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Found 9 papers, 7 papers with code
Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling
We refer to this subgraph as a task-oriented subgraph (TOSG), which contains a subset of task-related node and edge types in G. Training the task using TOSG instead of G alleviates the excessive computation required for a large KG.
SLAMB: Accelerated Large Batch Training with Sparse Communication
Distributed training of large deep neural networks requires frequent exchange of massive data between machines, thus communication efficiency is a major concern.
A Universal Question-Answering Platform for Knowledge Graphs
Knowledge from diverse application domains is organized as knowledge graphs (KGs) that are stored in RDF engines accessible in the web via SPARQL endpoints.
ChatGPT versus Traditional Question Answering for Knowledge Graphs: Current Status and Future Directions Towards Knowledge Graph Chatbots
Conversational AI and Question-Answering systems (QASs) for knowledge graphs (KGs) are both emerging research areas: they empower users with natural language interfaces for extracting information easily and effectively.
Rethinking gradient sparsification as total error minimization
Unlike with Top-$k$ sparsifier, we show that hard-threshold has the same asymptotic convergence and linear speedup property as SGD in the convex case and has no impact on the data-heterogeneity in the non-convex case.
DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning
DeepReduce is orthogonal to existing gradient sparsifiers and can be applied in conjunction with them, transparently to the end-user, to significantly lower the communication overhead.
DeepReduce: A Sparse-tensor Communication Framework for Distributed Deep Learning
This paper introduces DeepReduce, a versatile framework for the compressed communication of sparse tensors, tailored for distributed deep learning.
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning
Compressed communication, in the form of sparsification or quantization of stochastic gradients, is employed to reduce communication costs in distributed data-parallel training of deep neural networks.
Scaling Distributed Machine Learning with In-Network Aggregation
Training machine learning models in parallel is an increasingly important workload.
