Search Results for author:
Found 16 papers, 10 papers with code
Perceptual Generative Autoencoders
We therefore propose to map both the generated and target distributions to a latent space using the encoder of a standard autoencoder, and train the generator (or decoder) to match the target distribution in the latent space.
Your time series is worth a binary image: machine vision assisted deep framework for time series forecasting
To address this challenge, this paper proposes a novel machine vision assisted deep time series analysis (MV-DTSA) framework.
Explainable Multilayer Graph Neural Network for Cancer Gene Prediction
Unlike conventional graph learning on a single biological network, EMGNN uses a multilayered graph neural network to learn from multiple biological networks for accurate cancer gene prediction.
Normalized Direction-preserving Adam
Adaptive optimization algorithms, such as Adam and RMSprop, have shown better optimization performance than stochastic gradient descent (SGD) in some scenarios.
Generative Wind Power Curve Modeling Via Machine Vision: A Self-learning Deep Convolutional Network Based Method
First, different from traditional studies regarding the WPC modeling as a curve fitting problem, in this paper, we renovate the WPC modeling formulation from a machine vision aspect.
Removing the Feature Correlation Effect of Multiplicative Noise
Multiplicative noise, including dropout, is widely used to regularize deep neural networks (DNNs), and is shown to be effective in a wide range of architectures and tasks.
Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactions
Creating predictive and interpretable models for these pathways is challenging because of the complexity of the pathways and of the cellular and genomic contexts.
Efficient and Scalable Fine-Tune of Language Models for Genome Understanding
Impressively, applying these adapters on natural language foundation models matched or even exceeded the performance of DNA foundation models.
Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge
We report a neural architecture search framework, BioNAS, that is tailored for biomedical researchers to easily build, evaluate, and uncover novel knowledge from interpretable deep learning models.
Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures
This suggests that different neural architectures are sensitive to different aspects of the data, an important yet under-explored challenge for clinical prediction tasks.
PSTN: Periodic Spatial-temporal Deep Neural Network for Traffic Condition Prediction
Accurate forecasting of traffic conditions is critical for improving safety, stability, and efficiency of a city transportation system.
Intelligent Railway Foreign Object Detection: A Semi-supervised Convolutional Autoencoder Based Method
Most existing vision-based approaches focus on the detection of frontal intrusion objects with prior labels, such as categories and locations of the objects.
Automatic Rail Component Detection Based on AttnConv-Net
The automatic detection of major rail components using railway images is beneficial to ensure the rail transport safety.
Rubik's Cube Operator: A Plug And Play Permutation Module for Better Arranging High Dimensional Industrial Data in Deep Convolutional Processes
The convolutional neural network (CNN) has been widely applied to process the industrial data based tensor input, which integrates data records of distributed industrial systems from the spatial, temporal, and system dynamics aspects.
ProPath: Disease-Specific Protein Language Model for Variant Pathogenicity
Clinical variant classification of pathogenic versus benign genetic variants remains a pivotal challenge in clinical genetics.
Towards Robust Graph Incremental Learning on Evolving Graphs
We provide a formal formulation and analysis of the problem, and propose a novel regularization-based technique called Structural-Shift-Risk-Mitigation (SSRM) to mitigate the impact of the structural shift on catastrophic forgetting of the inductive NGIL problem.
