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Found 7 papers, 3 papers with code
SLATE: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content
We present SLATE, a sequence labeling approach for extracting tasks from free-form content such as digitally handwritten (or "inked") notes on a virtual whiteboard.
Only Train Once: A One-Shot Neural Network Training And Pruning Framework
Structured pruning is a commonly used technique in deploying deep neural networks (DNNs) onto resource-constrained devices.
A Half-Space Stochastic Projected Gradient Method for Group Sparsity Regularization
Optimizing with group sparsity is significant in enhancing model interpretability in machining learning applications, e. g., feature selection, compressed sensing and model compression.
Neural Network Compression Via Sparse Optimization
The compression of deep neural networks (DNNs) to reduce inference cost becomes increasingly important to meet realistic deployment requirements of various applications.
Orthant Based Proximal Stochastic Gradient Method for $\ell_1$-Regularized Optimization
Sparsity-inducing regularization problems are ubiquitous in machine learning applications, ranging from feature selection to model compression.
Realizing Pixel-Level Semantic Learning in Complex Driving Scenes based on Only One Annotated Pixel per Class
Semantic segmentation tasks based on weakly supervised condition have been put forward to achieve a lightweight labeling process.
WSOD with PSNet and Box Regression
To solve this problem, we added the box regression module to the weakly supervised object detection network and proposed a proposal scoring network (PSNet) to supervise it.
Ranked #16 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
