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Unveiling "Continuous Diffusion for Categorical Data Estimation for LLMs"

The CDCD framework harmoniously integrates continuous and discrete data generation methodologies, capitalizing on their respective strengths

Exploring Geometric Deep Learning and its Impactful Applications

The key idea behind geometric deep learning is to develop neural network architectures that can operate directly on geometric data.

Understanding CLIP

The reason why CLIP is so effective in zero-shot learning is because of the contrastive learning approach to jointly train a neural network.

NAS + Reinforcement learning: A review

Neural architecture search (NAS) is a method for automatically discovering the best neural network architecture for a given task.

Cost-efficient method to predict 3D hemodynamics

The deep learning methods are inexpensive compare to modeling a cardiovascular hemodynamic system.

OpenAI's GPT-3 Language Model

GPT-3 is now trending as one of the most powerful language models in the world.

Generative Methods in Medical Applications

Generative adversarial networks (GANs) have achieved state-of-the-art results in recent years and it is able to produce realistic images .

A Note on Bayesian Optimisation

Sometime during the 1740s, Reverend Thomas Bayes made an ingenious statistical discovery, an idea, a formula that had changed the world

Human-Machine Intelligence

The human brain is an amazing biological organ that dictates a human's life altogether right from the time the person is born to death.

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