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Found 13 papers, 6 papers with code
Hint Marginalization for Improved Reasoning in Large Language Models
Large Language Models (LLMs) have exhibited an impressive capability to perform reasoning tasks, especially if they are encouraged to generate a sequence of intermediate steps.
Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data
Despite recent advances in training and prompting strategies for Large Language Models (LLMs), these models continue to face challenges with complex logical reasoning tasks that involve long reasoning chains.
CKGConv: General Graph Convolution with Continuous Kernels
In this work, we propose a novel and general graph convolution framework by parameterizing the kernels as continuous functions of pseudo-coordinates derived via graph positional encoding.
Ranked #1 on
Graph Classification
on CIFAR-10
Multi-resolution Time-Series Transformer for Long-term Forecasting
Recent architectures learn complex temporal patterns by segmenting a time-series into patches and using the patches as tokens.
Node Copying: A Random Graph Model for Effective Graph Sampling
In this work, we introduce the node copying model for constructing a distribution over graphs.
Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks
Since a meaningful graph representing dependencies between bags is rarely available, we propose to use a Bayesian GNN framework that can generate a likely graph structure for scenarios where there is uncertainty in the graph or when no graph is available.
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks.
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation
Node classification in attributed graphs is an important task in multiple practical settings, but it can often be difficult or expensive to obtain labels.
Node Copying for Protection Against Graph Neural Network Topology Attacks
With the increased interest in graph based machine learning techniques, there have been investigations which suggest that these models are also vulnerable to attacks.
Non-Parametric Graph Learning for Bayesian Graph Neural Networks
A Bayesian framework which targets posterior inference of the graph by considering it as a random quantity can be beneficial.
Bayesian Graph Convolutional Neural Networks using Node Copying
Graph convolutional neural networks (GCNN) have numerous applications in different graph based learning tasks.
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning
Graph convolutional neural networks (GCNN) have been successfully applied to many different graph based learning tasks including node and graph classification, matrix completion, and learning of node embeddings.
Bayesian graph convolutional neural networks for semi-supervised classification
Graph convolutional neural networks (GCNNs) have been used to address node and graph classification and matrix completion.
