3 papers
cs.LG2025
Balancing Sparse RNNs with Hyperparameterization Benefiting Meta-Learning
Quincy Hershey, Randy Paffenroth
This paper develops alternative hyperparameters for specifying sparse Recurrent Neural Networks (RNNs). These hyperparameters allow for varying sparsity within the trainable weight…
cs.LG2025
Principled Curriculum Learning using Parameter Continuation Methods
Harsh Nilesh Pathak, Randy Paffenroth
In this work, we propose a parameter continuation method for the optimization of neural networks. There is a close connection between parameter continuation, homotopies, and curric…
cs.LG2025
Solo Connection: A Parameter Efficient Fine-Tuning Technique for Transformers
Harsh Nilesh Pathak, Randy Paffenroth
Parameter efficient fine tuning (PEFT) is a versatile and extensible approach for adapting a Large Language Model (LLM) for newer tasks. One of the most prominent PEFT approaches,…