Differences
This shows you the differences between two versions of the page.
Next revision | Previous revision Next revision Both sides next revision | ||
members:tomani [2019/07/18 15:23] Quirin Lohr created |
members:tomani [2024/03/18 19:58] Christian Tomani |
||
---|---|---|---|
Line 1: | Line 1: | ||
< | < | ||
+ | |||
+ | ===== Brief Bio ===== | ||
+ | Find me on [[https:// | ||
+ | |||
+ | I am a PhD student at the Technical University of Munich at the Chair for Computer Vision and Artificial Intelligence headed by [[: | ||
+ | |||
+ | My interests cover a large spectrum of Machine Learning and Deep Learning topics. Projects of mine include uncertainty aware and robust models for in domain, domain shift and out of domain (OOD) scenarios; Natural Language Processing (NLP) and Large Language Models (LLMs); investigating reasoning capabilities and developing reliable LLMs; Computer Vision (CV); Time Series Data Analysis with supervised and self-supervised learning algorithms; Recurrent Neural Networks (RNNs) and Transformer architectures; | ||
+ | |||
+ | I am looking for motivated as well as talented students. If you are interested please contact me directly via email and highlight your relevant academic experience and programming skills. Additionally, | ||
+ | |||
+ | ==== Publications ==== | ||
+ | * **Beyond In-Domain Scenarios: Robust Density-Aware Calibration**, | ||
+ | < | ||
+ | </ | ||
+ | < | ||
+ | |||
+ | |||
+ | * **Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration**, | ||
+ | < | ||
+ | </ | ||
+ | < | ||
+ | |||
+ | |||
+ | * **Post-hoc Uncertainty Calibration for Domain Drift Scenarios**, | ||
+ | < | ||
+ | </ | ||
+ | < | ||
+ | |||
+ | |||
+ | < | ||
+ | < | ||
+ | < | ||
+ | </ | ||
+ |