10 citations · 24 across the 12 of their papers we have counts for
12 papers
Graph Neural Networks Are Not Continuous Across Graph Resolutions
Christian Koke, Yuesong Shen, Abhishek Saroha +4
We show that contrary to conventional wisdom in the community, graph neural networks (GNNs) are not continuous with respect to all natural modes of graph convergence. As a result,…
Improving LoRA with Variational Learning
Bai Cong, Nico Daheim, Yuesong Shen +3
Bayesian methods have recently been used to improve LoRA finetuning and, although they improve calibration, their effect on other metrics (such as accuracy) is marginal and can som…
Variational Low-Rank Adaptation Using IVON
Bai Cong, Nico Daheim, Yuesong Shen +4
We show that variational learning can significantly improve the accuracy and calibration of Low-Rank Adaptation (LoRA) without a substantial increase in the cost. We replace AdamW…
Variational Learning is Effective for Large Deep Networks
Yuesong Shen, Nico Daheim, Bai Cong +8
We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks. We show that an optimizer called Improved Variati…
ResolvNet: A Graph Convolutional Network with multi-scale Consistency
Christian Koke, Abhishek Saroha, Yuesong Shen +2
It is by now a well known fact in the graph learning community that the presence of bottlenecks severely limits the ability of graph neural networks to propagate information over l…
Beyond In-Domain Scenarios: Robust Density-Aware Calibration
Christian Tomani, Futa Waseda, Yuesong Shen +1
Calibrating deep learning models to yield uncertainty-aware predictions is crucial as deep neural networks get increasingly deployed in safety-critical applications. While existing…
A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs
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. This limits uncer…
Deep Combinatorial Aggregation
Yuesong Shen, Daniel Cremers
Neural networks are known to produce poor uncertainty estimations, and a variety of approaches have been proposed to remedy this issue. This includes deep ensemble, a simple and ef…
What Makes Graph Neural Networks Miscalibrated?
Hans Hao-Hsun Hsu, Yuesong Shen, Christian Tomani +1
Given the importance of getting calibrated predictions and reliable uncertainty estimations, various post-hoc calibration methods have been developed for neural networks on standar…
Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised Node Classification
Yu Wang, Yuesong Shen, Daniel Cremers
Node features and structural information of a graph are both crucial for semi-supervised node classification problems. A variety of graph neural network (GNN) based approaches have…
A Chain Graph Interpretation of Real-World Neural Networks
Yuesong Shen, Daniel Cremers
The last decade has witnessed a boom of deep learning research and applications achieving state-of-the-art results in various domains. However, most advances have been established…
Probabilistic Discriminative Learning with Layered Graphical Models
Yuesong Shen, Tao Wu, Csaba Domokos +1
Probabilistic graphical models are traditionally known for their successes in generative modeling. In this work, we advocate layered graphical models (LGMs) for probabilistic discr…