Can neural networks count digit frequency?
- URL: http://arxiv.org/abs/2310.04431v1
- Date: Mon, 25 Sep 2023 03:45:36 GMT
- Title: Can neural networks count digit frequency?
- Authors: Padmaksh Khandelwal
- Abstract summary: We compare the performance of different classical machine learning models and neural networks in identifying the frequency of occurrence of each digit in a given number.
We observe that the neural networks significantly outperform the classical machine learning models in terms of both the regression and classification metrics for both the 6-digit and 10-digit number.
- Score: 16.04455549316468
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this research, we aim to compare the performance of different classical
machine learning models and neural networks in identifying the frequency of
occurrence of each digit in a given number. It has various applications in
machine learning and computer vision, e.g. for obtaining the frequency of a
target object in a visual scene. We considered this problem as a hybrid of
classification and regression tasks. We carefully create our own datasets to
observe systematic differences between different methods. We evaluate each of
the methods using different metrics across multiple datasets.The metrics of
performance used were the root mean squared error and mean absolute error for
regression evaluation, and accuracy for classification performance evaluation.
We observe that decision trees and random forests overfit to the dataset, due
to their inherent bias, and are not able to generalize well. We also observe
that the neural networks significantly outperform the classical machine
learning models in terms of both the regression and classification metrics for
both the 6-digit and 10-digit number datasets. Dataset and code are available
on github.
Related papers
- A Comparison of Machine Learning Methods for Data with High-Cardinality
Categorical Variables [6.85316573653194]
Machine learning methods can have difficulties with high-cardinality variables.
In this article, we empirically compare several versions of two of the most successful machine learning methods.
arXiv Detail & Related papers (2023-07-05T07:26:27Z) - Do We Really Need a Learnable Classifier at the End of Deep Neural
Network? [118.18554882199676]
We study the potential of learning a neural network for classification with the classifier randomly as an ETF and fixed during training.
Our experimental results show that our method is able to achieve similar performances on image classification for balanced datasets.
arXiv Detail & Related papers (2022-03-17T04:34:28Z) - Deep ensembles in bioimage segmentation [74.01883650587321]
In this work, we propose an ensemble of convolutional neural networks (CNNs)
In ensemble methods, many different models are trained and then used for classification, the ensemble aggregates the outputs of the single classifiers.
The proposed ensemble is implemented by combining different backbone networks using the DeepLabV3+ and HarDNet environment.
arXiv Detail & Related papers (2021-12-24T05:54:21Z) - Learning a binary search with a recurrent neural network. A novel
approach to ordinal regression analysis [0.0]
This article investigates the application of sequence-to-sequence learning methods provided by the deep learning framework in ordinal regression.
A method for visualizing the model's explanatory variables according to the ordinal target variable is proposed.
The method is compared to traditional ordinal regression methods on a number of benchmark dataset, and is shown to have comparable or significantly better predictive power.
arXiv Detail & Related papers (2021-01-07T16:16:43Z) - Theoretical Insights Into Multiclass Classification: A High-dimensional
Asymptotic View [82.80085730891126]
We provide the first modernally precise analysis of linear multiclass classification.
Our analysis reveals that the classification accuracy is highly distribution-dependent.
The insights gained may pave the way for a precise understanding of other classification algorithms.
arXiv Detail & Related papers (2020-11-16T05:17:29Z) - Category-Learning with Context-Augmented Autoencoder [63.05016513788047]
Finding an interpretable non-redundant representation of real-world data is one of the key problems in Machine Learning.
We propose a novel method of using data augmentations when training autoencoders.
We train a Variational Autoencoder in such a way, that it makes transformation outcome predictable by auxiliary network.
arXiv Detail & Related papers (2020-10-10T14:04:44Z) - Reservoir Memory Machines as Neural Computers [70.5993855765376]
Differentiable neural computers extend artificial neural networks with an explicit memory without interference.
We achieve some of the computational capabilities of differentiable neural computers with a model that can be trained very efficiently.
arXiv Detail & Related papers (2020-09-14T12:01:30Z) - System Identification Through Lipschitz Regularized Deep Neural Networks [0.4297070083645048]
We use neural networks to learn governing equations from data.
We reconstruct the right-hand side of a system of ODEs $dotx(t) = f(t, x(t))$ directly from observed uniformly time-sampled data.
arXiv Detail & Related papers (2020-09-07T17:52:51Z) - Learning What Makes a Difference from Counterfactual Examples and
Gradient Supervision [57.14468881854616]
We propose an auxiliary training objective that improves the generalization capabilities of neural networks.
We use pairs of minimally-different examples with different labels, a.k.a counterfactual or contrasting examples, which provide a signal indicative of the underlying causal structure of the task.
Models trained with this technique demonstrate improved performance on out-of-distribution test sets.
arXiv Detail & Related papers (2020-04-20T02:47:49Z)
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.