Search Results for author: Cristian Bodnar

Found 18 papers, 12 papers with code

Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation

no code implementations9 Nov 2023 Jialin Chen, Yuelin Wang, Cristian Bodnar, Rex Ying, Pietro Lio, Yu Guang Wang

However, recursively aggregating neighboring information with graph convolutions leads to indistinguishable node features in deep layers, which is known as the over-smoothing issue.

Node Classification

CIN++: Enhancing Topological Message Passing

1 code implementation6 Jun 2023 Lorenzo Giusti, Teodora Reu, Francesco Ceccarelli, Cristian Bodnar, Pietro Liò

Our message passing scheme accounts for the aforementioned limitations by letting the cells to receive also lower messages within each layer.

Graph Classification Graph Regression

On the Expressive Power of Geometric Graph Neural Networks

1 code implementation23 Jan 2023 Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Liò

The expressive power of Graph Neural Networks (GNNs) has been studied extensively through the Weisfeiler-Leman (WL) graph isomorphism test.

Sheaf Neural Networks with Connection Laplacians

1 code implementation17 Jun 2022 Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael Bronstein, Petar Veličković, Pietro Liò

A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) that operates on a sheaf, an object that equips a graph with vector spaces over its nodes and edges and linear maps between these spaces.

Node Classification

Simplicial Attention Networks

1 code implementation20 Apr 2022 Christopher Wei Jin Goh, Cristian Bodnar, Pietro Liò

Leveraging the success of attention mechanisms in structured domains, we propose Simplicial Attention Networks (SAT), a new type of simplicial network that dynamically weighs the interactions between neighbouring simplicies and can readily adapt to novel structures.

Graph Representation Learning

Neural ODE Processes: A Short Summary

1 code implementation NeurIPS Workshop DLDE 2021 Alexander Luke Ian Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Lio

To this end, we introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs.

Time Series Time Series Analysis

On Second Order Behaviour in Augmented Neural ODEs: A Short Summary

no code implementations NeurIPS Workshop DLDE 2021 Alexander Luke Ian Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Lio

In Norcliffe et al.[13], we discussed and systematically analysed how Neural ODEs (NODEs) can learn higher-order order dynamics.

Neural ODE Processes

2 code implementations ICLR 2021 Alexander Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Liò

To address these problems, we introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs.

Time Series Time Series Analysis

A Geometric Perspective on Self-Supervised Policy Adaptation

no code implementations14 Nov 2020 Cristian Bodnar, Karol Hausman, Gabriel Dulac-Arnold, Rico Jonschkowski

One of the most challenging aspects of real-world reinforcement learning (RL) is the multitude of unpredictable and ever-changing distractions that could divert an agent from what was tasked to do in its training environment.

Reinforcement Learning (RL)

The Role of Isomorphism Classes in Multi-Relational Datasets

no code implementations30 Sep 2020 Vijja Wichitwechkarn, Ben Day, Cristian Bodnar, Matthew Wales, Pietro Liò

The current training and evaluation procedures for these models through the use of synthetic multi-relational datasets however are agnostic to interaction network isomorphism classes, which produce identical dynamics up to initial conditions.

On Second Order Behaviour in Augmented Neural ODEs

1 code implementation NeurIPS 2020 Alexander Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Liò

Neural Ordinary Differential Equations (NODEs) are a new class of models that transform data continuously through infinite-depth architectures.

Image Classification

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

Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping

no code implementations1 Oct 2019 Cristian Bodnar, Adrian Li, Karol Hausman, Peter Pastor, Mrinal Kalakrishnan

The absence of an actor in Q2-Opt allows us to directly draw a parallel to the previous discrete experiments in the literature without the additional complexities induced by an actor-critic architecture.

Q-Learning Reinforcement Learning (RL) +1

Proximal Distilled Evolutionary Reinforcement Learning

1 code implementation24 Jun 2019 Cristian Bodnar, Ben Day, Pietro Lió

We propose a novel algorithm called Proximal Distilled Evolutionary Reinforcement Learning (PDERL) that is characterised by a hierarchical integration between evolution and learning.

OpenAI Gym reinforcement-learning +1

Text to Image Synthesis Using Generative Adversarial Networks

no code implementations2 May 2018 Cristian Bodnar

Then, I show how the novel loss function of Wasserstein GAN-CLS can be used in a Conditional Progressive Growing GAN.

Conditional Image Generation Sentence

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