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Found 9 papers, 2 papers with code
Resource-constrained knowledge diffusion processes inspired by human peer learning
We consider a setting where a population of artificial learners is given, and the objective is to optimize aggregate measures of performance, under constraints on training resources.
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
Neural Network Pruning as Spectrum Preserving Process
In this paper, we identify the close connection between matrix spectrum learning and neural network training for dense and convolutional layers and argue that weight pruning is essentially a matrix sparsification process to preserve the spectrum.
K-SpecPart: Supervised embedding algorithms and cut overlay for improved hypergraph partitioning
State-of-the-art hypergraph partitioners follow the multilevel paradigm that constructs multiple levels of progressively coarser hypergraphs that are used to drive cut refinement on each level of the hierarchy.
SGC: A semi-supervised pipeline for gene clustering using self-training approach in gene co-expression networks
A widely used approach for extracting information from gene expression data employ the construction of a gene co-expression network and the subsequent application of algorithms that discover network structure.
Neurally boosted supervised spectral clustering
Spectral network embeddings are based on the computation of eigenvectors of a normalized graph Laplacian.
Spectral Modification of Graphs for Improved Spectral Clustering
Applying then spectral clustering on $H$ has the potential to produce improved cuts that also exist in $G$ due to the cut similarity.
Improved large-scale graph learning through ridge spectral sparsification
By constructing a spectrally-similar graph, we are able to bound the error induced by the sparsification for a variety of downstream tasks (e. g., SSL).
Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning
While the harmonic function solution performs well in many semi-supervised learning (SSL) tasks, it is known to scale poorly with the number of samples.
