Hongyangdu Hongyang Github This repository hosts a demonstration of the semantic encoder and decoder algorithm as presented in the paper "ai generated incentive mechanism and full duplex semantic communications for informati… hongyangdu has no activity yet for this period. Hi, i am hongyang du ( 杜泓阳 ) i am an assistant professor at the department of electrical and electronic engineering, the university of hong kong (hku). i received my ph.d. degree from nanyang technological university, singapore, and my b.eng. degree from beijing jiaotong university, china.
Hongyangdu Hongyang Github Hongyang du has 19 repositories available. follow their code on github. My research explores how self supervised learning can build world models for robotic control. i focus on extracting spatiotemporal and 3d aware priors to capture physical dynamics and how to create a scalable loop that couples causal prediction and action prediction. Our research is highly interdisciplinary, involving collaboration with experts from ai, networking systems, human–computer interaction, and electrical engineering. here’s what we’re looking for. passion for learning: strong self motivation and ability to quickly learn from new fields. 📚 list of awesome university courses for learning computer science!.
Awards Hongyang Du S Website Our research is highly interdisciplinary, involving collaboration with experts from ai, networking systems, human–computer interaction, and electrical engineering. here’s what we’re looking for. passion for learning: strong self motivation and ability to quickly learn from new fields. 📚 list of awesome university courses for learning computer science!. Generative diffusion models (gdms) have emerged as a transformative force in the realm of generative artificial intelligence (gai), demonstrating their versatility and efficacy across a variety of applications. In this part, we representatively formulate an optimization problem in a wireless network and show a step bystep tutorial to solve it by using gdms. consider a wireless communication network where a base station with total power $p t$ serves a set of users over multiple orthogonal channels. "ai generated incentive mechanism and full duplex semantic communications for information sharing" authored by hongyang du, jiacheng wang, dusit niyato, jiawen kang, zehui xiong, and dong in kim, accepted by ieee jsac. the paper can be accessed here or arxiv. We further fine tune sota mllms using group relative policy optimization (grpo) on real and synthetic commonsense physics data. results show notable accuracy gains, especially with counterexample integration, advancing mllms' reasoning capabilities. [paper] [webpage] [github] [huggingface].
Awards Hongyang Du S Website Generative diffusion models (gdms) have emerged as a transformative force in the realm of generative artificial intelligence (gai), demonstrating their versatility and efficacy across a variety of applications. In this part, we representatively formulate an optimization problem in a wireless network and show a step bystep tutorial to solve it by using gdms. consider a wireless communication network where a base station with total power $p t$ serves a set of users over multiple orthogonal channels. "ai generated incentive mechanism and full duplex semantic communications for information sharing" authored by hongyang du, jiacheng wang, dusit niyato, jiawen kang, zehui xiong, and dong in kim, accepted by ieee jsac. the paper can be accessed here or arxiv. We further fine tune sota mllms using group relative policy optimization (grpo) on real and synthetic commonsense physics data. results show notable accuracy gains, especially with counterexample integration, advancing mllms' reasoning capabilities. [paper] [webpage] [github] [huggingface].
Dong Hongyang Github "ai generated incentive mechanism and full duplex semantic communications for information sharing" authored by hongyang du, jiacheng wang, dusit niyato, jiawen kang, zehui xiong, and dong in kim, accepted by ieee jsac. the paper can be accessed here or arxiv. We further fine tune sota mllms using group relative policy optimization (grpo) on real and synthetic commonsense physics data. results show notable accuracy gains, especially with counterexample integration, advancing mllms' reasoning capabilities. [paper] [webpage] [github] [huggingface].
Hi I Am Hongyang Du 杜泓阳 Hongyang Du S Website
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