47 citations · 111 across the 9 of their papers we have counts for
10 papers
Generative Model-based Simulation of Driver Behavior when Using Control Input Interface for Teleoperated Driving in Unstructured Canyon Terrains
Hyeonggeun Yun, Younggeol Cho, Jinwon Lee +2
Unmanned ground vehicles (UGVs) in unstructured environments mostly operate through teleoperation. To enable stable teleoperated driving in unstructured environments, some research…
Machining feature recognition using descriptors with range constraints for mechanical 3D models
Seungeun Lim, Changmo Yeo, Fazhi He +2
In machining feature recognition, geometric elements generated in a three-dimensional computer-aided design model are identified. This technique is used in manufacturability evalua…
GoonDAE: Denoising-Based Driver Assistance for Off-Road Teleoperation
Younggeol Cho, Hyeonggeun Yun, Jinwon Lee +2
Because of the limitations of autonomous driving technologies, teleoperation is widely used in dangerous environments such as military operations. However, the teleoperated driving…
LSQ+: Improving low-bit quantization through learnable offsets and better initialization
Yash Bhalgat, Jinwon Lee, Markus Nagel +2
Unlike ReLU, newer activation functions (like Swish, H-swish, Mish) that are frequently employed in popular efficient architectures can also result in negative activation values, w…
Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices
Byung Hoon Ahn, Jinwon Lee, Jamie Menjay Lin +3
Recent advances demonstrate that irregularly wired neural networks from Neural Architecture Search (NAS) and Random Wiring can not only automate the design of deep neural networks…
Learned Threshold Pruning
Kambiz Azarian, Yash Bhalgat, Jinwon Lee +1
This paper presents a novel differentiable method for unstructured weight pruning of deep neural networks. Our learned-threshold pruning (LTP) method learns per-layer thresholds vi…
QKD: Quantization-aware Knowledge Distillation
Jangho Kim, Yash Bhalgat, Jinwon Lee +2
Quantization and Knowledge distillation (KD) methods are widely used to reduce memory and power consumption of deep neural networks (DNNs), especially for resource-constrained edge…
Accelerated Training for CNN Distributed Deep Learning through Automatic Resource-Aware Layer Placement
Jay H. Park, Sunghwan Kim, Jinwon Lee +2
The Convolutional Neural Network (CNN) model, often used for image classification, requires significant training time to obtain high accuracy. To this end, distributed training is…
Automatic Grammar Augmentation for Robust Voice Command Recognition
Yang Yang, Anusha Lalitha, Jinwon Lee +1
This paper proposes a novel pipeline for automatic grammar augmentation that provides a significant improvement in the voice command recognition accuracy for systems with small foo…
A Fully Convolutional Neural Network for Speech Enhancement
Se Rim Park, Jinwon Lee
In hearing aids, the presence of babble noise degrades hearing intelligibility of human speech greatly. However, removing the babble without creating artifacts in human speech is a…