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Muhammad Irshad on Muck Rack

Muhammad Irshad

Pakistan
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Articles

On some improved two-parameter ridge regression estimators for the heteroscedastic linear regression models: simulation and application

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By Muhammad Irshad, Qamruz Zaman, Danish Wasim
| Taylor & Francis Online
Abstract Monte Carlo simulations are used to demonstrate the novelty and improved performance of the proposed heteroscedasticity-adjusted two-parameter ridge estimators in regression analysis. This study addresses the problem of multicollinearity and heteroscedasticity in linear regression by developing improved two-parameter ridge estimators. A modified heteroscedasticity-adjusted error variance is proposed and incorporated into existing ridge-type estimators.
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A careful examination of large behavior models for multitask dexterous manipulation

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By Volker Strobel, Kai Chen, Alexander Robey, Jose Barreiros, Andrew E. Beaulieu, Aditya Bhat, Rick Cory, Eric Cousineau, Hongkai Dai, Ching-Hsin Fang, Kunimatsu Hashimoto, Muhammad Irshad, Masha Itkina, Naveen Kuppuswamy, Kuan-Hui Lee, Katherine Liu, Dale McConachie, Ian McMahon, Haruki Nishimura, Charles Richter, Paarth Shah, Krishnan Srinivasan, Chen Xu, Calder Phillips-Grafflin, Blake Wulfe
| Science Magazine Verified
Abstract Robot manipulation has seen tremendous progress in recent years, with imitation learning policies enabling successful performance of dexterous and hard-to-model tasks. Concurrently, scaling data and model size has led to the development of capable language and vision foundation models, motivating large-scale efforts to create general-purpose robot foundation models.
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๐—ฃ๐—ฅ ๐—บ๐—ฒ๐—ฟ๐—ด๐—ฒ๐—ฑ: ๐—”๐˜‡๐˜‚๐—ฟ๐—ฒ ๐—ข๐—ฝ๐—ฒ๐—ป๐—”๐—œ ๐˜€๐˜‚๐—ฝ๐—ฝ๐—ผ๐—ฟ๐˜ ๐—ณ๐—ผ๐—ฟ ๐—๐—ผ๐—ฝ๐—น๐—ถ๐—ป ๐—ฃ๐—น๐˜‚๐—ด๐—ถ๐—ป ๐—๐—ฎ๐—ฟ๐˜ƒ๐—ถ๐˜€! ๐Ÿš€

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By Muhammad Irshad
| DEV Community
Iโ€™m excited to share my latest open source contribution, enabling Azure OpenAI integration for the Jarvis ecosystem. ๐—ง๐—ต๐—ฒ ๐—œ๐—บ๐—ฝ๐—ฎ๐—ฐ๐˜: โ€ข Seamless Integration: Added support via the OpenAI-compatible custom model path. โ€ข Optimized Architecture: Refactored header handling for better provider flexibility. โ€ข Reliability: Preserved critical retry logic for stable note indexing. Open source is about building together and making tools better for everyone.
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