Hui Jiang
I am a professor in Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University. I am conducting research on machine learning and its applications to speech and language processing. I regularly post here some technology blogs on machine learning, artificial intelligence and beyond.
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A Deterministic View of Diffusion Models
In this post, we present a deterministic perspective on diffusion models. In this approach, neural networks are trained as an inverse function of the deterministic diffusion mapping that progressively corrupts images at each timestep. This method simplifies the derivation of diffusion models, making the process more straightforward and comprehensive.
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Understanding Transformers and GPT: An In-depth Overview
In this post, we delve into the technical details of the widely used transformer architecture by deriving all formulas involved in its forward and backward passes step by step. By doing so, we can implement these passes ourselves and often achieve more efficient performance than using autograd methods. Additionally, we introduce the technical details on the construction of the popular GPT-3 model using the transformer architecture.
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How Good is ChatGPT at Logical Reasoning?
In this post, I have employed open-domain test cases to explore ChatGPT’s proficiency in logical reasoning. The examples presented have demonstrated unequivocally that ChatGPT possesses remarkable logical reasoning skills, enabling it to solve a diverse set of open-domain reasoning tasks with outstanding performance.
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How will ChatGPT Affect Our Teaching and Learning?
In this post, I will address the pressing challenges posed by ChatGPT to our current post-secondary education systems in universities and colleges. Additionally, I will explore the far-reaching impacts that ChatGPT implies for the future of online learning and how we will engage in it.
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What Is ChatGPT?
This is an accessible introduction to explain what ChatGPT is and how it works. It uses plain language to aim at a general audience without any technical background in machine learning. -
Machine Learning Fundamentals
