Search Results for author:
Found 5 papers, 2 papers with code
Global and Local Structure Learning for Sparse Tensor Completion
This paper proposes TGL (Tensor Decomposition Learning Global and Local Structures) to accurately predict missing entries in tensors.
Tensor Completion for Surrogate Modeling of Material Property Prediction
When designing materials to optimize certain properties, there are often many possible configurations of designs that need to be explored.
Automating Data Science Pipelines with Tensor Completion
Hyperparameter optimization is an essential component in many data science pipelines and typically entails exhaustive time and resource-consuming computations in order to explore the combinatorial search space.
TRAWL: Tensor Reduced and Approximated Weights for Large Language Models
Recent research has shown that pruning large-scale language models for inference is an effective approach to improving model efficiency, significantly reducing model weights with minimal impact on performance.
Time-Aware Tensor Decomposition for Missing Entry Prediction
The essential problems of how to exploit the temporal property for tensor decomposition and consider the sparsity of time slices remain unresolved.
