Abstract: A computer implemented method obtains neural network-based model base model weight matrices for each of multiple neural network layers. First low-rank factorization matrices are added to corresponding base model weight matrices to form a first domain model. The low-rank factorization matrices are treated as trainable parameters. The first domain model is trained with first domain specific training data without modifying base model weight matrices.

Abstract: In some examples, iterative sampling based dataset clustering may include sampling a dataset that includes a plurality of items to identify a specified number of sampled items. The sampled items may be clustered to generate a plurality of clusters. Un-sampled items may be assigned from the plurality of items to the clusters. Remaining un-sampled items that are not assigned to the clusters may be identified. A ratio associated with the remaining un-sampled items and the plurality of items may be compared to a specified threshold. Based on a determination that the ratio is greater than the specified threshold, an indication of completion of clustering of the plurality of items may be generated.