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Found 15 papers, 13 papers with code
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
In these graphs, the geometric attributes transform according to the inherent physical symmetries of 3D atomic systems, including rotations and translations in Euclidean space, as well as node permutations.
Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?
Deep generative models for structure-based drug design (SBDD), where molecule generation is conditioned on a 3D protein pocket, have received considerable interest in recent years.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Group Invariant Global Pooling
Much work has been devoted to devising architectures that build group-equivariant representations, while invariance is often induced using simple global pooling mechanisms.
gRNAde: Geometric Deep Learning for 3D RNA inverse design
Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity.
On the Expressive Power of Geometric Graph Neural Networks
The expressive power of Graph Neural Networks (GNNs) has been studied extensively through the Weisfeiler-Leman (WL) graph isomorphism test.
On Representation Knowledge Distillation for Graph Neural Networks
Past work on distillation for GNNs proposed the Local Structure Preserving loss (LSP), which matches local structural relationships defined over edges across the student and teacher's node embeddings.
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis
In this work we propose PointDisc, a point discriminative learning method to leverage self-supervisions for data-efficient 3D point cloud classification and segmentation.
Learning the Travelling Salesperson Problem Requires Rethinking Generalization
End-to-end training of neural network solvers for graph combinatorial optimization problems such as the Travelling Salesperson Problem (TSP) have seen a surge of interest recently, but remain intractable and inefficient beyond graphs with few hundreds of nodes.
Benchmarking Graph Neural Networks
In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.
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Multi-Graph Transformer for Free-Hand Sketch Recognition
In this work, we propose a new representation of sketches as multiple sparsely connected graphs.
On Learning Paradigms for the Travelling Salesman Problem
We explore the impact of learning paradigms on training deep neural networks for the Travelling Salesman Problem.
An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem
This paper introduces a new learning-based approach for approximately solving the Travelling Salesman Problem on 2D Euclidean graphs.
Working women and caste in India: A study of social disadvantage using feature attribution
Women belonging to the socially disadvantaged caste-groups in India have historically been engaged in labour-intensive, blue-collar work.
Personalization in Goal-Oriented Dialog
The main goal of modeling human conversation is to create agents which can interact with people in both open-ended and goal-oriented scenarios.
