Technicolor@MediaEval 2016 Predicting Media Interestingness Task. - Yuesong Shen; Claire-Hélène Demarty; Ngoc Q. K. Duong (MediaEval, 2016)

Deep learning for multimodal-based video interestingness prediction. - Yuesong Shen; Claire-Hélène Demarty; Ngoc Q. K. Duong (ICME, 2017)

Probabilistic Discriminative Learning with Layered Graphical Models. - Yuesong Shen; Tao Wu 006; Csaba Domokos; Daniel Cremers (CoRR, 2019)

Deriving Neural Network Design and Learning from the Probabilistic Framework of Chain Graphs. - Yuesong Shen; Daniel Cremers (CoRR, 2020)

Explicit pairwise factorized graph neural network for semi-supervised node classification. - Yu Wang 158; Yuesong Shen; Daniel Cremers (UAI, 2021)

What Makes Graph Neural Networks Miscalibrated? - Hans Hao-Hsun Hsu; Yuesong Shen; Christian Tomani; Daniel Cremers (NeurIPS, 2022)

Deep Combinatorial Aggregation. - Yuesong Shen; Daniel Cremers (NeurIPS, 2022)

A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs. - Hans Hao-Hsun Hsu; Yuesong Shen; Daniel Cremers (CoRR, 2022)

Beyond In-Domain Scenarios: Robust Density-Aware Calibration. - Christian Tomani; Futa Kai Waseda; Yuesong Shen; Daniel Cremers (ICML, 2023)

ResolvNet: A Graph Convolutional Network with multi-scale Consistency. - Christian Koke; Abhishek Saroha; Yuesong Shen; Marvin Eisenberger; Daniel Cremers (CoRR, 2023)

Variational Learning is Effective for Large Deep Networks. - Yuesong Shen; Nico Daheim; Bai Cong; Peter Nickl; Gian Maria Marconi; Clement Bazan; Rio Yokota; Iryna Gurevych; Daniel Cremers; Mohammad Emtiyaz Khan; Thomas Möllenhoff (ICML, 2024)

Variational Low-Rank Adaptation Using IVON. - Bai Cong; Nico Daheim; Yuesong Shen; Daniel Cremers; Rio Yokota; Mohammad Emtiyaz Khan; Thomas Möllenhoff (CoRR, 2024)

Improving LoRA with Variational Learning. - Bai Cong; Nico Daheim; Yuesong Shen; Rio Yokota; Mohammad Emtiyaz Khan; Thomas Möllenhoff (CoRR, 2025)