Search Results for author: Cătălina Cangea

Found 15 papers, 9 papers with code

Goal-Conditioned Reinforcement Learning in the Presence of an Adversary

no code implementations13 Nov 2022 Carlos Purves, Pietro Liò, Cătălina Cangea

Finally, we unify the two threads and introduce IGOAL: a novel framework for goal-conditioned learning in the presence of an adversary.

reinforcement-learning Reinforcement Learning (RL)

Structure-aware generation of drug-like molecules

no code implementations7 Nov 2021 Pavol Drotár, Arian Rokkum Jamasb, Ben Day, Cătălina Cangea, Pietro Liò

Molecules are built atom-by-atom inside pockets, guided by structural information from crystallographic data.

Message Passing Neural Processes

no code implementations29 Sep 2020 Ben Day, Cătălina Cangea, Arian R. Jamasb, Pietro Liò

Neural Processes (NPs) are powerful and flexible models able to incorporate uncertainty when representing stochastic processes, while maintaining a linear time complexity.

Few-Shot Learning

Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks

1 code implementation6 Jul 2020 Péter Mernyei, Cătălina Cangea

We present Wiki-CS, a novel dataset derived from Wikipedia for benchmarking Graph Neural Networks.

Benchmarking Link Prediction +1

Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation

1 code implementation17 May 2020 Boris Knyazev, Harm de Vries, Cătălina Cangea, Graham W. Taylor, Aaron Courville, Eugene Belilovsky

We show that such models can suffer the most in their ability to generalize to rare compositions, evaluating two different models on the Visual Genome dataset and its more recent, improved version, GQA.

Graph Generation Scene Graph Generation

Deep Graph Mapper: Seeing Graphs through the Neural Lens

1 code implementation NeurIPS Workshop TDA_and_Beyond 2020 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.

Graph Classification Graph Representation Learning +1

The PlayStation Reinforcement Learning Environment (PSXLE)

1 code implementation12 Dec 2019 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 expose a simple control API that enables rich game-state representations.

OpenAI Gym reinforcement-learning +1

VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering

1 code implementation14 Aug 2019 Cătălina Cangea, Eugene Belilovsky, Pietro Liò, Aaron Courville

The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the EQA task.

Embodied Question Answering Question Answering +1

Spatio-Temporal Deep Graph Infomax

no code implementations12 Apr 2019 Felix L. Opolka, Aaron Solomon, Cătălina Cangea, Petar Veličković, Pietro Liò, R. Devon Hjelm

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 over time.

Representation Learning Traffic Prediction

Structure-Based Networks for Drug Validation

no code implementations21 Nov 2018 Cătălina Cangea, Arturas Grauslys, Pietro Liò, Francesco Falciani

Classifying chemicals according to putative modes of action (MOAs) is of paramount importance in the context of risk assessment.

Towards Sparse Hierarchical Graph Classifiers

1 code implementation3 Nov 2018 Cătălina Cangea, Petar Veličković, Nikola Jovanović, Thomas Kipf, Pietro Liò

Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graph-based benchmark tasks.

General Classification Graph Classification +3

XFlow: Cross-modal Deep Neural Networks for Audiovisual Classification

1 code implementation2 Sep 2017 Cătălina Cangea, Petar Veličković, Pietro Liò

Our work improves on existing multimodal deep learning algorithms in two essential ways: (1) it presents a novel method for performing cross-modality (before features are learned from individual modalities) and (2) extends the previously proposed cross-connections which only transfer information between streams that process compatible data.

Classification General Classification +2

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