Search Results for author:
Found 9 papers, 4 papers with code
E2V-SDE: From Asynchronous Events to Fast and Continuous Video Reconstruction via Neural Stochastic Differential Equations
In terms of image quality, the LPIPS score improves by up to 12% and the reconstruction speed is 87% higher than that of ET-Net.
Demystifying the Neural Tangent Kernel from a Practical Perspective: Can it be trusted for Neural Architecture Search without training?
To take such non-linear characteristics into account, we introduce Label-Gradient Alignment (LGA), a novel NTK-based metric whose inherent formulation allows it to capture the large amount of non-linear advantage present in modern neural architectures.
AutoSNN: Towards Energy-Efficient Spiking Neural Networks
We investigate the design choices used in the previous studies in terms of the accuracy and number of spikes and figure out that they are not best-suited for SNNs.
Scalable Smartphone Cluster for Deep Learning
We implemented large-batch synchronous training of DNNs based on Caffe, a deep learning library.
AdvRush: Searching for Adversarially Robust Neural Architectures
Current efforts to improve the robustness of neural networks against adversarial examples are focused on developing robust training methods, which update the weights of a neural network in a more robust direction.
Accelerating Neural Architecture Search via Proxy Data
By analyzing proxy data constructed using various selection methods through data entropy, we propose a novel proxy data selection method tailored for NAS.
T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding
Spiking neural networks (SNNs) have gained considerable interest due to their energy-efficient characteristics, yet lack of a scalable training algorithm has restricted their applicability in practical machine learning problems.
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection
Over the past decade, deep neural networks (DNNs) have demonstrated remarkable performance in a variety of applications.
DNA-Level Splice Junction Prediction using Deep Recurrent Neural Networks
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 intron.
