Zobrazeno 1 - 10
of 248
pro vyhledávání: '"Brintrup, Alexandra"'
This paper explores how Large Language Models (LLMs) can automate consensus-seeking in supply chain management (SCM), where frequent decisions on problems such as inventory levels and delivery times require coordination among companies. Traditional S
Externí odkaz:
http://arxiv.org/abs/2411.10184
Autor:
Chundawat, Vikram S, Niroula, Pushkar, Dhungana, Prasanna, Schoepf, Stefan, Mandal, Murari, Brintrup, Alexandra
Federated learning (FL) has enabled collaborative model training across decentralized data sources or clients. While adding new participants to a shared model does not pose great technical hurdles, the removal of a participant and their related infor
Externí odkaz:
http://arxiv.org/abs/2410.04144
Traditional machine learning-based visual inspection systems require extensive data collection and repetitive model training to improve accuracy. These systems typically require expensive camera, computing equipment and significant machine learning e
Externí odkaz:
http://arxiv.org/abs/2409.15980
The penultimate goal for developing machine learning models in supply chain management is to make optimal interventions. However, most machine learning models identify correlations in data rather than inferring causation, making it difficult to syste
Externí odkaz:
http://arxiv.org/abs/2408.13556
In today's globalized economy, comprehensive supply chain visibility is crucial for effective risk management. Achieving visibility remains a significant challenge due to limited information sharing among supply chain partners. This paper presents a
Externí odkaz:
http://arxiv.org/abs/2408.07705
Adversarial attacks by malicious actors on machine learning systems, such as introducing poison triggers into training datasets, pose significant risks. The challenge in resolving such an attack arises in practice when only a subset of the poisoned d
Externí odkaz:
http://arxiv.org/abs/2406.09173
We present a machine unlearning approach that is both retraining- and label-free. Most existing machine unlearning approaches require a model to be fine-tuned to remove information while preserving performance. This is computationally expensive and n
Externí odkaz:
http://arxiv.org/abs/2402.19308
Data entry constitutes a fundamental component of the machine learning pipeline, yet it frequently results in the introduction of labelling errors. When a model has been trained on a dataset containing such errors its performance is reduced. This lea
Externí odkaz:
http://arxiv.org/abs/2402.10098
To comply with AI and data regulations, the need to forget private or copyrighted information from trained machine learning models is increasingly important. The key challenge in unlearning is forgetting the necessary data in a timely manner, while p
Externí odkaz:
http://arxiv.org/abs/2402.01401
Publikováno v:
Volume 157, December 2023, 104376
Collaborative vehicle routing occurs when carriers collaborate through sharing their transportation requests and performing transportation requests on behalf of each other. This achieves economies of scale, thus reducing cost, greenhouse gas emission
Externí odkaz:
http://arxiv.org/abs/2310.17485