Zobrazeno 1 - 10
of 145
pro vyhledávání: '"Kraus, Mathias"'
Generalized Additive Models (GAMs) offer a balance between performance and interpretability in machine learning. The interpretability aspect of GAMs is expressed through shape plots, representing the model's decision-making process. However, the visu
Externí odkaz:
http://arxiv.org/abs/2409.16870
Autor:
Kruschel, Sven, Hambauer, Nico, Weinzierl, Sven, Zilker, Sandra, Kraus, Mathias, Zschech, Patrick
Machine learning is permeating every conceivable domain to promote data-driven decision support. The focus is often on advanced black-box models due to their assumed performance advantages, whereas interpretable models are often associated with infer
Externí odkaz:
http://arxiv.org/abs/2409.14429
Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient's complete health history to make informed decisions about f
Externí odkaz:
http://arxiv.org/abs/2405.13187
Feature selection is a critical component in predictive analytics that significantly affects the prediction accuracy and interpretability of models. Intrinsic methods for feature selection are built directly into model learning, providing a fast and
Externí odkaz:
http://arxiv.org/abs/2403.11363
Advances towards more faithful and traceable answers of Large Language Models (LLMs) are crucial for various research and practical endeavors. One avenue in reaching this goal is basing the answers on reliable sources. However, this Evidence-Based QA
Externí odkaz:
http://arxiv.org/abs/2402.08277
Public and private actors struggle to assess the vast amounts of information about sustainability commitments made by various institutions. To address this problem, we create a novel tool for automatically detecting corporate, national, and regional
Externí odkaz:
http://arxiv.org/abs/2310.08096
Problem Definition. Increasing costs of healthcare highlight the importance of effective disease prevention. However, decision models for allocating preventive care are lacking. Methodology/Results. In this paper, we develop a data-driven decision mo
Externí odkaz:
http://arxiv.org/abs/2308.06959
Autor:
Ni, Jingwei, Bingler, Julia, Colesanti-Senni, Chiara, Kraus, Mathias, Gostlow, Glen, Schimanski, Tobias, Stammbach, Dominik, Vaghefi, Saeid Ashraf, Wang, Qian, Webersinke, Nicolas, Wekhof, Tobias, Yu, Tingyu, Leippold, Markus
In the face of climate change, are companies really taking substantial steps toward more sustainable operations? A comprehensive answer lies in the dense, information-rich landscape of corporate sustainability reports. However, the sheer volume and c
Externí odkaz:
http://arxiv.org/abs/2307.15770
Autor:
Ni, Jingwei, Bingler, Julia, Colesanti-Senni, Chiara, Kraus, Mathias, Gostlow, Glen, Schimanski, Tobias, Stammbach, Dominik, Vaghefi, Saeid Ashraf, Wang, Qian, Webersinke, Nicolas, Wekhof, Tobias, Yu, Tingyu, Leippold, Markus
This paper introduces a novel approach to enhance Large Language Models (LLMs) with expert knowledge to automate the analysis of corporate sustainability reports by benchmarking them against the Task Force for Climate-Related Financial Disclosures (T
Externí odkaz:
http://arxiv.org/abs/2306.15518
Autor:
Vaghefi, Saeid Ashraf, Wang, Qian, Muccione, Veruska, Ni, Jingwei, Kraus, Mathias, Bingler, Julia, Schimanski, Tobias, Colesanti-Senni, Chiara, Webersinke, Nicolas, Huggel, Christrian, Leippold, Markus
Large Language Models (LLMs) have made significant progress in recent years, achieving remarkable results in question-answering tasks (QA). However, they still face two major challenges: hallucination and outdated information after the training phase
Externí odkaz:
http://arxiv.org/abs/2304.05510