Blog(3)

• Learning Representations from Explainable and Connectionist Approaches for Visual Question Answering

  VQA [1,2] is the field of research that aims to develop methods for answering natural language questions based on the information provided in corresponding images.

• SemEval-2025 Task 8: LLM Ensemble Methods for QA over Tabular Data

  Question answering using Large Language Models has gained significant popularity in both everyday communication and at the workplace. However|certain tasks|such as querying tables|still pose challenges for commercial and open-source chatbots powered by advanced deep learning models.

• Vision-Language Model Guided Semi-Supervised Learning for No-Reference Video Quality Assessment

  The widespread adoption of mobile devices has led to a rapid growth of video content that is captured, transmitted and shared on various social media platforms.

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Publications(25)

• Prodigy: Expeditiously Adaptive Parameter-Free Learner

  AuthorKonstantin Mishchenko

  PublishedInternational Conference on Machine Learning (ICML)

  Date2024-07-23

• A 140-GHz RF Beamforming Phased-Array Receiver in 22-nm CMOS FDSOI for 6G Communication

  AuthorShenggang Dong,Navneet Sharma,Won Suk Choi,Gary Xu

  PublishedIEEE Radio Frequency Integrated Circuits Symposium

  Date2023-06-12

• AI/ML Empowered High-Order Modulations for 6G High Capacity Communications

  AuthorPranav Madadi,Joonyoung Cho,Charlie Zhang,Daoud Burghal

  PublishedIEEE International Conference on Communications (ICC)

  Date2023-05-29

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News(3)

• [Blog] SemEval-2025 Task 8: LLM Ensemble Methods for QA over Tabular Data

  Question answering using Large Language Models has gained significant popularity in both everyday communication and at the workplace. However|certain tasks|such as querying tables|still pose challenges for commercial and open-source chatbots powered by advanced deep learning models.

• [Blog] SRCB's Non-Uniform Constellation Scheme in GLOBECOM 2024 Conference

  The current 5G systems adopt conventional uniform quadrature-amplitude modulation (QAM) with signal points on the rectangular grid, which results in a theoretical 1.53 dB gap to the Shannon capacity.

• [Blog] Learning Representations from Explainable and Connectionist Approaches for Visual Question Answering

  VQA [1,2] is the field of research that aims to develop methods for answering natural language questions based on the information provided in corresponding images.

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