no code implementations • 22 Jun 2023 • 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 they belong to the same class.
no code implementations • 6 Jun 2023 • Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Veličković
In this paper, we provide a rigorous analysis to determine which function classes of node features can be learned by an MPNN of a given capacity.
no code implementations • 28 May 2023 • Marco Pegoraro, Clémentine Dominé, Emanuele Rodolà, Petar Veličković, Andreea Deac
Antibody-antigen interactions play a crucial role in identifying and neutralizing harmful foreign molecules.
no code implementations • 9 Feb 2023 • Andreea Deac, Théophane Weber, George Papamakarios
Model-based reinforcement learning algorithms, such as the highly successful MuZero, aim to accomplish this by learning a world model.
Model-based Reinforcement Learning reinforcement-learning +2
no code implementations • 29 Nov 2022 • Yu He, Petar Veličković, Pietro Liò, Andreea Deac
Neural algorithmic reasoning studies the problem of learning algorithms with neural networks, especially with graph architectures.
no code implementations • 6 Oct 2022 • 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 both local interactions in their neighbourhood and the global context of the graph structure.
2 code implementations • 22 Sep 2022 • Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Dudzik, Matko Bošnjak, Alex Vitvitskyi, Yulia Rubanova, Andreea Deac, Beatrice Bevilacqua, Yaroslav Ganin, Charles Blundell, Petar Veličković
The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution.
no code implementations • NeurIPS 2021 • Louis-Pascal A. C. Xhonneux, Andreea Deac, Petar Velickovic, Jian Tang
Due to the fundamental differences between algorithmic reasoning knowledge and feature extractors such as used in Computer Vision or NLP, we hypothesise that standard transfer techniques will not be sufficient to achieve systematic generalisation.
no code implementations • NeurIPS 2021 • Andreea Deac, Petar Veličković, Ognjen Milinković, Pierre-Luc Bacon, Jian Tang, Mladen Nikolić
We find that prior approaches either assume that the environment is provided in such a tabular form -- which is highly restrictive -- or infer "local neighbourhoods" of states to run value iteration over -- for which we discover an algorithmic bottleneck effect.
1 code implementation • 20 Jul 2021 • Ravichandra Addanki, Peter W. Battaglia, David Budden, Andreea Deac, Jonathan Godwin, Thomas Keck, Wai Lok Sibon Li, Alvaro Sanchez-Gonzalez, Jacklynn Stott, Shantanu Thakoor, Petar Veličković
In doing so, we demonstrate evidence of scalable self-supervised graph representation learning, and utility of very deep GNNs -- both very important open issues.
no code implementations • 31 May 2021 • Alice Del Vecchio, Andreea Deac, Pietro Liò, Petar Veličković
Antibodies are proteins in the immune system which bind to antigens to detect and neutralise them.
no code implementations • 25 Oct 2020 • Andreea Deac, Petar Veličković, Ognjen Milinković, Pierre-Luc Bacon, Jian Tang, Mladen Nikolić
Value Iteration Networks (VINs) have emerged as a popular method to incorporate planning algorithms within deep reinforcement learning, enabling performance improvements on tasks requiring long-range reasoning and understanding of environment dynamics.
no code implementations • 26 Sep 2020 • Andreea Deac, Pierre-Luc Bacon, Jian Tang
Previously, such planning components have been incorporated through a neural network that partially aligns with the computational graph of value iteration.
no code implementations • 25 Sep 2019 • Andreea Deac, Yu-Hsiang Huang, Petar Velickovic, Pietro Lio, Jian Tang
Through many recent advances in graph representation learning, performance achieved on tasks involving graph-structured data has substantially increased in recent years---mostly on tasks involving node-level predictions.
1 code implementation • 2 May 2019 • Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, Jian Tang
Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects.
Ranked #3 on Drug–drug Interaction Extraction on DrugBank
no code implementations • 12 Jun 2018 • 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.