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Found 11 papers, 1 papers with code
Semantic Similarity Based Evaluation for Abstractive News Summarization
To achieve this, we translated the English STSb dataset into Turkish and presented the first semantic textual similarity dataset for Turkish as well.
A Turkish Hate Speech Dataset and Detection System
We present a machine learning system for automatic detection of hate speech in Turkish, along with a hate speech dataset consisting of tweets collected in two separate domains.
Going Forward-Forward in Distributed Deep Learning
We introduce a new approach in distributed deep learning, utilizing Geoffrey Hinton's Forward-Forward (FF) algorithm to speed up the training of neural networks in distributed computing environments.
Variational Self-Supervised Contrastive Learning Using Beta Divergence
The method (VCL) utilizes variational contrastive learning with beta-divergence to learn robustly from unlabelled datasets, including uncurated and noisy datasets.
CNN-BiLSTM model for English Handwriting Recognition: Comprehensive Evaluation on the IAM Dataset
Test time augmentation with rotation and shear transformations applied to the input image, is proposed to increase recognition of difficult cases and found to reduce the word error rate by 2. 5\% points.
Real or Virtual: A Video Conferencing Background Manipulation-Detection System
Additionally, it enables users to employ a virtual background to conceal their own environment due to privacy concerns or to reduce distractions, particularly in professional settings.
COVID-19 Detection in Computed Tomography Images with 2D and 3D Approaches
The 2D system detects the infection on each CT slice independently, combining them to obtain the patient-level decision via different methods (averaging and long-short term memory networks).
Relative Attribute Classification with Deep Rank SVM
Relative attributes indicate the strength of a particular attribute between image pairs.
Deep Convolutional Neural Network Ensembles using ECOC
Deep neural networks have enhanced the performance of decision making systems in many applications including image understanding, and further gains can be achieved by constructing ensembles.
GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data
This synthetic data is sampled from a GMM fitted to each class of the limited training data.
Classification Of Hyperspectral Images
General Classification
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