192 citations · 334 across the 14 of their papers we have counts for
9 papers · 1 filter
Goal-Conditioned Reinforcement Learning in the Presence of an Adversary
Carlos Purves, Pietro Liò, Cătălina Cangea
Reinforcement learning has seen increasing applications in real-world contexts over the past few years. However, physical environments are often imperfect and policies that perform…
Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task
Jannik Kossen, Cătălina Cangea, Eszter Vértes +5
We introduce a challenging decision-making task that we call active acquisition for multimodal temporal data (A2MT). In many real-world scenarios, input features are not readily av…
General-purpose, long-context autoregressive modeling with Perceiver AR
Curtis Hawthorne, Andrew Jaegle, Cătălina Cangea +12
Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression. However, the most commonly u…
Message Passing Neural Processes
Ben Day, Cătălina Cangea, Arian R. Jamasb +1
Neural Processes (NPs) are powerful and flexible models able to incorporate uncertainty when representing stochastic processes, while maintaining a linear time complexity. However,…
Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks
Péter Mernyei, Cătălina Cangea
We present Wiki-CS, a novel dataset derived from Wikipedia for benchmarking Graph Neural Networks. The dataset consists of nodes corresponding to Computer Science articles, with ed…
Sparse Dynamic Distribution Decomposition: Efficient Integration of Trajectory and Snapshot Time Series Data
Jake P. Taylor-King, Cristian Regep, Jyothish Soman +3
Dynamic Distribution Decomposition (DDD) was introduced in Taylor-King et. al. (PLOS Comp Biol, 2020) as a variation on Dynamic Mode Decomposition. In brief, by using basis functio…
Deep Graph Mapper: Seeing Graphs through the Neural Lens
Cristian Bodnar, Cătălina Cangea, Pietro Liò
Recent advancements in graph representation learning have led to the emergence of condensed encodings that capture the main properties of a graph. However, even though these abstra…
The PlayStation Reinforcement Learning Environment (PSXLE)
Carlos Purves, Cătălina Cangea, Petar Veličković
We propose a new benchmark environment for evaluating Reinforcement Learning (RL) algorithms: the PlayStation Learning Environment (PSXLE), a PlayStation emulator modified to expos…
Spatio-Temporal Deep Graph Infomax
Felix L. Opolka, Aaron Solomon, Cătălina Cangea +3
Spatio-temporal graphs such as traffic networks or gene regulatory systems present challenges for the existing deep learning methods due to the complexity of structural changes ove…