23 citations · 95 across the 11 of their papers we have counts for
11 papers
IA-TIGRIS: An Incremental and Adaptive Sampling-Based Planner for Online Informative Path Planning
Brady Moon, Nayana Suvarna, Andrew Jong +4
Planning paths that maximize information gain for robotic platforms has wide-ranging applications and significant potential impact. To effectively adapt to real-time data collectio…
A Closer Look at Hardware-Friendly Weight Quantization
Sungmin Bae, Piotr Zielinski, Satrajit Chatterjee
Quantizing a Deep Neural Network (DNN) model to be used on a custom accelerator with efficient fixed-point hardware implementations, requires satisfying many stringent hardware-fri…
TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning
Brady Moon, Satrajit Chatterjee, Sebastian Scherer
Informative path planning is an important and challenging problem in robotics that remains to be solved in a manner that allows for wide-spread implementation and real-world practi…
On the Generalization Mystery in Deep Learning
Satrajit Chatterjee, Piotr Zielinski
The generalization mystery in deep learning is the following: Why do over-parameterized neural networks trained with gradient descent (GD) generalize well on real datasets even tho…
Apollo: Transferable Architecture Exploration
Amir Yazdanbakhsh, Christof Angermueller, Berkin Akin +7
The looming end of Moore's Law and ascending use of deep learning drives the design of custom accelerators that are optimized for specific neural architectures. Architecture explor…
Enabling Binary Neural Network Training on the Edge
Erwei Wang, James J. Davis, Daniele Moro +6
The ever-growing computational demands of increasingly complex machine learning models frequently necessitate the use of powerful cloud-based infrastructure for their training. Bin…