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Found 12 papers, 4 papers with code
Aspect category learning and sentimental analysis using weakly supervised learning
In this study, we deployed hybrid models, namely BiLSTM, CNN-BiLSTM, and CNN-LSTM, which harness multiple inputs, including review text, aspect terms, and ratings.
Comparative Study of Predicting Stock Index Using Deep Learning Models
Time series forecasting has seen many methods attempted over the past few decades, including traditional technical analysis, algorithmic statistical models, and more recent machine learning and artificial intelligence approaches.
Evaluating the Utility of GAN Generated Synthetic Tabular Data for Class Balancing and Low Resource Settings
The results of the class balancing experiments showed that the GLM model trained on GAN-balanced data achieved the highest recall value.
Efficient Neural Net Approaches in Metal Casting Defect Detection
Our work sheds light on the fact that custom networks with efficient architectures and faster inference times can be built without the need of relying on pre-trained architectures.
Training Efficient CNNS: Tweaking the Nuts and Bolts of Neural Networks for Lighter, Faster and Robust Models
Deep Learning has revolutionized the fields of computer vision, natural language understanding, speech recognition, information retrieval and more.
Classification of Quasars, Galaxies, and Stars in the Mapping of the Universe Multi-modal Deep Learning
Our works shed new light on transfer learning and multi-modal deep learning architectures.
Do Deep Learning Models and News Headlines Outperform Conventional Prediction Techniques on Forex Data?
The Forex market is enormous, and it operates 24 hours a day.
Topic Modelling on Consumer Financial Protection Bureau Data: An Approach Using BERT Based Embeddings
Customers' reviews and comments are important for businesses to understand users' sentiment about the products and services.
Revisiting Facial Key Point Detection: An Efficient Approach Using Deep Neural Networks
The objective of the research has been to develop efficient deep learning models in terms of model size, parameters, and inference time and to study the effect of augmentation imputation and fine-tuning on these models.
Efficient Deep Learning Methods for Identification of Defective Casting Products
In order to reduce human error, it has become imperative to use efficient and low computational AI algorithms to identify such defective products.
Classification of Astronomical Bodies by Efficient Layer Fine-Tuning of Deep Neural Networks
Different architectures had different responses to the change in the number of trainable layers w. r. t accuracies.
Augmentations: An Insight into their Effectiveness on Convolution Neural Networks
Depth-wise separable convolutions outperformed 3x3 convolutions at higher parameters due to their ability to create deeper networks.
