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cs.LG2026
Improving Parameter Utilization by Sharing Neural Experts Across Layers in Transformers
Dian Jiao, Jiaxin Duan, Shuai Zhao +3
Transformer-based large language models often suffer from inter-layer parameter redundancy, where functional transformations are redundantly learned across network depths. We propo…
cs.LG2026
TF-CoDiT: Conditional Time Series Synthesis with Diffusion Transformers for Treasury Futures
Yingxiao Zhang, Jiaxin Duan, Junfu Zhang +1
Diffusion Transformers (DiT) have achieved milestones in synthesizing financial time-series data, such as stock prices and order flows. However, their performance in synthesizing t…