KAUST Introduction on Machine Learning Bootcamp

As part of KAUST’s mission to disseminate skills a

Rising Stars in AI Symposium 2022 at KAUST

The "Rising Stars in AI Symposium" is geared towards yo

In this talk, I will discuss how the use of second-order information – e.g, curvature or covariance – can help in all three problems, yet with vastly different roles in each. First, I will present a noisy quadratic model, which qualitatively predicts scaling properties of a variety of optimizers and in particular suggests that second-order optimization algorithms would extend perfect scaling to much bigger batches. Second, I will show how we can derive and implement scalable and flexible Bayesian inference algorithms from standard second-order optimization algorithms. Third, I will describe a novel second-order algorithm that finds desired equilibria and save us from converging to spurious fixed points in two-player sequential games (i.e. bilevel optimization) or even more general settings. Finally, I will conclude how my research would pave the way towards intelligent machines that can learn from data and experience efficiently, reason about their own decisions, and act in our interests.

I will give a self-contained introduction to the theory of the neural network function class and its application to image classification and numerical solution of partial differential equations.

I will give a self-contained introduction to the theory of the neural network function class and its application to image classification and numerical solution of partial differential equations.

I will give a self-contained introduction to the theory of the neural network function class and its application to image classification and numerical solution of partial differential equations.

Abstract

The overarching goal of Prof.

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Most existing learning algorithms can be categorized into supervised, semi-supervised, an

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In this talk, I will talk about recent topics in generative modeling.

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In essence, science is about discovering regularities in Nature.

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An ontology is an explicit specification of a conceptualization of a domain.

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RNAs need to form specific secondary structures for their functions.

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Protein-protein interactions drive many important biological events such as infection, re

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Quantitative structure-property relationships (QSPRs) using machine learning tools to rel

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In this talk, I will introduce our recent progress on how deep learning models inspired u

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In safety-critical application domains, it is crucial to assess the attackability of the

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We propose a new optimization formulation for training federated learning models.

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Recent research showed that most of the existing machine learning algorithms are vulnerab

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A key to causal inference with observational data is achieving balance in predictive feat

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Artificial Intelligence (AI) is a top subject in the expectations about emerging problem-

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Even though CNNs achieve great success in performing complex intelligence tasks, this ach

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When there are a lot of training data, or the deep neural network is too large, parallel

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Scaling deep learning to a large cluster of workers is challenging due to high communicat

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The Red Sea Development Company (TRSDC), one of Saudi’s premiere GigaProjects, aims to de

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In this talk, I will present our progress in controlling fish feeding and monitoring wate