7 citations · 9 across the 4 of their papers we have counts for
4 papers
QUEST: A robust attention formulation using query-modulated spherical attention
Hariprasath Govindarajan, Per Sidén, Jacob Roll +1
The Transformer model architecture has become one of the most widely used in deep learning and the attention mechanism is at its core. The standard attention formulation uses a sof…
On Partial Prototype Collapse in the DINO Family of Self-Supervised Methods
Hariprasath Govindarajan, Per Sidén, Jacob Roll +1
A prominent self-supervised learning paradigm is to model the representations as clusters, or more generally as a mixture model. Learning to map the data samples to compact represe…
Temporal Graph Neural Networks for Irregular Data
Joel Oskarsson, Per Sidén, Fredrik Lindsten
This paper proposes a temporal graph neural network model for forecasting of graph-structured irregularly observed time series. Our TGNN4I model is designed to handle both irregula…
Scalable Deep Gaussian Markov Random Fields for General Graphs
Joel Oskarsson, Per Sidén, Fredrik Lindsten
Machine learning methods on graphs have proven useful in many applications due to their ability to handle generally structured data. The framework of Gaussian Markov Random Fields…