2 citations · 2 across the 4 of their papers we have counts for
5 papers
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…
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,…
Rethinking the Relationship between Recurrent and Non-Recurrent Neural Networks: A Study in Sparsity
Quincy Hershey, Randy Paffenroth, Harsh Pathak +1
Neural networks (NN) can be divided into two broad categories, recurrent and non-recurrent. Both types of neural networks are popular and extensively studied, but they are often tr…
Towards Learning and Explaining Indirect Causal Effects in Neural Networks
Abbavaram Gowtham Reddy, Saketh Bachu, Harsharaj Pathak +4
Recently, there has been a growing interest in learning and explaining causal effects within Neural Network (NN) models. By virtue of NN architectures, previous approaches consider…
Efficient Super Resolution For Large-Scale Images Using Attentional GAN
Harsh Nilesh Pathak, Xinxin Li, Shervin Minaee +1
Single Image Super Resolution (SISR) is a well-researched problem with broad commercial relevance. However, most of the SISR literature focuses on small-size images under 500px, wh…