1 code implementation • 7 Nov 2024 • Bai Cong, Nico Daheim, Yuesong Shen, Daniel Cremers, Rio Yokota, Mohammad Emtiyaz Khan, Thomas Möllenhoff
We replace AdamW by the Improved Variational Online Newton (IVON) algorithm to finetune large language models.
1 code implementation • 27 Feb 2024 • Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Clement Bazan, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff
We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks.
no code implementations • 30 Sep 2023 • Christian Koke, Abhishek Saroha, Yuesong Shen, Marvin Eisenberger, Daniel Cremers
To remedy these shortcomings, we introduce ResolvNet, a flexible graph neural network based on the mathematical concept of resolvents.
1 code implementation • 10 Feb 2023 • Christian Tomani, Futa Waseda, Yuesong Shen, Daniel Cremers
While existing post-hoc calibration methods achieve impressive results on in-domain test datasets, they are limited by their inability to yield reliable uncertainty estimates in domain-shift and out-of-domain (OOD) scenarios.
1 code implementation • 27 Oct 2022 • Hans Hao-Hsun Hsu, Yuesong Shen, Daniel Cremers
Current graph neural networks (GNNs) that tackle node classification on graphs tend to only focus on nodewise scores and are solely evaluated by nodewise metrics.
1 code implementation • 12 Oct 2022 • Hans Hao-Hsun Hsu, Yuesong Shen, Christian Tomani, Daniel Cremers
Furthermore, based on the insights from this study, we design a novel calibration method named Graph Attention Temperature Scaling (GATS), which is tailored for calibrating graph neural networks.
1 code implementation • 12 Oct 2022 • Yuesong Shen, Daniel Cremers
In this work, we explore a combinatorial generalization of deep ensemble called deep combinatorial aggregation (DCA).
no code implementations • 27 Jul 2021 • Yu Wang, Yuesong Shen, Daniel Cremers
To learn the direct influence among output nodes in a graph, we propose the Explicit Pairwise Factorized Graph Neural Network (EPFGNN), which models the whole graph as a partially observed Markov Random Field.
1 code implementation • 30 Jun 2020 • Yuesong Shen, Daniel Cremers
It is thus a promising framework that deepens our understanding of neural networks and provides a coherent theoretical formulation for future deep learning research.
1 code implementation • 31 Jan 2019 • Yuesong Shen, Tao Wu, Csaba Domokos, Daniel Cremers
Probabilistic graphical models are traditionally known for their successes in generative modeling.