Search Results for author:
Found 6 papers, 5 papers with code
ConstScene: Dataset and Model for Advancing Robust Semantic Segmentation in Construction Environments
The increasing demand for autonomous machines in construction environments necessitates the development of robust object detection algorithms that can perform effectively across various weather and environmental conditions.
Learning Activation Functions for Sparse Neural Networks
By conducting experiments on popular DNN models (LeNet-5, VGG-16, ResNet-18, and EfficientNet-B0) trained on MNIST, CIFAR-10, and ImageNet-16 datasets, we show that the novel combination of these two approaches, dubbed Sparse Activation Function Search, short: SAFS, results in up to 15. 53%, 8. 88%, and 6. 33% absolute improvement in the accuracy for LeNet-5, VGG-16, and ResNet-18 over the default training protocols, especially at high pruning ratios.
Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Search
This paper presents a novel machine learning framework for detecting Paroxysmal Atrial Fibrillation (PxAF), a pathological characteristic of Electrocardiogram (ECG) that can lead to fatal conditions such as heart attack.
3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane Detection
Lane detection is one of the most fundamental tasks for autonomous driving.
DASS: Differentiable Architecture Search for Sparse neural networks
In this paper, we propose a new method to search for sparsity-friendly neural architectures.
TAS: Ternarized Neural Architecture Search for Resource-Constrained Edge Devices
This paper proposes TAS, a framework that drastically reduces the accuracy gap between TNNs and their full-precision counterparts by integrating quantization into the network design.
