45 citations · 63 across the 11 of their papers we have counts for
11 papers
End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver
Shaocong Ma, James Diffenderfer, Bhavya Kailkhura +1
Deep learning has been widely applied to solve partial differential equations (PDEs) in computational fluid dynamics. Recent research proposed a PDE correction framework that lever…
Pursing the Sparse Limitation of Spiking Deep Learning Structures
Hao Cheng, Jiahang Cao, Erjia Xiao +7
Spiking Neural Networks (SNNs), a novel brain-inspired algorithm, are garnering increased attention for their superior computation and energy efficiency over traditional artificial…
Instance-wise Linearization of Neural Network for Model Interpretation
Zhimin Li, Shusen Liu, Kailkhura Bhavya +2
Neural network have achieved remarkable successes in many scientific fields. However, the interpretability of the neural network model is still a major bottlenecks to deploy such t…
NEFTune: Noisy Embeddings Improve Instruction Finetuning
Neel Jain, Ping-yeh Chiang, Yuxin Wen +10
We show that language model finetuning can be improved, sometimes dramatically, with a simple augmentation. NEFTune adds noise to the embedding vectors during training. Standard fi…
On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization
Akshay Mehra, Yunbei Zhang, Bhavya Kailkhura +1
Achieving high accuracy on data from domains unseen during training is a fundamental challenge in domain generalization (DG). While state-of-the-art DG classifiers have demonstrate…
Less is More: Data Pruning for Faster Adversarial Training
Yize Li, Pu Zhao, Xue Lin +2
Deep neural networks (DNNs) are sensitive to adversarial examples, resulting in fragile and unreliable performance in the real world. Although adversarial training (AT) is currentl…