4 citations · 9 across the 6 of their papers we have counts for
7 papers
Data Scaling Laws for End-to-End Autonomous Driving
Alexander Naumann, Xunjiang Gu, Tolga Dimlioglu +7
Autonomous vehicle (AV) stacks have traditionally relied on decomposed approaches, with separate modules handling perception, prediction, and planning. However, this design introdu…
ERASE-Net: Efficient Segmentation Networks for Automotive Radar Signals
Shihong Fang, Haoran Zhu, Devansh Bisla +4
Among various sensors for assisted and autonomous driving systems, automotive radar has been considered as a robust and low-cost solution even in adverse weather or lighting condit…
Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape
Devansh Bisla, Jing Wang, Anna Choromanska
In this paper, we study the sharpness of a deep learning (DL) loss landscape around local minima in order to reveal systematic mechanisms underlying the generalization abilities of…
ESAFE: Enterprise Security and Forensics at Scale
Bernard McShea, Kevin Wright, Denley Lam +7
Securing enterprise networks presents challenges in terms of both their size and distributed structure. Data required to detect and characterize malicious activities may be diffuse…
A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs
Devansh Bisla, Apoorva Nandini Saridena, Anna Choromanska
This paper focuses on understanding how the generalization error scales with the amount of the training data for deep neural networks (DNNs). Existing techniques in statistical lea…
Towards Automated Melanoma Detection with Deep Learning: Data Purification and Augmentation
Devansh Bisla, Anna Choromanska, Jennifer A. Stein +2
Melanoma is one of the ten most common cancers in the US. Early detection is crucial for survival, but often the cancer is diagnosed in the fatal stage. Deep learning has the poten…