43 citations · 84 across the 10 of their papers we have counts for
10 papers
FSCNN: A Fast Sparse Convolution Neural Network Inference System
Bo Ji, Tianyi Chen
Convolution neural networks (CNNs) have achieved remarkable success, but typically accompany high computation cost and numerous redundant weight parameters. To reduce the FLOPs, st…
Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution
Yuehan Zhang, Bo Ji, Jia Hao +1
In image super-resolution, both pixel-wise accuracy and perceptual fidelity are desirable. However, most deep learning methods only achieve high performance in one aspect due to th…
A Closer Look at Branch Classifiers of Multi-exit Architectures
Shaohui Lin, Bo Ji, Rongrong Ji +1
Multi-exit architectures consist of a backbone and branch classifiers that offer shortened inference pathways to reduce the run-time of deep neural networks. In this paper, we anal…
On Kernelized Multi-Armed Bandits with Constraints
Xingyu Zhou, Bo Ji
We study a stochastic bandit problem with a general unknown reward function and a general unknown constraint function. Both functions can be non-linear (even non-convex) and are as…
FEDI: Few-shot learning based on Earth Mover's Distance algorithm combined with deep residual network to identify diabetic retinopathy
Liangrui Pan, Boya Ji, Peng Xi +3
Diabetic retinopathy(DR) is the main cause of blindness in diabetic patients. However, DR can easily delay the occurrence of blindness through the diagnosis of the fundus. In view…
Only Train Once: A One-Shot Neural Network Training And Pruning Framework
Tianyi Chen, Bo Ji, Tianyu Ding +7
Structured pruning is a commonly used technique in deploying deep neural networks (DNNs) onto resource-constrained devices. However, the existing pruning methods are usually heuris…
Neural Network Compression Via Sparse Optimization
Tianyi Chen, Bo Ji, Yixin Shi +4
The compression of deep neural networks (DNNs) to reduce inference cost becomes increasingly important to meet realistic deployment requirements of various applications. There have…
Half-Space Proximal Stochastic Gradient Method for Group-Sparsity Regularized Problem
Tianyi Chen, Guanyi Wang, Tianyu Ding +3
Optimizing with group sparsity is significant in enhancing model interpretability in machining learning applications, e.g., feature selection, compressed sensing and model compress…
Orthant Based Proximal Stochastic Gradient Method for -Regularized Optimization
Tianyi Chen, Tianyu Ding, Bo Ji +6
Sparsity-inducing regularization problems are ubiquitous in machine learning applications, ranging from feature selection to model compression. In this paper, we present a novel st…
Generative Adversarial Network for Handwritten Text
Bo Ji, Tianyi Chen
Generative adversarial networks (GANs) have proven hugely successful in variety of applications of image processing. However, generative adversarial networks for handwriting is rel…