阅读 · 4月28日
On the Ability of Transformers to Verify Plans
Arxiv
0+阅读 · 3月20日
TransGP: Task-Conditioned Transformer-Guided Genetic Programming for Multitask Dynamic Flexible Job Shop Scheduling
Arxiv
0+阅读 · 4月4日
Screening Is Enough
Arxiv
0+阅读 · 4月6日
Stability and Generalization in Looped Transformers
Arxiv
0+阅读 · 4月16日
Scalable and Adaptive Parallel Training of Graph Transformer on Large Graphs
Arxiv
0+阅读 · 4月17日
Demystifying amortized causal discovery with transformers
Arxiv
0+阅读 · 3月18日
NaviFormer: A Deep Reinforcement Learning Transformer-like Model to Holistically Solve the Navigation Problem
Arxiv
0+阅读 · 4月18日
Trust, but Verify: Peeling Low-Bit Transformer Networks for Training Monitoring
Trust, but Verify: Peeling Low-Bit Transformer Networks for Training Monitoring
Arxiv
0+阅读 · 5月4日
DDCL-INCRT: A Self-Organising Transformer with Hierarchical Prototype Structure (Theoretical Foundations)
Arxiv
0+阅读 · 4月2日
Working Memory Constraints Scaffold Learning in Transformers under Data Scarcity
Arxiv
0+阅读 · 4月22日
Jailbreaking Generative AI: Multivector Phishing Threats and Transformer based Defenses
Arxiv
0+阅读 · 4月1日
FastCache: Fast Caching for Diffusion Transformer Through Learnable Linear Approximation
Arxiv
0+阅读 · 3月27日
Adaptive Swin Transformer Partitioning over AI-RAN Networks
Arxiv
0+阅读 · 4月26日
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