Search Results for author: Abien Fred Agarap

Found 9 papers, 7 papers with code

k-Winners-Take-All Ensemble Neural Network

no code implementations4 Jan 2024 Abien Fred Agarap, Arnulfo P. Azcarraga

We improve on these aforementioned ways for combining a group of neural networks by using a k-Winners-Take-All (kWTA) activation function, that acts as the combination method for the outputs of each sub-network in the ensemble.

Text Classification and Clustering with Annealing Soft Nearest Neighbor Loss

no code implementations23 Jul 2021 Abien Fred Agarap

We define disentanglement as how far class-different data points from each other are, relative to the distances among class-similar data points.

Clustering Disentanglement +3

Improving k-Means Clustering Performance with Disentangled Internal Representations

1 code implementation5 Jun 2020 Abien Fred Agarap, Arnulfo P. Azcarraga

Deep clustering algorithms combine representation learning and clustering by jointly optimizing a clustering loss and a non-clustering loss.

Clustering Deep Clustering +2

Deep Learning using Rectified Linear Units (ReLU)

1 code implementation22 Mar 2018 Abien Fred Agarap

We introduce the use of rectified linear units (ReLU) as the classification function in a deep neural network (DNN).

Classification General Classification

An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification

2 code implementations10 Dec 2017 Abien Fred Agarap

Empirical data has shown that the CNN-SVM model was able to achieve a test accuracy of ~99. 04% using the MNIST dataset (LeCun, Cortes, and Burges, 2010).

General Classification Image Classification

A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data

5 code implementations10 Sep 2017 Abien Fred Agarap

Conventionally, like most neural networks, both of the aforementioned RNN variants employ the Softmax function as its final output layer for its prediction, and the cross-entropy function for computing its loss.

Binary Classification General Classification +4

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