31 citations · 85 across the 18 of their papers we have counts for
24 papers
Latent Graph Attention for Enhanced Spatial Context
Ayush Singh, Yash Bhambhu, Himanshu Buckchash +2
Global contexts in images are quite valuable in image-to-image translation problems. Conventional attention-based and graph-based models capture the global context to a large exten…
One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning
Arnav Chavan, Zhuang Liu, Deepak Gupta +2
We present Generalized LoRA (GLoRA), an advanced approach for universal parameter-efficient fine-tuning tasks. Enhancing Low-Rank Adaptation (LoRA), GLoRA employs a generalized pro…
Data-Efficient Training of CNNs and Transformers with Coresets: A Stability Perspective
Animesh Gupta, Irtiza Hasan, Dilip K. Prasad +1
Coreset selection is among the most effective ways to reduce the training time of CNNs, however, only limited is known on how the resultant models will behave under variations of t…
Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts
Rohit Agarwal, Deepak Gupta, Alexander Horsch +1
Many real-world applications based on online learning produce streaming data that is haphazard in nature, i.e., contains missing features, features becoming obsolete in time, the a…
Patch Gradient Descent: Training Neural Networks on Very Large Images
Deepak K. Gupta, Gowreesh Mago, Arnav Chavan +1
Traditional CNN models are trained and tested on relatively low resolution images (<300 px), and cannot be directly operated on large-scale images due to compute and memory constra…
On Using Deep Learning Proxies as Forward Models in Deep Learning Problems
Fatima Albreiki, Nidhal Belayouni, Deepak K. Gupta
Physics-based optimization problems are generally very time-consuming, especially due to the computational complexity associated with the forward model. Recent works have demonstra…