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Found 14 papers, 7 papers with code
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
The choice of a graph learning (GL) model (i. e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks.
Forward Learning of Graph Neural Networks
To address these limitations, the forward-forward algorithm (FF) was recently proposed as an alternative to BP in the image classification domain, which trains NNs by performing two forward passes over positive and negative data.
MPIrigen: MPI Code Generation through Domain-Specific Language Models
This study first investigates the performance of state-of-the-art language models in generating MPI-based parallel programs.
OMPGPT: A Generative Pre-trained Transformer Model for OpenMP
Large language models (LLMs), as epitomized by models like ChatGPT, have revolutionized the field of natural language processing (NLP).
Domain-Specific Code Language Models: Unraveling the Potential for HPC Codes and Tasks
Specifically, we start off with HPC as a domain and build an HPC-specific LM, named MonoCoder, that is orders of magnitude smaller than existing LMs but delivers similar, if not better performance, on non-HPC and HPC tasks.
Scope is all you need: Transforming LLMs for HPC Code
With easier access to powerful compute resources, there is a growing trend in the field of AI for software development to develop larger and larger language models (LLMs) to address a variety of programming tasks.
Causal Lifting and Link Prediction
Existing causal models for link prediction assume an underlying set of inherent node factors -- an innate characteristic defined at the node's birth -- that governs the causal evolution of links in the graph.
A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings
In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph.
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning
In this work, we develop the first meta-learning approach for evaluation-free graph learning model selection, called MetaGL, which utilizes the prior performances of existing methods on various benchmark graph datasets to automatically select an effective model for the new graph, without any model training or evaluations.
Network Report: A Structured Description for Network Datasets
Challenges arise as networks are often used across different domains (e. g., network science, physics, etc) and have complex structures.
CGC: Contrastive Graph Clustering for Community Detection and Tracking
Especially, deep graph clustering (DGC) methods have successfully extended deep clustering to graph-structured data by learning node representations and cluster assignments in a joint optimization framework.
Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes
In this paper, we introduce the online and streaming MAP inference and learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where data points arrive in an arbitrary order and the algorithms are constrained to use a single-pass over the data as well as sub-linear memory.
Neural Algorithms for Graph Navigation
The application of deep reinforcement learning (RL) to graph learning and meta-learning admits challenges from both topics.
Inferring Individual Level Causal Models from Graph-based Relational Time Series
We propose causal inference models for this problem that leverage both the graph topology and time-series to accurately estimate local causal effects of nodes.
