48 citations · 120 across the 9 of their papers we have counts for
10 papers
E2V-SDE: From Asynchronous Events to Fast and Continuous Video Reconstruction via Neural Stochastic Differential Equations
Jongwan Kim, DongJin Lee, Byunggook Na +3
Event cameras respond to brightness changes in the scene asynchronously and independently for every pixel. Due to the properties, these cameras have distinct features: high dynamic…
Demystifying the Neural Tangent Kernel from a Practical Perspective: Can it be trusted for Neural Architecture Search without training?
Jisoo Mok, Byunggook Na, Ji-Hoon Kim +2
In Neural Architecture Search (NAS), reducing the cost of architecture evaluation remains one of the most crucial challenges. Among a plethora of efforts to bypass training of each…
AutoSNN: Towards Energy-Efficient Spiking Neural Networks
Byunggook Na, Jisoo Mok, Seongsik Park +3
Spiking neural networks (SNNs) that mimic information transmission in the brain can energy-efficiently process spatio-temporal information through discrete and sparse spikes, there…
Scalable Smartphone Cluster for Deep Learning
Byunggook Na, Jaehee Jang, Seongsik Park +7
Various deep learning applications on smartphones have been rapidly rising, but training deep neural networks (DNNs) has too large computational burden to be executed on a single s…
AdvRush: Searching for Adversarially Robust Neural Architectures
Jisoo Mok, Byunggook Na, Hyeokjun Choe +1
Deep neural networks continue to awe the world with their remarkable performance. Their predictions, however, are prone to be corrupted by adversarial examples that are imperceptib…
Accelerating Neural Architecture Search via Proxy Data
Byunggook Na, Jisoo Mok, Hyeokjun Choe +1
Despite the increasing interest in neural architecture search (NAS), the significant computational cost of NAS is a hindrance to researchers. Hence, we propose to reduce the cost o…
T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding
Seongsik Park, Seijoon Kim, Byunggook Na +1
Spiking neural networks (SNNs) have gained considerable interest due to their energy-efficient characteristics, yet lack of a scalable training algorithm has restricted their appli…
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection
Seijoon Kim, Seongsik Park, Byunggook Na +1
Over the past decade, deep neural networks (DNNs) have demonstrated remarkable performance in a variety of applications. As we try to solve more advanced problems, increasing deman…
Homomorphic Parameter Compression for Distributed Deep Learning Training
Jaehee Jang, Byungook Na, Sungroh Yoon
Distributed training of deep neural networks has received significant research interest, and its major approaches include implementations on multiple GPUs and clusters. Paralleliza…
DNA-Level Splice Junction Prediction using Deep Recurrent Neural Networks
Byunghan Lee, Taehoon Lee, Byunggook Na +1
A eukaryotic gene consists of multiple exons (protein coding regions) and introns (non-coding regions), and a splice junction refers to the boundary between a pair of exon and intr…