LG AI Research has unveiled a self-supervised learning technique using Binning to overcome the limitations of Tabular Data.
Introduces recent research that improves OOD detection performance by generating synthetic outliers in the text and image domains.
LG AI Research proposed an OOD detection methodology that generates sophisticated synthetic outliers using a Diffusion model.
This post analyzes the research trends in architecture and downstream tasks for Score-based Generative Modeling presented at NeurIPS 2023.
Introducing the multimodal generative technologies that drew attention at NeurIPS 2023 and LG AI Research's music generation interface.
Introduces industrial anomaly detection technology and the PatchCore algorithm for quality control in manufacturing processes.
Introduces the key research trends in LLM-based autonomous agents presented at NeurIPS 2023.
Introduces research that improves visual OOD detection performance by generating text-based outliers using LLMs.
LG AI Research unveiled research that improves OOD detection performance by generating sophisticated synthetic outliers using a Diffusion model.
This piece looks at research trends in the architecture and downstream tasks of score-based generative models presented at NeurIPS 2023.
Introduces deep learning-based anomaly detection technology for quality control in manufacturing processes, along with the PatchCore and ReConPatch algorithms.
LG AI Research has proposed a framework that efficiently handles complex spatiotemporal relationships in video to precisely estimate 3D human pose and shape.
By efficiently modeling spatio-temporal relationships, the team developed a robust 3D human pose and shape estimation technology that performs well even in complex environments.
Introduces innovative time series forecasting AI models presented at ICLR 2024 and LG AI Research's research vision.
It introduces the methodologies and technical characteristics of innovative time series forecasting models presented at ICLR 2024.
This piece examines the concept of foundation models for handling time series data and their classification based on how they utilize LLMs.
This introduces the latest research methodologies of Foundation models for tackling time series data, classified according to how they utilize LLMs.
LG AI Research and KAIST took first place in the EHRSQL task at NAACL 2024 with a highly reliable Text-to-SQL model for extracting medical data.
LG AI Research, together with KAIST, took 1st place at the NAACL 2024 workshop with a reliable Text-to-SQL model that improves access to medical data.
At ICLR 2024, LG AI Research introduced LeSS, a new model compression technique that combines filter pruning and low-rank decomposition.
LG AI Research presented research on the model compression technique LeSS and LLM structured pruning at an ICLR 2024 workshop.
LG AI Research has proposed the EXAONE LMM architecture, which resolves data licensing issues in existing LMMs while improving efficiency.
LG AI Research introduces a case where it combined EXAONE's Image Understanding and Generation technologies to realize the Hwadamsup media art.
LG AI Research proposed the AOCC architecture, which improves the efficiency of asynchronous multi-agent reinforcement learning (MARL).
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