Interpretable Multi|Task Deep Neural Networks for Dynamic ...

Interpretable Multi-Task Deep Neural Networks for Dynamic ...

Request PDF | Interpretable Multi-Task Deep Neural Networks for Dynamic Predictions of Postoperative Complications | Accurate prediction of postoperative ...

Dynamic predictions of postoperative complications from ... - Nature

Model interpretability. We applied integrated gradients to our multi-task deep learning postoperative prediction model. The top 10 features per ...

[2004.12551] Dynamic Predictions of Postoperative Complications ...

... Multi-Task Deep Neural Networks. Authors:Benjamin Shickel, Tyler J ... Multi-task learning, interpretability mechanisms, and uncertainty ...

Interpretable multi-task neural network modeling and particle swarm ...

In the field of ML, deep learning (DL) stands out as a particularly remarkable technique, based on neural networks that learn high-level feature representations ...

Convolutional Dynamic Alignment Networks for Interpretable ... - arXiv

We introduce a new family of neural network models called Convolutional Dynamic Alignment Networks (CoDA-Nets), which are performant classifiers with a high ...

Interpretable neural networks: principles and applications - Frontiers

The interpretability of neural networks has now become a research hotspot in the field of deep learning. It covers a wide range of topics in speech and text ...

Biologically interpretable multi-task deep learning pipeline predicts ...

Deep learning models have been developed for various predictions in glioma; yet, they were constrained by manual segmentation, task-specific ...

Dynamic neural networks: advantages and challenges

Dynamic neural networks represent an emerging research focus within deep learning [4]. ... task, spatial-wise dynamic networks can significantly enhance ...

Interpretable neural networks: principles and applications - PMC

“Deep multi-scale convolutional neural network for dynamic scene deblurring,” in Proceedings of the IEEE Conference on Computer Vision and Pattern ...

MultiModN—multimodal, multi-task, interpretable modular networks

Deep multimodal representation learning: A survey. IEEE Access, 2019. Google Scholar. [4]. Arnab Barua. A systematic literature ...

[PDF] Interpretable Deep Neural Networks for Dimensional and ...

QATAR UNIVERSITY COLLEGE OF ENGINEERING STATIC AND DYNAMIC FACIAL EMOTION RECOGNITION USING NEURAL NETWORK MODELS BY EALAF SAYED AHMED HUSSEIN ... Multi-Task ...

Interpretable multi-graph convolution network integrating spatial ...

To achieve high-precision WPF, a deep reinforcement learning algorithm is used to dynamically select the weights for each graph. Overall ...

Interpretable Deep Learning for Time Series Forecasting

TFT is designed to explicitly align the model with the general multi-horizon forecasting task for both superior accuracy and interpretability, ...

Improving Interpretability of Deep Neural Networks With Semantic ...

Figure 2. The attentive encoder-decoder framework for the video captioning task, which can automatically learn interpretable features. We stack a CNN model and ...

Inducing Causal Structure for Interpretable Neural Networks

seemingly opaque and complex network dynamics have an interpretable and faithful abstract structure given by CPVR. ... An overview of multi-task learning in deep ...

Multi-Task Deep Neural Networks for Natural Language ...

In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT- ...

Demystifying Multitask Deep Neural Networks for Quantitative ...

Demystifying Multi-Task Deep Neural Networks. for Quantitative ... Multitask deep learning with dynamic task balancing for quantum mechanical ...

Exploring Interpretable LSTM Neural Networks over Multi-Variable ...

Meanwhile, individual variables typically present different dynamics. This information is implicitly neglected by the hidden states mixing multi-variable data, ...

Adaptive Mask-Based Interpretable Convolutional Neural Network ...

[11,12] proposed multi-task models that can simultaneously handle tasks such as MFI and OSNR, improving efficiency while using additional tasks to constrain the ...

Dynamic-Graph-to-Sequence Interpretable Learning for Health ...

interpretability of the proposed models. Index Terms—deep learning, dynamic graph, sequence predic- tion, health stage prediction. I. INTRODUCTION. Online ...