Being able to automatically detect and track surgical instruments in endoscopic video recordings would allow for many useful applications that could transform different aspects of surgery.
Depth sensing is a crucial function of unmanned aerial vehicles and autonomous vehicles.
Power line detection is a critical inspection task for electricity companies and is also useful in avoiding drone obstacles.
We first argue that the MSE minimization approach is equivalent to a conditional entropy learning problem, and then propose a mutual information learning formulation for solving regression problems by using a reparameterization technique.
Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks.
We first propose a diffusion-based generative model to tackle this problem by generating voxelized shapes with close-to-reality outlines and structures.
Deep learning systems have been reported to acheive state-of-the-art performances in many applications, and one of the keys for achieving this is the existence of well trained classifiers on benchmark datasets which can be used as backbone feature extractors in downstream tasks.
Deep learning systems have been reported to achieve state-of-the-art performances in many applications, and a key is the existence of well trained classifiers on benchmark datasets.
In the proposed ensemble averaging method, multiple models are independently trained and model predictions are averaged at each time step.
Matrix multiplication is the bedrock in Deep Learning inference application.
To address this problem, we propose a personalized retrogress-resilient framework to produce a superior personalized model for each client.
Consequently, a trained DNN defines a predictive model for the underlying unknown PDE over structureless grids.
While considerable multi-task efforts have been made in this direction, a long-standing challenge is how to explicitly model the long-path sequential dependence among audience multi-step conversions for improving the end-to-end conversion.
As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations for participants.
To circumvent the difficulty presented by the non-autonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances.
The overdetermination of the mathematical problem underlying ptychography is reduced by a host of experimentally more desirable settings.
Various numerical examples are then presented to demonstrate the performance and properties of the numerical methods.
When an existing coarse model is not available, we present numerical strategies for fast creation of coarse models, to be used in conjunction with the generalized ResNet.
To tackle these issues, we propose a novel complementary network with adaptive receptive filed learning.
In this work, we present a deep learning framework for multi-class breast cancer image classification as our submission to the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology images (BACH).