69 citations · 201 across the 15 of their papers we have counts for
15 papers
Geometric Epitope and Paratope Prediction
Marco Pegoraro, Clémentine Dominé, Emanuele Rodolà +2
Antibody-antigen interactions play a crucial role in identifying and neutralizing harmful foreign molecules. In this paper, we investigate the optimal representation for predicting…
Evolving Computation Graphs
Andreea Deac, Jian Tang
Graph neural networks (GNNs) have demonstrated success in modeling relational data, especially for data that exhibits homophily: when a connection between nodes tends to imply that…
How does over-squashing affect the power of GNNs?
Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein +4
Graph Neural Networks (GNNs) are the state-of-the-art model for machine learning on graph-structured data. The most popular class of GNNs operate by exchanging information between…
Equivariant MuZero
Andreea Deac, Théophane Weber, George Papamakarios
Deep reinforcement learning repeatedly succeeds in closed, well-defined domains such as games (Chess, Go, StarCraft). The next frontier is real-world scenarios, where setups are nu…
Continuous Neural Algorithmic Planners
Yu He, Petar Veličković, Pietro Liò +1
Neural algorithmic reasoning studies the problem of learning algorithms with neural networks, especially with graph architectures. A recent proposal, XLVIN, reaps the benefits of u…
Expander Graph Propagation
Andreea Deac, Marc Lackenby, Petar Veličković
Deploying graph neural networks (GNNs) on whole-graph classification or regression tasks is known to be challenging: it often requires computing node features that are mindful of b…
A Generalist Neural Algorithmic Learner
Borja Ibarz, Vitaly Kurin, George Papamakarios +12
The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution. While recent years have seen a…
How to transfer algorithmic reasoning knowledge to learn new algorithms?
Louis-Pascal A. C. Xhonneux, Andreea Deac, Petar Velickovic +1
Learning to execute algorithms is a fundamental problem that has been widely studied. Prior work~\cite{veli19neural} has shown that to enable systematic generalisation on graph alg…
Neural Algorithmic Reasoners are Implicit Planners
Andreea Deac, Petar Veličković, Ognjen Milinković +3
Implicit planning has emerged as an elegant technique for combining learned models of the world with end-to-end model-free reinforcement learning. We study the class of implicit pl…
Neural message passing for joint paratope-epitope prediction
Alice Del Vecchio, Andreea Deac, Pietro Liò +1
Antibodies are proteins in the immune system which bind to antigens to detect and neutralise them. The binding sites in an antibody-antigen interaction are known as the paratope an…
Large-scale graph representation learning with very deep GNNs and self-supervision
Ravichandra Addanki, Peter W. Battaglia, David Budden +8
Effectively and efficiently deploying graph neural networks (GNNs) at scale remains one of the most challenging aspects of graph representation learning. Many powerful solutions ha…
XLVIN: eXecuted Latent Value Iteration Nets
Andreea Deac, Petar Veličković, Ognjen Milinković +3
Value Iteration Networks (VINs) have emerged as a popular method to incorporate planning algorithms within deep reinforcement learning, enabling performance improvements on tasks r…
Graph neural induction of value iteration
Andreea Deac, Pierre-Luc Bacon, Jian Tang
Many reinforcement learning tasks can benefit from explicit planning based on an internal model of the environment. Previously, such planning components have been incorporated thro…
Drug-Drug Adverse Effect Prediction with Graph Co-Attention
Andreea Deac, Yu-Hsiang Huang, Petar Veličković +2
Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects. The detection of polypharmacy side effects is u…
Attentive cross-modal paratope prediction
Andreea Deac, Petar Veličković, Pietro Sormanni
Antibodies are a critical part of the immune system, having the function of directly neutralising or tagging undesirable objects (the antigens) for future destruction. Being able t…