Search Results for author:
Found 12 papers, 7 papers with code
On Isotropy Calibration of Transformer Models
Different studies of the embedding space of transformer models suggest that the distribution of contextual representations is highly anisotropic - the embeddings are distributed in a narrow cone.
Efficient and Scalable Graph Generation through Iterative Local Expansion
However, most existing methods struggle with large graphs due to the complexity of representing the entire joint distribution across all node pairs and capturing both global and local graph structures simultaneously.
AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies
We introduce AbDiffuser, an equivariant and physics-informed diffusion model for the joint generation of antibody 3D structures and sequences.
Discovering Graph Generation Algorithms
We provide a novel approach to construct generative models for graphs.
Automating Rigid Origami Design
Rigid origami has shown potential in large diversity of practical applications.
Diffusion Models for Graphs Benefit From Discrete State Spaces
Denoising diffusion probabilistic models and score-matching models have proven to be very powerful for generative tasks.
Agent-based Graph Neural Networks
AgentNet is inspired by sublinear algorithms, featuring a computational complexity that is independent of the graph size.
DT+GNN: A Fully Explainable Graph Neural Network using Decision Trees
We propose the fully explainable Decision Tree Graph Neural Network (DT+GNN) architecture.
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators
We approach the graph generation problem from a spectral perspective by first generating the dominant parts of the graph Laplacian spectrum and then building a graph matching these eigenvalues and eigenvectors.
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks
In DropGNNs, we execute multiple runs of a GNN on the input graph, with some of the nodes randomly and independently dropped in each of these runs.
Ranked #11 on
Graph Classification
on IMDb-B
On Isotropy Calibration of Transformers
Different studies of the embedding space of transformer models suggest that the distribution of contextual representations is highly anisotropic - the embeddings are distributed in a narrow cone.
Scalable Graph Networks for Particle Simulations
However, the dynamics of many real-world systems are challenging to learn due to the presence of nonlinear potentials and a number of interactions that scales quadratically with the number of particles $N$, as in the case of the N-body problem.
