Progress in nanorobotics for advancing biomedicine
Nanorobotics, which has long been a fantasy in the realm of science fiction, is now a reality
due to the considerable developments in diverse fields including chemistry, materials …
due to the considerable developments in diverse fields including chemistry, materials …
Prior knowledge elicitation: The past, present, and future
Prior Knowledge Elicitation: The Past, Present, and Future Page 1 Bayesian Analysis (2024)
19, Number 4, pp. 1129–1161 Prior Knowledge Elicitation: The Past, Present, and Future ∗ …
19, Number 4, pp. 1129–1161 Prior Knowledge Elicitation: The Past, Present, and Future ∗ …
Human-in-the-loop assisted de novo molecular design
A de novo molecular design workflow can be used together with technologies such as
reinforcement learning to navigate the chemical space. A bottleneck in the workflow that …
reinforcement learning to navigate the chemical space. A bottleneck in the workflow that …
A survey of domain knowledge elicitation in applied machine learning
Eliciting knowledge from domain experts can play an important role throughout the machine
learning process, from correctly specifying the task to evaluating model results. However …
learning process, from correctly specifying the task to evaluating model results. However …
Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions
A Kirsch - arXiv preprint arXiv:2401.04305, 2024 - arxiv.org
At its core, this thesis aims to enhance the practicality of deep learning by improving the
label and training efficiency of deep learning models. To this end, we investigate data subset …
label and training efficiency of deep learning models. To this end, we investigate data subset …
Approximate Bayesian computation with domain expert in the loop
Approximate Bayesian computation (ABC) is a popular likelihood-free inference method for
models with intractable likelihood functions. As ABC methods usually rely on comparing …
models with intractable likelihood functions. As ABC methods usually rely on comparing …
AI for Science: an emerging agenda
This report documents the programme and the outcomes of Dagstuhl Seminar 22382"
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling". Today's …
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling". Today's …
A decision-theoretic approach for model interpretability in Bayesian framework
A salient approach to interpretable machine learning is to restrict modeling to simple
models. In the Bayesian framework, this can be pursued by restricting the model structure …
models. In the Bayesian framework, this can be pursued by restricting the model structure …
Decision rule elicitation for domain adaptation
A Nikitin, S Kaski - Proceedings of the 26th International Conference on …, 2021 - dl.acm.org
Human-in-the-loop machine learning is widely used in artificial intelligence (AI) to elicit
labels for data points from experts or to provide feedback on how close the predicted results …
labels for data points from experts or to provide feedback on how close the predicted results …
Knowledge extraction via decentralized knowledge graph aggregation
R Nordsieck, M Heider, A Winschel… - 2021 IEEE 15th …, 2021 - ieeexplore.ieee.org
In many industrial manufacturing processes, human operators play a central role when it
comes to parameterizing the involved machinery and dealing with errors in the process …
comes to parameterizing the involved machinery and dealing with errors in the process …