1 code implementation • NeurIPS 2023 • Zhongjie Yu, Martin Trapp, Kristian Kersting
In many real-world scenarios, it is crucial to be able to reliably and efficiently reason under uncertainty while capturing complex relationships in data.
no code implementations • 27 Feb 2023 • Zhongjie Yu, Shuyang Wang, Lin Chen, Zhongwei Cheng
Few-shot audio classification is an emerging topic that attracts more and more attention from the research community.
2 code implementations • 13 Feb 2023 • Fabrizio Ventola, Steven Braun, Zhongjie Yu, Martin Mundt, Kristian Kersting
In contrast to neural networks, they are often assumed to be well-calibrated and robust to out-of-distribution (OOD) data.
no code implementations • 14 Sep 2021 • Zhongjie Yu, Devendra Singh Dhami, Kristian Kersting
Probabilistic circuits (PCs) have become the de-facto standard for learning and inference in probabilistic modeling.
1 code implementation • 16 Jun 2021 • Zhongjie Yu, Mingye Zhu, Martin Trapp, Arseny Skryagin, Kristian Kersting
Inspired by recent advances in the field of expert-based approximations of Gaussian processes (GPs), we present an expert-based approach to large-scale multi-output regression using single-output GP experts.
no code implementations • 8 Jun 2021 • Nils Thoma, Zhongjie Yu, Fabrizio Ventola, Kristian Kersting
Time series forecasting is a relevant task that is performed in several real-world scenarios such as product sales analysis and prediction of energy demand.
no code implementations • 26 Mar 2021 • Zhongjie Yu, Gaoang Wang, Lin Chen, Sebastian Raschka, Jiebo Luo
We employ a transfer-learning framework to effectively train the video object detector on a large number of base-class objects and a few video clips of novel-class objects.
no code implementations • 27 May 2020 • Zhongjie Yu, Sebastian Raschka
In this work, we propose the utilization of lower-level, supporting information, namely the feature embeddings of the hidden neural network layers, to improve classifier accuracy.
no code implementations • CVPR 2020 • Zhongjie Yu, Lin Chen, Zhongwei Cheng, Jiebo Luo
Under the proposed framework, we develop a novel method for semi-supervised few-shot learning called TransMatch by instantiating the three components with Imprinting and MixMatch.