Search Results for author: Sukhpal Singh Gill

Found 8 papers, 2 papers with code

Stock Market Price Prediction: A Hybrid LSTM and Sequential Self-Attention based Approach

no code implementations7 Aug 2023 Karan Pardeshi, Sukhpal Singh Gill, Ahmed M. Abdelmoniem

In this paper, our aim is to focus on the second aspect and build a model that predicts future prices with minimal errors.

A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction

no code implementations7 Aug 2023 Karan Gadgil, Sukhpal Singh Gill, Ahmed M. Abdelmoniem

Companies across the globe are keen on targeting potential high-value customers in an attempt to expand revenue and this could be achieved only by understanding the customers more.

Meta-Learning regression +1

ChatGPT: Vision and Challenges

no code implementations8 May 2023 Sukhpal Singh Gill, Rupinder Kaur

Artificial intelligence (AI) and machine learning have changed the nature of scientific inquiry in recent years.

Ethics Language Modelling

Mind meets machine: Unravelling GPT-4's cognitive psychology

no code implementations20 Mar 2023 Sifatkaur Dhingra, Manmeet Singh, Vaisakh SB, Neetiraj Malviya, Sukhpal Singh Gill

Cognitive psychology delves on understanding perception, attention, memory, language, problem-solving, decision-making, and reasoning.

Common Sense Reasoning Decision Making +2

Quantum Artificial Intelligence for the Science of Climate Change

1 code implementation28 Jul 2021 Manmeet Singh, Chirag Dhara, Adarsh Kumar, Sukhpal Singh Gill, Steve Uhlig

Climate change has become one of the biggest global problems increasingly compromising the Earth's habitability.

Deep learning for improved global precipitation in numerical weather prediction systems

no code implementations20 Jun 2021 Manmeet Singh, Bipin Kumar, Suryachandra Rao, Sukhpal Singh Gill, Rajib Chattopadhyay, Ravi S Nanjundiah, Dev Niyogi

This study is a proof-of-concept showing that residual learning-based UNET can unravel physical relationships to target precipitation, and those physical constraints can be used in the dynamical operational models towards improved precipitation forecasts.

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