Blog(5)

• RaBiT: Residual-Aware Binarization Training for Accurate and Efficient LLMs

  The rapid advancement of Large Language Model (LLM) capabilities has been accompanied by a significant increase in model parameter scale.

• LittleBit-2: Maximizing the Spectral Energy Gain in Sub-1-Bit LLMs via Latent Geometry Alignment

  Large Language Models (LLMs) continue to deliver remarkable capabilities, but their memory footprint remains a rigid barrier to deployment.

• Respecting Underlying Biosignal Structure: Cross-Biosignal Pretraining for Wearable Health AI

  Wearable photoplethysmography (PPG) has become one of the most widely available physiological signals in modern health devices.

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Research Areas(0)

Publications(24)

• Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning

  AuthorHao Zhou, Cyrus Tanade, Keum San Chun, Juhyeon Lee, Migyeong Gwak, Viswam Nathan, Md Mahbubur Rahman, Subramaniam Venkatraman, Sharanya Arcot Desai

  PublishedInternational Conference on Machine Learning (ICML)

  Date2026-05-01

• Hierarchical planning for complex tasks with KG-RAG and symbolic verification

  AuthorCristina Cornelio

  PublishedInternational Conference on Machine Learning (ICML)

  Date2025-07-13

• BoA: Attention-aware Post-training Quantization without Backpropagation

  AuthorJunhan Kim, Ho-young Kim, Chungman Lee, Joonyoung Kim, Yongkweon Jeon

  PublishedInternational Conference on Machine Learning (ICML)

  Date2025-07-13

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News(1)

• [Blog] ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networks

  Recently we proposed a new optimization algorithm called Adaptive Sharpness-Aware Minimization (ASAM), which pushes the limit of deep learning via PAC-Bayesian theory. ASAM has been improving generalization performance for various tasks leveraging geometry of loss landscape, which is highly correlated with generalization. In recognition of its theoretical contribution and practicality, our paper was published at the 38th International Conference on Machine Learning (ICML 2021).

Others(1)

• Samsung AI Center - Moscow

  About Us Papers Play Samsung AI Center – Moscow is the first institution in Russia focused directly on artificial intelligence (AI), where Russia’s strengths and capabilities in fundamental sciences, especially mathematics and physics, are brought together with Samsung global R&D strategy. Samsung AI Center - Moscow is led by Dr. Anton Konushin who is world-known AI Computer Vision expert and associate professor at Moscow State University and Higher School of Econo...