Search Results for author:
Found 22 papers, 11 papers with code
IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight
In this paper, we propose IGANN Sparse, a novel machine learning model from the family of generalized additive models, which promotes sparsity through a non-linear feature selection process during training.
Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering
One avenue in reaching this goal is basing the answers on reliable sources.
A Globally Convergent Algorithm for Neural Network Parameter Optimization Based on Difference-of-Convex Functions
Furthermore, we mathematically analyze the convergence rate of parameters and the convergence rate in value (i. e., the training loss).
ClimateBERT-NetZero: Detecting and Assessing Net Zero and Reduction Targets
Public and private actors struggle to assess the vast amounts of information about sustainability commitments made by various institutions.
Data-Driven Allocation of Preventive Care With Application to Diabetes Mellitus Type II
In this paper, we develop a data-driven decision model for determining a cost-effective allocation of preventive treatments to patients at risk.
CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools
In the face of climate change, are companies really taking substantial steps toward more sustainable operations?
Paradigm Shift in Sustainability Disclosure Analysis: Empowering Stakeholders with CHATREPORT, a Language Model-Based Tool
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 (TCFD) recommendations.
chatClimate: Grounding Conversational AI in Climate Science
The answers and their sources were evaluated by our team of IPCC authors, who used their expert knowledge to score the accuracy of the answers from 1 (very-low) to 5 (very-high).
Enhancing Large Language Models with Climate Resources
Large language models (LLMs) have significantly transformed the landscape of artificial intelligence by demonstrating their ability in generating human-like text across diverse topics.
Environmental Claim Detection
To transition to a green economy, environmental claims made by companies must be reliable, comparable, and verifiable.
Towards Climate Awareness in NLP Research
We argue that this deficiency is one of the reasons why very few publications in NLP report key figures that would allow a more thorough examination of environmental impact.
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints
The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of machine learning (ML) models.
A Light in the Dark: Deep Learning Practices for Industrial Computer Vision
In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV).
ClimateBert: A Pretrained Language Model for Climate-Related Text
Over the recent years, large pretrained language models (LM) have revolutionized the field of natural language processing (NLP).
AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units
In this work, we propose a novel generative deep probabilistic model for real-time risk scoring in ICUs.
Improving Heart Rate Variability Measurements from Consumer Smartwatches with Machine Learning
The reactions of the human body to physical exercise, psychophysiological stress and heart diseases are reflected in heart rate variability (HRV).
Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences
This demonstrates the performance and superior interpretability of our method, while we finally discuss implications for decision support.
Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching
In contrast, our research overcomes the limitations of pre-set rules by contributing a novel predictor of market baskets from sequential purchase histories: our predictions are based on similarity matching in order to identify similar purchase habits among the complete shopping histories of all customers.
Deep learning in business analytics and operations research: Models, applications and managerial implications
(4) We provide guidelines and implications for researchers, managers and practitioners in operations research who want to advance their capabilities for business analytics with regard to deep learning.
Deep learning for affective computing: text-based emotion recognition in decision support
Emotions widely affect human decision-making.
Decision support from financial disclosures with deep neural networks and transfer learning
Hence, this paper studies the use of deep neural networks for financial decision support.
Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees
To learn from the resulting rhetorical structure, we propose a tensor-based, tree-structured deep neural network (named Discourse-LSTM) in order to process the complete discourse tree.
