A Neural Network Based Method with Transfer Learning for Genetic Data
Analysis
- URL: http://arxiv.org/abs/2206.09872v1
- Date: Mon, 20 Jun 2022 16:16:05 GMT
- Title: A Neural Network Based Method with Transfer Learning for Genetic Data
Analysis
- Authors: Jinghang Lin, Shan Zhang, Qing Lu
- Abstract summary: We combine transfer learning technique with a neural network based method(expectile neural networks)
We leverage previous learnings and avoid starting from scratch to improve the model performance.
By using transfer learning algorithm, the performance of expectile neural networks is improved compared to expectile neural network without using transfer learning technique.
- Score: 3.8599966694228667
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Transfer learning has emerged as a powerful technique in many application
problems, such as computer vision and natural language processing. However,
this technique is largely ignored in application to genetic data analysis. In
this paper, we combine transfer learning technique with a neural network based
method(expectile neural networks). With transfer learning, instead of starting
the learning process from scratch, we start from one task that have been
learned when solving a different task. We leverage previous learnings and avoid
starting from scratch to improve the model performance by passing information
gained in different but related task. To demonstrate the performance, we run
two real data sets. By using transfer learning algorithm, the performance of
expectile neural networks is improved compared to expectile neural network
without using transfer learning technique.
Related papers
- Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
We propose to represent neural networks as computational graphs of parameters.
Our approach enables a single model to encode neural computational graphs with diverse architectures.
We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
arXiv Detail & Related papers (2024-03-18T18:01:01Z) - Simple and Effective Transfer Learning for Neuro-Symbolic Integration [50.592338727912946]
A potential solution to this issue is Neuro-Symbolic Integration (NeSy), where neural approaches are combined with symbolic reasoning.
Most of these methods exploit a neural network to map perceptions to symbols and a logical reasoner to predict the output of the downstream task.
They suffer from several issues, including slow convergence, learning difficulties with complex perception tasks, and convergence to local minima.
This paper proposes a simple yet effective method to ameliorate these problems.
arXiv Detail & Related papers (2024-02-21T15:51:01Z) - A Novel Method for improving accuracy in neural network by reinstating
traditional back propagation technique [0.0]
We propose a novel instant parameter update methodology that eliminates the need for computing gradients at each layer.
Our approach accelerates learning, avoids the vanishing gradient problem, and outperforms state-of-the-art methods on benchmark data sets.
arXiv Detail & Related papers (2023-08-09T16:41:00Z) - Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs.
By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
arXiv Detail & Related papers (2022-02-01T17:11:13Z) - Being Friends Instead of Adversaries: Deep Networks Learn from Data
Simplified by Other Networks [23.886422706697882]
A different idea has been recently proposed, named Friendly Training, which consists in altering the input data by adding an automatically estimated perturbation.
We revisit and extend this idea inspired by the effectiveness of neural generators in the context of Adversarial Machine Learning.
We propose an auxiliary multi-layer network that is responsible of altering the input data to make them easier to be handled by the classifier.
arXiv Detail & Related papers (2021-12-18T16:59:35Z) - Neuronal Learning Analysis using Cycle-Consistent Adversarial Networks [4.874780144224057]
We use a variant of deep generative models called - CycleGAN, to learn the unknown mapping between pre- and post-learning neural activities.
We develop an end-to-end pipeline to preprocess, train and evaluate calcium fluorescence signals, and a procedure to interpret the resulting deep learning models.
arXiv Detail & Related papers (2021-11-25T13:24:19Z) - Dynamic Neural Diversification: Path to Computationally Sustainable
Neural Networks [68.8204255655161]
Small neural networks with a constrained number of trainable parameters, can be suitable resource-efficient candidates for many simple tasks.
We explore the diversity of the neurons within the hidden layer during the learning process.
We analyze how the diversity of the neurons affects predictions of the model.
arXiv Detail & Related papers (2021-09-20T15:12:16Z) - Incremental Embedding Learning via Zero-Shot Translation [65.94349068508863]
Current state-of-the-art incremental learning methods tackle catastrophic forgetting problem in traditional classification networks.
We propose a novel class-incremental method for embedding network, named as zero-shot translation class-incremental method (ZSTCI)
In addition, ZSTCI can easily be combined with existing regularization-based incremental learning methods to further improve performance of embedding networks.
arXiv Detail & Related papers (2020-12-31T08:21:37Z) - Malicious Network Traffic Detection via Deep Learning: An Information
Theoretic View [0.0]
We study how homeomorphism affects learned representation of a malware traffic dataset.
Our results suggest that although the details of learned representations and the specific coordinate system defined over the manifold of all parameters differ slightly, the functional approximations are the same.
arXiv Detail & Related papers (2020-09-16T15:37:44Z) - Curriculum By Smoothing [52.08553521577014]
Convolutional Neural Networks (CNNs) have shown impressive performance in computer vision tasks such as image classification, detection, and segmentation.
We propose an elegant curriculum based scheme that smoothes the feature embedding of a CNN using anti-aliasing or low-pass filters.
As the amount of information in the feature maps increases during training, the network is able to progressively learn better representations of the data.
arXiv Detail & Related papers (2020-03-03T07:27:44Z) - Inter- and Intra-domain Knowledge Transfer for Related Tasks in Deep
Character Recognition [2.320417845168326]
Pre-training a deep neural network on the ImageNet dataset is a common practice for training deep learning models.
The technique of pre-training on one task and then retraining on a new one is called transfer learning.
In this paper we analyse the effectiveness of using deep transfer learning for character recognition tasks.
arXiv Detail & Related papers (2020-01-02T14:18:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.