Search Results for author:
Found 132 papers, 64 papers with code
Graph Attention Networks
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 Classification
on Pubmed
(Validation metric)
Principal Neighbourhood Aggregation for Graph Nets
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
Do We Need Anisotropic Graph Neural Networks?
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 #11 on
Graph Property Prediction
on ogbg-code2
Deep Graph Infomax
We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner.
Ranked #49 on
Node Classification
on Citeseer
Drug-Drug Adverse Effect Prediction with Graph Co-Attention
Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects.
On the Expressive Power of Geometric Graph Neural Networks
The expressive power of Graph Neural Networks (GNNs) has been studied extensively through the Weisfeiler-Leman (WL) graph isomorphism test.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering
Then we use Poisson Surface Reconstruction to transform the reconstructed point cloud into a 3D mesh.
Ranked #2 on
Single-View 3D Reconstruction
on ShapeNet
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
The pairwise interaction paradigm of graph machine learning has predominantly governed the modelling of relational systems.
Weisfeiler and Lehman Go Cellular: CW Networks
Nevertheless, these models can be severely constrained by the rigid combinatorial structure of Simplicial Complexes (SCs).
Ranked #1 on
Graph Regression
on ZINC 100k
3D Infomax improves GNNs for Molecular Property Prediction
Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts.
CIN++: Enhancing Topological Message Passing
Our message passing scheme accounts for the aforementioned limitations by letting the cells to receive also lower messages within each layer.
Ranked #1 on
Graph Classification
on HIV dataset
gRNAde: Geometric Deep Learning for 3D RNA inverse design
Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity.
Directional Graph Networks
Then, we propose the use of the Laplacian eigenvectors as such vector field.
Ranked #2 on
Node Classification
on PATTERN 100k
Predicting Patient Outcomes with Graph Representation Learning
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.
Predicting Length of Stay in the Intensive Care Unit with Temporal Pointwise Convolutional Networks
The pressure of ever-increasing patient demand and budget restrictions make hospital bed management a daily challenge for clinical staff.
Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unit
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.
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
In these graphs, the geometric attributes transform according to the inherent physical symmetries of 3D atomic systems, including rotations and translations in Euclidean space, as well as node permutations.
Neural ODE Processes
To address these problems, we introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs.
Neural Distance Embeddings for Biological Sequences
The development of data-dependent heuristics and representations for biological sequences that reflect their evolutionary distance is critical for large-scale biological research.
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
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.
Deep Graph Mapper: Seeing Graphs through the Neural Lens
Recent advancements in graph representation learning have led to the emergence of condensed encodings that capture the main properties of a graph.
On Second Order Behaviour in Augmented Neural ODEs
Neural Ordinary Differential Equations (NODEs) are a new class of models that transform data continuously through infinite-depth architectures.
Ranked #21 on
Image Classification
on MNIST
Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches
Concept-based explanations have emerged as a popular way of extracting human-interpretable representations from deep discriminative models.
Towards a predictive spatio-temporal representation of brain data
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.
Graph Denoising Diffusion for Inverse Protein Folding
In contrast, diffusion probabilistic models, as an emerging genre of generative approaches, offer the potential to generate a diverse set of sequence candidates for determined protein backbones.
Explaining the Explainers in Graph Neural Networks: a Comparative Study
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.
XFlow: Cross-modal Deep Neural Networks for Audiovisual Classification
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.
Graph Neural Networks with Adaptive Readouts
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.
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering
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.
Algorithmic Concept-based Explainable Reasoning
Recent research on graph neural network (GNN) models successfully applied GNNs to classical graph algorithms and combinatorial optimisation problems.
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library
Multi-Agent Reinforcement Learning (MARL) encompasses a powerful class of methodologies that have been applied in a wide range of fields.
Constraining Variational Inference with Geometric Jensen-Shannon Divergence
We examine the problem of controlling divergences for latent space regularisation in variational autoencoders.
Simplicial Attention Networks
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.
Global Explainability of GNNs via Logic Combination of Learned Concepts
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.
Neural Algorithmic Reasoning for Combinatorial Optimisation
Solving NP-hard/complete combinatorial problems with neural networks is a challenging research area that aims to surpass classical approximate algorithms.
MEME: Generating RNN Model Explanations via Model Extraction
Recurrent Neural Networks (RNNs) have achieved remarkable performance on a range of tasks.
Now You See Me (CME): Concept-based Model Extraction
Deep Neural Networks (DNNs) have achieved remarkable performance on a range of tasks.
SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks
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.
Towards Sparse Hierarchical Graph Classifiers
Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graph-based benchmark tasks.
ncRNA Classification with Graph Convolutional Networks
Non-coding RNA (ncRNA) are RNA sequences which don't code for a gene but instead carry important biological functions.
Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders
In this paper we quantify the effects of the parameter $\beta$ on the model performance and disentanglement.
Attentional Meta-learners for Few-shot Polythetic Classification
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.
Meta-learning using privileged information for dynamics
Neural ODE Processes approach the problem of meta-learning for dynamics using a latent variable model, which permits a flexible aggregation of contextual information.
Distributed representations of graphs for drug pair scoring
In this paper we study the practicality and usefulness of incorporating distributed representations of graphs into models within the context of drug pair scoring.
SHARCS: Shared Concept Space for Explainable Multimodal Learning
Multimodal learning is an essential paradigm for addressing complex real-world problems, where individual data modalities are typically insufficient to accurately solve a given modelling task.
SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation
However, some of its key hurdles include domain generalisation, which is the ability to adapt to previously unseen databases, and alignment of natural language questions with the corresponding SQL queries.
HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs
In this paper, we propose a new architecture, HyperBERT, a mixed text-hypergraph model which simultaneously models hypergraph relational structure while maintaining the high-quality text encoding capabilities of a pre-trained BERT.
Unsupervised Pretraining for Fact Verification by Language Model Distillation
Fact verification aims to verify a claim using evidence from a trustworthy knowledge base.
Heavy-tailed denoising score matching
Score-based model research in the last few years has produced state of the art generative models by employing Gaussian denoising score-matching (DSM).
Score-Based Generative Models for Designing Binding Peptide Backbones
We apply our framework to design antibody binding loop structures conditional on a target epitope and evaluate a variety of modelling choices in SGM-based protein design.
Co-Attentive Cross-Modal Deep Learning for Medical Evidence Synthesis and Decision Making
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.
Adaptive Prediction Timing for Electronic Health Records
In realistic scenarios, multivariate timeseries evolve over case-by-case time-scales.
Uncertainty in Neural Relational Inference Trajectory Reconstruction
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.
Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery
By one of the most fundamental principles in physics, a dynamical system will exhibit those motions which extremise an action functional.
Automatic Inference of Cross-modal Connection Topologies for X-CNNs
The base approach learns the topology in a data-driven manner, by using measurements performed on the base CNN and supplied data.
Signal metrics analysis of oscillatory patterns in bacterial multi-omic networks
Results: Network multi-omic integration has led to the discovery of interesting oscillatory signals.
Molecular Networks Computational Engineering, Finance, and Science I.5.2
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
Existing GNN benchmarking methods for molecular representation learning focus on comparing the GNNs' performances on some node/graph classification/regression tasks on certain datasets.
Language Model Knowledge Distillation for Efficient Question Answering in Spanish
Recent advances in the development of pre-trained Spanish language models has led to significant progress in many Natural Language Processing (NLP) tasks, such as question answering.
Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data
We analyse multimodal time-series data corresponding to weight, sleep and steps measurements.
X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets
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).
Introducing Curvature to the Label Space
One-hot encoding is a labelling system that embeds classes as standard basis vectors in a label space.
Structure-Based Networks for Drug Validation
Classifying chemicals according to putative modes of action (MOAs) is of paramount importance in the context of risk assessment.
Modelling trait dependent speciation with Approximate Bayesian Computation
The user is not restricted to a predefined set of models and can specify a variety of evolutionary and branching models.
ChronoMID - Cross-Modal Neural Networks for 3-D Temporal Medical Imaging Data
ChronoMID builds on the success of cross-modal convolutional neural networks (X-CNNs), making the novel application of the technique to medical imaging data.
Spatio-Temporal Deep Graph Infomax
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.
On Graph Classification Networks, Datasets and Baselines
Graph classification receives a great deal of attention from the non-Euclidean machine learning community.
Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing
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.
Impact of novel aggregation methods for flexible, time-sensitive EHR prediction without variable selection or cleaning
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.
Graph Convolutional Gaussian Processes For Link Prediction
Link prediction aims to reveal missing edges in a graph.
Neural Bipartite Matching
Graph neural networks (GNNs) have found application for learning in the space of algorithms.
tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder
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.
Investigating Estimated Kolmogorov Complexity as a Means of Regularization for Link Prediction
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.
A Multiscale Graph Convolutional Network Using Hierarchical Clustering
The information contained in hierarchical topology, intrinsic to many networks, is currently underutilised.
RicciNets: Curvature-guided Pruning of High-performance Neural Networks Using Ricci Flow
A novel method to identify salient computational paths within randomly wired neural networks before training is proposed.
Message Passing Neural Processes
Neural Processes (NPs) are powerful and flexible models able to incorporate uncertainty when representing stochastic processes, while maintaining a linear time complexity.
The Role of Isomorphism Classes in Multi-Relational Datasets
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.
Incorporating network based protein complex discovery into automated model construction
We propose a method for gene expression based analysis of cancer phenotypes incorporating network biology knowledge through unsupervised construction of computational graphs.
Using ontology embeddings for structural inductive bias in gene expression data analysis
Stratifying cancer patients based on their gene expression levels allows improving diagnosis, survival analysis and treatment planning.
Neural message passing for joint paratope-epitope prediction
Antibodies are proteins in the immune system which bind to antigens to detect and neutralise them.
High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm
Moreover, these pipelines are deterministic in nature, making them unable to capture predictive uncertainty.
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks
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.
An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution
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.
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets
Graph-based models require aggregating information in the graph from neighbourhoods of different sizes.
Structure-aware generation of drug-like molecules
Molecules are built atom-by-atom inside pockets, guided by structural information from crystallographic data.
Spectral Transform Forms Scalable Transformer
The architecture is assembled with a few simple effective computational blocks that constitute randomized SVD, MLP, and graph Framelet convolution.
AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis
Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process.
Modular multi-source prediction of drug side-effects with DruGNN
Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes.
PC-SwinMorph: Patch Representation for Unsupervised Medical Image Registration and Segmentation
The core of our framework is two patch-based strategies, where we demonstrate that patch representation is key for performance gain.
Sheaf Neural Networks with Connection Laplacians
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 #6 on
Node Classification
on Wisconsin
Approximate Equivariance SO(3) Needlet Convolution
This paper develops a rotation-invariant needlet convolution for rotation group SO(3) to distill multiscale information of spherical signals.
Goal-Conditioned Reinforcement Learning in the Presence of an Adversary
Finally, we unify the two threads and introduce IGOAL: a novel framework for goal-conditioned learning in the presence of an adversary.
SCOTCH and SODA: A Transformer Video Shadow Detection Framework
In this work, we argue that accounting for shadow deformation is essential when designing a video shadow detection method.
Learning Feynman Diagrams using Graph Neural Networks
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.
Transductive Kernels for Gaussian Processes on Graphs
To address this, we present a novel, generalized kernel for graphs with node feature data for semi-supervised learning.
Continuous Neural Algorithmic Planners
Neural algorithmic reasoning studies the problem of learning algorithms with neural networks, especially with graph architectures.
Latent Graph Inference using Product Manifolds
The original dDGM architecture used the Euclidean plane to encode latent features based on which the latent graphs were generated.
Learning Graph Search Heuristics
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.
A physics-informed search for metric solutions to Ricci flow, their embeddings, and visualisation
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.
State of the Art and Potentialities of Graph-level Learning
Traditional approaches to learning a set of graphs heavily rely on hand-crafted features, such as substructures.
Assisting clinical practice with fuzzy probabilistic decision trees
In this work, we propose FPT, (MedFP), a novel method that combines probabilistic trees and fuzzy logic to assist clinical practice.
Sheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systems
GNNs have proven to be effective in addressing the challenges posed by recommendation systems by efficiently modeling graphs in which nodes represent users or items and edges denote preference relationships.
ElegansNet: a brief scientific report and initial experiments
The performance of the models is demonstrated against randomly wired networks and compared to artificial networks ranked on global benchmarks.
Learning Linear Embeddings for Non-Linear Network Dynamics with Koopman Message Passing
Recently, Koopman operator theory has become a powerful tool for developing linear representations of non-linear dynamical systems.
Deep Multiple Instance Learning with Distance-Aware Self-Attention
We evaluate our model on a custom MNIST-based MIL dataset that requires the consideration of relative spatial information, as well as on CAMELYON16, a publicly available cancer metastasis detection dataset, where we achieve a test AUROC score of 0. 91.
Group Invariant Global Pooling
Much work has been devoted to devising architectures that build group-equivariant representations, while invariance is often induced using simple global pooling mechanisms.
Renormalized Graph Neural Networks
It is recognized as the preferred lens through which to study complex systems, offering a framework that can untangle their intricate dynamics.
Graph Classification Gaussian Processes via Spectral Features
Graph classification aims to categorise graphs based on their structure and node attributes.
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs
Additionally, though many Graph Neural Networks (GNNs) have been proposed for representation learning on higher-order graphs, they are usually only evaluated on simple graph datasets.
Neural Embeddings for Protein Graphs
Proteins perform much of the work in living organisms, and consequently the development of efficient computational methods for protein representation is essential for advancing large-scale biological research.
Neural Priority Queues for Graph Neural Networks
Graph Neural Networks (GNNs) have shown considerable success in neural algorithmic reasoning.
Ranked #20 on
Graph Regression
on Peptides-struct
Topological Graph Signal Compression
Recently emerged Topological Deep Learning (TDL) methods aim to extend current Graph Neural Networks (GNN) by naturally processing higher-order interactions, going beyond the pairwise relations and local neighborhoods defined by graph representations.
Will More Expressive Graph Neural Networks do Better on Generative Tasks?
Moreover, we show that GCPN and GraphAF with advanced GNNs can achieve state-of-the-art results across 17 other non-GNN-based graph generative approaches, such as variational autoencoders and Bayesian optimisation models, on the proposed molecular generative objectives (DRD2, Median1, Median2), which are impor- tant metrics for de-novo molecular design.
Graph Neural Stochastic Differential Equations
We present a novel model Graph Neural Stochastic Differential Equations (Graph Neural SDEs).
From Charts to Atlas: Merging Latent Spaces into One
Models trained on semantically related datasets and tasks exhibit comparable inter-sample relations within their latent spaces.
Traffic Video Object Detection using Motion Prior
In this work, we propose two innovative methods to exploit the motion prior and boost the performance of both fully-supervised and semi-supervised traffic video object detection.
AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference
More importantly, our interpretability framework provides a general approach for quantitatively comparing embedding spaces across different tasks based on their contributions, a dimension that has been overlooked in previous literature on latent graph inference.
TRIDENT: The Nonlinear Trilogy for Implicit Neural Representations
Implicit neural representations (INRs) have garnered significant interest recently for their ability to model complex, high-dimensional data without explicit parameterisation.
Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts
A line of interpretable methods approach this by discovering a small set of relevant concepts as subgraphs in the last GNN layer that together explain the prediction.
Fourier Neural Differential Equations for learning Quantum Field Theories
A Quantum Field Theory is defined by its interaction Hamiltonian, and linked to experimental data by the scattering matrix.
TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios
Multi-object tracking in traffic videos is a crucial research area, offering immense potential for enhancing traffic monitoring accuracy and promoting road safety measures through the utilisation of advanced machine learning algorithms.
An Effective Universal Polynomial Basis for Spectral Graph Neural Networks
Afterward, we develop an adaptive heterophily basis by incorporating graph heterophily degrees.
Unsupervised Adaptive Implicit Neural Representation Learning for Scan-Specific MRI Reconstruction
In recent studies on MRI reconstruction, advances have shown significant promise for further accelerating the MRI acquisition.
Dual-Domain Multi-Contrast MRI Reconstruction with Synthesis-based Fusion Network
We also compare the reconstruction performance with existing deep learning-based methods using a dataset of brain MRI scans.
Digital Histopathology with Graph Neural Networks: Concepts and Explanations for Clinicians
To address the challenge of the ``black-box" nature of deep learning in medical settings, we combine GCExplainer - an automated concept discovery solution - along with Logic Explained Networks to provide global explanations for Graph Neural Networks.
The Deep Equilibrium Algorithmic Reasoner
Recent work on neural algorithmic reasoning has demonstrated that graph neural networks (GNNs) could learn to execute classical algorithms.
Position Paper: Challenges and Opportunities in Topological Deep Learning
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models.
Enhancing Real-World Complex Network Representations with Hyperedge Augmentation
These methods cannot fully address the complexities of real-world large-scale networks that often involve higher-order node relations beyond only being pairwise.
Understanding Biology in the Age of Artificial Intelligence
Modern life sciences research is increasingly relying on artificial intelligence approaches to model biological systems, primarily centered around the use of machine learning (ML) models.
Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach
To bypass eigendecomposition, polynomial graph filters are proposed to approximate graph filters by leveraging various polynomial bases for filter training.
Wet TinyML: Chemical Neural Network Using Gene Regulation and Cell Plasticity
This study advances this concept by incorporating cell plasticity, to further exploit natural cell's adaptability, in order to diversify the GRNN search that can match larger spectrum as well as dynamic computing tasks.
Sphere Neural-Networks for Rational Reasoning
SphNN is the first neural model that can determine the validity of long-chained syllogistic reasoning in one epoch by constructing sphere configurations as Euler diagrams, with the worst computational complexity of O(N^2).
