You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

1 code implementation • 23 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.

no code implementations • 7 Dec 2022 • Michal Pándy, Weikang Qiu, Gabriele Corso, Petar Veličković, Rex Ying, Jure Leskovec, Pietro Liò

At training time, we aggregate datasets of search trajectories and ground-truth shortest path distances, which we use to train a specialized graph neural network-based heuristic function using backpropagation through steps of the pathfinding process.

no code implementations • 30 Nov 2022 • Aarjav Jain, Challenger Mishra, Pietro Liò

Neural networks with PDEs embedded in their loss functions (physics-informed neural networks) are employed as a function approximators to find solutions to the Ricci flow (a curvature based evolution) of Riemannian metrics.

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 • 28 Nov 2022 • Yin-Cong Zhi, Felix L. Opolka, Yin Cheng Ng, Pietro Liò, Xiaowen Dong

To address this, we present a novel, generalized kernel for graphs with node feature data for semi-supervised learning.

no code implementations • 26 Nov 2022 • Haitz Sáez de Ocáriz Borde, Anees Kazi, Federico Barbero, Pietro Liò

The original dDGM architecture used the Euclidean plane to encode latent features based on which the latent graphs were generated.

no code implementations • 25 Nov 2022 • Harrison Mitchell, Alexander Norcliffe, Pietro Liò

In the wake of the growing popularity of machine learning in particle physics, this work finds a new application of geometric deep learning on Feynman diagrams to make accurate and fast matrix element predictions with the potential to be used in analysis of quantum field theory.

1 code implementation • 20 Nov 2022 • Yana Lishkova, Paul Scherer, Steffen Ridderbusch, Mateja Jamnik, Pietro Liò, Sina Ober-Blöbaum, Christian Offen

By one of the most fundamental principles in physics, a dynamical system will exhibit those motions which extremise an action functional.

no code implementations • 13 Nov 2022 • Lihao Liu, Jean Prost, Lei Zhu, Nicolas Papadakis, Pietro Liò, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero

Shadows in videos are difficult to detect because of the large shadow deformation between frames.

no code implementations • 13 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.

1 code implementation • 9 Nov 2022 • David Buterez, Jon Paul Janet, Steven J. Kiddle, Dino Oglic, Pietro Liò

We argue that in some problems such as binding affinity prediction where molecules are typically presented in a canonical form it might be possible to relax the constraints on permutation invariance of the hypothesis space and learn a more effective model of the affinity by employing an adaptive readout function.

1 code implementation • 27 Oct 2022 • Antonio Longa, Steve Azzolin, Gabriele Santin, Giulia Cencetti, Pietro Liò, Bruno Lepri, Andrea Passerini

Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need for methods to understand their decision process.

no code implementations • 13 Oct 2022 • Steve Azzolin, Antonio Longa, Pietro Barbiero, Pietro Liò, Andrea Passerini

While instance-level explanation of GNN is a well-studied problem with plenty of approaches being developed, providing a global explanation for the behaviour of a GNN is much less explored, despite its potential in interpretability and debugging.

1 code implementation • 19 Sep 2022 • Paul Scherer, Pietro Liò, Mateja Jamnik

In this paper we study the practicality and usefulness of incorporating distributed representations of graphs into models within the context of drug pair scoring.

1 code implementation • 16 Jul 2022 • Davide Buffelli, Pietro Liò, Fabio Vandin

Previous works have tried to tackle this issue in graph classification by providing the model with inductive biases derived from assumptions on the generative process of the graphs, or by requiring access to graphs from the test domain.

no code implementations • 17 Jun 2022 • Kai Yi, Jialin Chen, Yu Guang Wang, Bingxin Zhou, Pietro Liò, Yanan Fan, Jan Hamann

This paper develops a rotation-invariant needlet convolution for rotation group SO(3) to distill multiscale information of spherical signals.

no code implementations • 17 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.

Ranked #3 on Node Classification on Wisconsin

1 code implementation • 20 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.

no code implementations • 10 Mar 2022 • Lihao Liu, Zhening Huang, Pietro Liò, Carola-Bibiane Schönlieb, Angelica I. Aviles-Rivero

The core of our framework is two patch-based strategies, where we demonstrate that patch representation is key for performance gain.

no code implementations • 15 Feb 2022 • Pietro Bongini, Franco Scarselli, Monica Bianchini, Giovanna Maria Dimitri, Niccolò Pancino, Pietro Liò

Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes.

1 code implementation • 9 Feb 2022 • Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Liò, Michael M. Bronstein

In this paper, we use cellular sheaf theory to show that the underlying geometry of the graph is deeply linked with the performance of GNNs in heterophilic settings and their oversmoothing behaviour.

Ranked #1 on Node Classification on Wisconsin

no code implementations • 23 Dec 2021 • Yutong Chen, Carola-Bibiane Schönlieb, Pietro Liò, Tim Leiner, Pier Luigi Dragotti, Ge Wang, Daniel Rueckert, David Firmin, Guang Yang

Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process.

1 code implementation • 17 Dec 2021 • Jacob Deasy, Nikola Simidjievski, Pietro Liò

Score-based model research in the last few years has produced state of the art generative models by employing Gaussian denoising score-matching (DSM).

1 code implementation • 15 Nov 2021 • Bingxin Zhou, Xinliang Liu, Yuehua Liu, Yunying Huang, Pietro Liò, Yuguang Wang

The architecture is assembled with a few simple effective computational blocks that constitute randomized SVD, MLP, and graph Framelet convolution.

no code implementations • 7 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.

no code implementations • 25 Oct 2021 • Felix L. Opolka, Yin-Cong Zhi, Pietro Liò, Xiaowen Dong

Graph-based models require aggregating information in the graph from neighbourhoods of different sizes.

1 code implementation • NeurIPS Workshop AI4Scien 2021 • Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Liò

Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts.

no code implementations • 30 Sep 2021 • James King, Ramon Viñas Torné, Alexander Campbell, Pietro Liò

Our paper compares the pre-upsampling AudioUNet to a new generative model that upsamples the signal before using deep learning to transform it into a more believable signal.

1 code implementation • NeurIPS 2021 • Gabriele Corso, Rex Ying, Michal Pándy, Petar Veličković, Jure Leskovec, Pietro Liò

The development of data-dependent heuristics and representations for biological sequences that reflect their evolutionary distance is critical for large-scale biological research.

no code implementations • 25 Jul 2021 • Lucie Charlotte Magister, Dmitry Kazhdan, Vikash Singh, Pietro Liò

Motivated by the aim of providing global explanations, we adapt the well-known Automated Concept-based Explanation approach (Ghorbani et al., 2019) to GNN node and graph classification, and propose GCExplainer.

no code implementations • 21 Jul 2021 • Lorena Qendro, Alexander Campbell, Pietro Liò, Cecilia Mascolo

Moreover, these pipelines are deterministic in nature, making them unable to capture predictive uncertainty.

no code implementations • 15 Jul 2021 • Dobrik Georgiev, Pietro Barbiero, Dmitry Kazhdan, Petar Veličković, Pietro Liò

Recent research on graph neural network (GNN) models successfully applied GNNs to classical graph algorithms and combinatorial optimisation problems.

1 code implementation • NeurIPS 2021 • Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yu Guang Wang, Pietro Liò, Guido Montúfar, Michael Bronstein

Nevertheless, these models can be severely constrained by the rigid combinatorial structure of Simplicial Complexes (SCs).

Ranked #1 on Graph Regression on ZINC 100k

1 code implementation • 9 Jun 2021 • Ben Day, Ramon Viñas, Nikola Simidjievski, Pietro Liò

Polythetic classifications, based on shared patterns of features that need neither be universal nor constant among members of a class, are common in the natural world and greatly outnumber monothetic classifications over a set of features.

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.

1 code implementation • ICLR Workshop Learning_to_Learn 2021 • Ben Day, Alexander Norcliffe, Jacob Moss, Pietro Liò

Neural ODE Processes approach the problem of meta-learning for dynamics using a latent variable model, which permits a flexible aggregation of contextual information.

1 code implementation • 14 Apr 2021 • Dmitry Kazhdan, Botty Dimanov, Helena Andres Terre, Mateja Jamnik, Pietro Liò, Adrian Weller

Concept-based explanations have emerged as a popular way of extracting human-interpretable representations from deep discriminative models.

2 code implementations • 3 Apr 2021 • Shyam A. Tailor, Felix L. Opolka, Pietro Liò, Nicholas D. Lane

We demonstrate that EGC outperforms existing approaches across 6 large and diverse benchmark datasets, and conclude by discussing questions that our work raise for the community going forward.

Ranked #10 on Graph Property Prediction on ogbg-code2

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.

1 code implementation • 5 Mar 2021 • Nikola Zubić, Pietro Liò

Then we use Poisson Surface Reconstruction to transform the reconstructed point cloud into a 3D mesh.

Ranked #1 on 3D Reconstruction on ShapeNet (Mean metric)

1 code implementation • ICLR Workshop GTRL 2021 • Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang, Nina Otter, Guido Montúfar, Pietro Liò, Michael Bronstein

The pairwise interaction paradigm of graph machine learning has predominantly governed the modelling of relational systems.

1 code implementation • 11 Jan 2021 • Emma Rocheteau, Catherine Tong, Petar Veličković, Nicholas Lane, Pietro Liò

Recent work on predicting patient outcomes in the Intensive Care Unit (ICU) has focused heavily on the physiological time series data, largely ignoring sparse data such as diagnoses and medications.

1 code implementation • 13 Dec 2020 • Dmitry Kazhdan, Botty Dimanov, Mateja Jamnik, Pietro Liò

Recurrent Neural Networks (RNNs) have achieved remarkable performance on a range of tasks.

no code implementations • 22 Nov 2020 • Maja Trębacz, Zohreh Shams, Mateja Jamnik, Paul Scherer, Nikola Simidjievski, Helena Andres Terre, Pietro Liò

Stratifying cancer patients based on their gene expression levels allows improving diagnosis, survival analysis and treatment planning.

1 code implementation • 25 Oct 2020 • Dmitry Kazhdan, Botty Dimanov, Mateja Jamnik, Pietro Liò, Adrian Weller

Deep Neural Networks (DNNs) have achieved remarkable performance on a range of tasks.

2 code implementations • 6 Oct 2020 • Dominique Beaini, Saro Passaro, Vincent Létourneau, William L. Hamilton, Gabriele Corso, Pietro Liò

Then, we propose the use of the Laplacian eigenvectors as such vector field.

Ranked #2 on Graph Classification on CIFAR10 100k

no code implementations • 30 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.

no code implementations • 29 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.

no code implementations • 29 Sep 2020 • Paul Scherer, Maja Trȩbacz, Nikola Simidjievski, Zohreh Shams, Helena Andres Terre, Pietro Liò, Mateja Jamnik

We propose a method for gene expression based analysis of cancer phenotypes incorporating network biology knowledge through unsupervised construction of computational graphs.

1 code implementation • 1 Aug 2020 • Francesco Bardozzo, Pietro Liò, Roberto Tagliaferri

Results: Network multi-omic integration has led to the discovery of interesting oscillatory signals.

Molecular Networks Computational Engineering, Finance, and Science I.5.2

1 code implementation • 18 Jul 2020 • Emma Rocheteau, Pietro Liò, Stephanie Hyland

In this work, we propose a new deep learning model based on the combination of temporal convolution and pointwise (1x1) convolution, to solve the length of stay prediction task on the eICU and MIMIC-IV critical care datasets.

1 code implementation • 8 Jul 2020 • Samuel Glass, Simeon Spasov, Pietro Liò

A novel method to identify salient computational paths within randomly wired neural networks before training is proposed.

1 code implementation • 29 Jun 2020 • Emma Rocheteau, Pietro Liò, Stephanie Hyland

The pressure of ever-increasing patient demand and budget restrictions make hospital bed management a daily challenge for clinical staff.

1 code implementation • 24 Jun 2020 • Vasileios Karavias, Ben Day, Pietro Liò

Neural networks used for multi-interaction trajectory reconstruction lack the ability to estimate the uncertainty in their outputs, which would be useful to better analyse and understand the systems they model.

no code implementations • 22 Jun 2020 • Alex Lipov, Pietro Liò

The information contained in hierarchical topology, intrinsic to many networks, is currently underutilised.

1 code implementation • NeurIPS 2020 • Jacob Deasy, Nikola Simidjievski, Pietro Liò

We examine the problem of controlling divergences for latent space regularisation in variational autoencoders.

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.

Ranked #23 on Image Classification on MNIST

no code implementations • 8 Jun 2020 • Alex Campbell, Pietro Liò

Using spatiotemporally correlated image time series as an example, we show that the choice of which correlation structures to explicitly represent in the latent space has a significant impact on model performance in terms of reconstruction.

no code implementations • 7 Jun 2020 • Paris D. L. Flood, Ramon Viñas, Pietro Liò

We investigate a flexible means of regularization for link prediction based on an approximation of the Kolmogorov complexity of graphs that is differentiable and compatible with recent advances in link prediction algorithms.

no code implementations • 22 May 2020 • Dobrik Georgiev, Pietro Liò

Graph neural networks (GNNs) have found application for learning in the space of algorithms.

1 code implementation • 16 Apr 2020 • Dmitry Kazhdan, Zohreh Shams, Pietro Liò

Multi-Agent Reinforcement Learning (MARL) encompasses a powerful class of methodologies that have been applied in a wide range of fields.

8 code implementations • NeurIPS 2020 • Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Veličković

Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data.

Ranked #4 on Node Classification on PATTERN 100k

1 code implementation • 5 Mar 2020 • Jacob Deasy, Ari Ercole, Pietro Liò

In realistic scenarios, multivariate timeseries evolve over case-by-case time-scales.

1 code implementation • 29 Feb 2020 • Tiago Azevedo, Luca Passamonti, Pietro Liò, Nicola Toschi

The characterisation of the brain as a "connectome", in which the connections are represented by correlational values across timeseries and as summary measures derived from graph theory analyses, has been very popular in the last years.

no code implementations • 11 Feb 2020 • Felix L. Opolka, Pietro Liò

Link prediction aims to reveal missing edges in a graph.

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.

no code implementations • 17 Sep 2019 • Jacob Deasy, Ari Ercole, Pietro Liò

Dynamic assessment of patient status (e. g. by an automated, continuously updated assessment of outcome) in the Intensive Care Unit (ICU) is of paramount importance for early alerting, decision support and resource allocation.

1 code implementation • 13 Sep 2019 • Devin Taylor, Simeon Spasov, Pietro Liò

Modern medicine requires generalised approaches to the synthesis and integration of multimodal data, often at different biological scales, that can be applied to a variety of evidence structures, such as complex disease analyses and epidemiological models.

no code implementations • 13 Sep 2019 • Jacob Deasy, Pietro Liò, Ari Ercole

Recordings in the first few hours of a patient's stay were found to be strongly predictive of mortality, outperforming models using SAPS II and OASIS scores within just 2 hours and achieving a state of the art Area Under the Receiver Operating Characteristic (AUROC) value of 0. 80 (95% CI 0. 79-0. 80) at 12 hours vs 0. 70 and 0. 66 for SAPS II and OASIS at 24 hours respectively.

1 code implementation • 14 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.

1 code implementation • 16 May 2019 • Emanuele Rossi, Federico Monti, Michael Bronstein, Pietro Liò

Non-coding RNA (ncRNA) are RNA sequences which don't code for a gene but instead carry important biological functions.

no code implementations • 12 May 2019 • Enxhell Luzhnica, Ben Day, Pietro Liò

Graph classification receives a great deal of attention from the non-Euclidean machine learning community.

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.

no code implementations • 12 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.

no code implementations • 12 Jan 2019 • Alexander G. Rakowski, Petar Veličković, Enrico Dall'Ara, Pietro Liò

ChronoMID builds on the success of cross-modal convolutional neural networks (X-CNNs), making the novel application of the technique to medical imaging data.

no code implementations • 10 Dec 2018 • Krzysztof Bartoszek, Pietro Liò

The user is not restricted to a predefined set of models and can specify a variety of evolutionary and branching models.

no code implementations • 21 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.

1 code implementation • 3 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.

no code implementations • 22 Oct 2018 • Conor Sheehan, Ben Day, Pietro Liò

One-hot encoding is a labelling system that embeds classes as standard basis vectors in a label space.

11 code implementations • ICLR 2019 • Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R. Devon Hjelm

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner.

Ranked #45 on Node Classification on Citeseer

1 code implementation • 2 May 2018 • Laurynas Karazija, Petar Veličković, Pietro Liò

The base approach learns the topology in a data-driven manner, by using measurements performed on the base CNN and supplied data.

1 code implementation • 24 Nov 2017 • Momchil Peychev, Petar Veličković, Pietro Liò

In this paper we quantify the effects of the parameter $\beta$ on the model performance and disentanglement.

78 code implementations • ICLR 2018 • Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.

Ranked #1 on Node Property Prediction on ogbn-proteins

no code implementations • 23 Sep 2017 • Petar Veličković, Laurynas Karazija, Nicholas D. Lane, Sourav Bhattacharya, Edgar Liberis, Pietro Liò, Angela Chieh, Otmane Bellahsen, Matthieu Vegreville

We analyse multimodal time-series data corresponding to weight, sleep and steps measurements.

1 code implementation • 2 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.

no code implementations • 1 Oct 2016 • Petar Veličković, Duo Wang, Nicholas D. Lane, Pietro Liò

In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the network---thus generalising the already well-established concept of neural network ensembles (where information typically may flow only between the output layers of the individual networks).

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.