Search Results for author:
Found 12 papers, 5 papers with code
Additive Decompositions in Primitive Extensions
This paper extends the classical Ostrogradsky-Hermite reduction for rational functions to more general functions in primitive extensions of certain types.
Symbolic Computation 33F10, 68W30, 12H05
A Self-Correcting Deep Learning Approach to Predict Acute Conditions in Critical Care
Compared with the existing models, our method has a number of distinct features: we utilized the accumulative data of patients in ICU; we developed a self-correcting mechanism that feeds errors from the previous predictions back into the network; we also proposed a regularization method that takes into account not only the model's prediction error on the label but also its estimation errors on the input data.
Segmented convolutional gated recurrent neural networks for human activity recognition in ultra-wideband radar
In this work, we propose a neural network architecture, namely segmented convolutional gated recurrent neural network (SCGRNN), to recognize human activities based on micro-Doppler spectrograms measured by the ultra-wideband radar.
Zoom in to where it matters: a hierarchical graph based model for mammogram analysis
In clinical practice, human radiologists actually review medical images with high resolution monitors and zoom into region of interests (ROIs) for a close-up examination.
An Additive Decomposition in S-Primitive Towers
We consider the additive decomposition problem in primitive towers and present an algorithm to decompose a function in an S-primitive tower as a sum of a derivative in the tower and a remainder which is minimal in some sense.
Symbolic Computation
Cyclic Differentiable Architecture Search
First, the search network generates an initial architecture for evaluation, and the weights of the evaluation network are optimized.
Ranked #17 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-10
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search
The experiments on ImageNet verify such path distillation method can improve the convergence ratio and performance of the hypernetwork, as well as boosting the training of subnetworks.
Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms
In this study, we proposed a multi-task deep graph convolutional network (GCN) method for the automatic characterization of morphology and distribution of microcalcifications in mammograms.
Deep Portrait Lighting Enhancement with 3D Guidance
To address this, we present a novel deep learning framework for portrait lighting enhancement based on 3D facial guidance.
Crossing the Gap: Domain Generalization for Image Captioning
In this paper, we introduce a new setting called Domain Generalization for Image Captioning (DGIC), where the data from the target domain is unseen in the learning process.
Weakly-Supervised 3D Medical Image Segmentation using Geometric Prior and Contrastive Similarity
Furthermore, we propose contrastive similarity to encourage organ pixels to gather around in the contrastive embedding space, which helps better distinguish low-contrast tissues.
Preserving Tumor Volumes for Unsupervised Medical Image Registration
Our approach balances image similarity and volume preservation in different regions, i. e., normal and tumor regions, by using soft tumor masks to adjust the imposition of volume-preserving loss on each one.
