Search Results for author:
Found 14 papers, 9 papers with code
Learning with Recoverable Forgetting
Life-long learning aims at learning a sequence of tasks without forgetting the previously acquired knowledge.
Factorizing Knowledge in Neural Networks
The core idea of KF lies in the modularization and assemblability of knowledge: given a pretrained network model as input, KF aims to decompose it into several factor networks, each of which handles only a dedicated task and maintains task-specific knowledge factorized from the source network.
Neural Point Process for Learning Spatiotemporal Event Dynamics
The key construction of our approach is the nonparametric space-time intensity function, governed by a latent process.
On the Generation of Medical Dialogs for COVID-19
Training complex dialog generation models on small datasets bears high risk of overfitting.
Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation
Medical report generation is one of the most challenging tasks in medical image analysis.
Neural Point Process for Forecasting Spatiotemporal Events
To the best of our knowledge, this is the first neural point process model that can jointly predict both the space and time of events.
Stochastic Gradient Variance Reduction by Solving a Filtering Problem
Deep neural networks (DNN) are typically optimized using stochastic gradient descent (SGD).
DSRNA: Differentiable Search of Robust Neural Architectures
To address this problem, we propose methods to perform differentiable search of robust neural architectures.
Single View Metrology in the Wild
Most 3D reconstruction methods may only recover scene properties up to a global scale ambiguity.
Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining Paradigms
There has not been a clear understanding on what properties of data and tasks render one approach outperforms the other.
XRayGAN: Consistency-preserving Generation of X-ray Images from Radiology Reports
To address this problem, we develop methods to generate view-consistent, high-fidelity, and high-resolution X-ray images from radiology reports to facilitate radiology training of medical students.
On the Generation of Medical Dialogues for COVID-19
On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT.
Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans
Besides, these works require a large number of CTs to train accurate diagnosis models, which are difficult to obtain.
COVID-CT-Dataset: A CT Scan Dataset about COVID-19
Using this dataset, we develop diagnosis methods based on multi-task learning and self-supervised learning, that achieve an F1 of 0. 90, an AUC of 0. 98, and an accuracy of 0. 89.
