604 citations · 1.4k across the 64 of their papers we have counts for
21 papers · 1 filter
PRISM: Probabilistic Real-Time Inference in Spatial World Models
Atanas Mirchev, Baris Kayalibay, Ahmed Agha +3
We introduce PRISM, a method for real-time filtering in a probabilistic generative model of agent motion and visual perception. Previous approaches either lack uncertainty estimate…
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…
CHALLENGER: Training with Attribution Maps
Christian Tomani, Daniel Cremers
We show that utilizing attribution maps for training neural networks can improve regularization of models and thus increase performance. Regularization is key in deep learning, esp…
Dive into Layers: Neural Network Capacity Bounding using Algebraic Geometry
Ji Yang, Lu Sang, Daniel Cremers
The empirical results suggest that the learnability of a neural network is directly related to its size. To mathematically prove this, we borrow a tool in topological algebra: Bett…