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(27)

• LittleBit: Ultra-Low Bit Quantization via Latent Factorization

  AuthorBanseok Lee, Dongkyu Kim, Youngcheon You, Youngmin Kim

  PublishedNeural Information Processing Systems (NeurIPS)

  Date2025-12-02

• Augmenting Perceptual Super-Resolution via Image Quality Predictors

  AuthorFengjia Zhang, Samrudhdhi B. Rangrej, Tristan Ty Aumentado-Armstrong, Afsaneh Fazly, Alex Levinshtein

  PublishedComputer Vision and Pattern Recognition (CVPR)

  Date2025-06-11

• Prodigy: Expeditiously Adaptive Parameter-Free Learner

  AuthorKonstantin Mishchenko

  PublishedInternational Conference on Machine Learning (ICML)

  Date2024-07-23

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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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