Deep learning models are already revolutionizing the way we think about AI. One such type is the 'transformer model,' which takes an attention mechanism...
Machine Learning has been extensively used in developing accurate interatomic potentials based on initial data of the given chemical system. Atomic Neural Networks(ANN) have...
Introduction
Deep learning neural networks, which are at the heart of modern artificial intelligence, are frequently characterized as "black boxes" with mysterious inner workings. However,...
Background of GNNs and Simplicial Complexes
A graph is a type of data structure that consists of two components, vertices, and edges. Graph Neural Network...
Language Modelling utilizes various statistics and probability techniques to predict the sequence of words occurring in a sentence. These models are widely used in...
Data augmentation
Data augmentation in machine learning is a technique that helps reduce overfitting. It increases the amount of data by adding slightly modified copies...
In recent years, developments in neural networks have led to the advance of data-to-text generation. However, their inability to control structure can be limiting...
DALL·E has shown an impressive ability of composition-based systematic generalization in image generation, but it requires the dataset of text-image pairs and provides compositional...
Object recognition algorithms have come a long way in recent years, but they still require training datasets containing thousands of high-quality, annotated examples for...
Multi-object tracking (MOT) involves identifying and following objects as they move about in videos. Currently, available methods obtain identities by associating detection boxes whose...
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