Search Results for author:
Found 9 papers, 3 papers with code
Denoising of 3D MR images using a voxel-wise hybrid residual MLP-CNN model to improve small lesion diagnostic confidence
We evaluate the proposed method on 720 T2-FLAIR brain images with small lesions at different noise levels.
DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation
Our method jointly learns the treatment and response functions in the entire sample space to avoid treatment bias and employs an intermediate pseudo treatment effect prediction network to relieve sample imbalance.
Multi-core fiber enabled fading noise suppression in φ-OFDR based quantitative distributed vibration sensing
Coherent fading has been regarded as a critical issue in phase-sensitive optical frequency domain reflectometry ({\phi}-OFDR) based distributed fiber-optic sensing.
MQBench: Towards Reproducible and Deployable Model Quantization Benchmark
Surprisingly, no existing algorithm wins every challenge in MQBench, and we hope this work could inspire future research directions.
One Model for All Quantization: A Quantized Network Supporting Hot-Swap Bit-Width Adjustment
When the precision of quantization is adjusted, it is necessary to fine-tune the quantized model or minimize the quantization noise, which brings inconvenience in practical applications.
MWQ: Multiscale Wavelet Quantized Neural Networks
Inspired by the characteristics of images in the frequency domain, we propose a novel multiscale wavelet quantization (MWQ) method.
Effective and Fast: A Novel Sequential Single Path Search for Mixed-Precision Quantization
Since model quantization helps to reduce the model size and computation latency, it has been successfully applied in many applications of mobile phones, embedded devices and smart chips.
Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1
The training of deep neural networks (DNNs) requires intensive resources both for computation and for storage performance.
ASVRG: Accelerated Proximal SVRG
This paper proposes an accelerated proximal stochastic variance reduced gradient (ASVRG) method, in which we design a simple and effective momentum acceleration trick.
