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Neueste Paper
- StelLA: Subspace Learning in Low-rank Adaptation using Stiefel Manifold
Zhizhong Li, Sina Sajadmanesh, Jingtao Li, Lingjuan Lyu · 3. April 2026
Low-rank adaptation (LoRA) has been widely adopted as a parameter-efficient technique for fine-tuning large-scale pre-trained models. However, it still lags behind full fine-tuning in performance, partly due to its insufficient exploitation of the geometric structure underlying low-rank manifolds. I…
- Improving a Hybrid Graphsage Deep Network for Automatic Multi-objective Logistics Management in Supply Chain
Mehdi Khaleghi, Nastaran Khaleghi, Sobhan Sheykhivand, Sebelan Danishvar · 18. November 2025
Systematic logistics, conveyance amenities and facilities as well as warehousing information play a key role in fostering profitable development in a supply chain. The aim of transformation in industries is the improvement of the resiliency regarding the supply chain. The resiliency policies are req…
- Study on Supply Chain Finance Decision-Making Model and Enterprise Economic Performance Prediction Based on Deep Reinforcement Learning
Shiman Zhang, Jinghan Zhou, Zhoufan Yu, Ningai Leng · 4. November 2025
To improve decision-making and planning efficiency in back-end centralized redundant supply chains, this paper proposes a decision model integrating deep learning with intelligent particle swarm optimization. A distributed node deployment model and optimal planning path are constructed for the suppl…
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