Distance metric learning (DML) is to learn a representation space equipped with a metric, such that examples from the same class are closer than examples from different classes with respect to the metric.
Graph data augmentation has proven to be effective in enhancing the generalizability and robustness of graph neural networks (GNNs) for graph-level classifications.
To explore the ultimate limits of the approach, we derived the Cramer-Rao lower bound of the measurement, showing that RNN yields lifetime estimations with near-optimal precision.
The empirical analysis is developed in four aspects: algorithm, perception and decision granularity, hyperparameters, and reward function.
RL has received continuous attention and research, but there is still a lack of systematic analysis of the design elements of RL-based EMS.
no code implementations • 20 Apr 2022 • Kelly Payette, Hongwei Li, Priscille de Dumast, Roxane Licandro, Hui Ji, Md Mahfuzur Rahman Siddiquee, Daguang Xu, Andriy Myronenko, Hao liu, Yuchen Pei, Lisheng Wang, Ying Peng, Juanying Xie, Huiquan Zhang, Guiming Dong, Hao Fu, Guotai Wang, ZunHyan Rieu, Donghyeon Kim, Hyun Gi Kim, Davood Karimi, Ali Gholipour, Helena R. Torres, Bruno Oliveira, João L. Vilaça, Yang Lin, Netanell Avisdris, Ori Ben-Zvi, Dafna Ben Bashat, Lucas Fidon, Michael Aertsen, Tom Vercauteren, Daniel Sobotka, Georg Langs, Mireia Alenyà, Maria Inmaculada Villanueva, Oscar Camara, Bella Specktor Fadida, Leo Joskowicz, Liao Weibin, Lv Yi, Li Xuesong, Moona Mazher, Abdul Qayyum, Domenec Puig, Hamza Kebiri, Zelin Zhang, Xinyi Xu, Dan Wu, Kuanlun Liao, Yixuan Wu, Jintai Chen, Yunzhi Xu, Li Zhao, Lana Vasung, Bjoern Menze, Meritxell Bach Cuadra, Andras Jakab
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
In recent years, deep network-based methods have continuously refreshed state-of-the-art performance on Salient Object Detection (SOD) task.
In this paper, we present SSDNet, a novel deep learning approach for time series forecasting.
Network quantization significantly reduces model inference complexity and has been widely used in real-world deployments.
Ranked #1 on Quantization on ImageNet
TCAN requires less number of convolutional layers than TCNN for an extended receptive field, is faster to train and is able to visualize the most important timesteps for the prediction.
Based on this significant discovery and the proposed strategy, we introduce a scalable symmetric discrete hashing algorithm that gradually and smoothly updates each batch of binary codes.
However, the shallow models leveraging bilinear forms suffer from limitations on capturing complicated nonlinear interactions between drug pairs.
no code implementations • 20 Sep 2018 • Wang Shidan, Wang Tao, Yang Lin, Yi Faliu, Luo Xin, Yang Yikun, Gazdar Adi, Fujimoto Junya, Wistuba Ignacio I., Yao Bo, Lin ShinYi, Xie Yang, Mao Yousheng, Xiao Guanghua
By identifying cells and classifying cell types, this pipeline can convert a pathology image into a spatial map of tumor, stromal and lymphocyte cells.
In the experiments, we first evaluate performance of the proposed detection module on UDID and its deformed variations.