This introduces the latest research trends and key papers on RLHF and RLAIF presented at NeurIPS 2023.
This summarizes the latest research trends presented at NeurIPS 2023, covering the performance, safety, efficiency, and tool-use capabilities of LLMs.
LG AI Research presented a new training methodology at NeurIPS 2023 to improve deep learning performance on Tabular Data.
This introduces the audio and music generation technologies that drew attention at NeurIPS 2023, along with research achievements from LG AI Research.
Introduces the latest Computer Vision (CV) research trends and key papers from LG's participation in NeurIPS 2023.
Introduces the latest research trends in RLHF and RLAIF for improving LLM performance, along with the DPO and Fine-Grained RLHF methodologies.
This looks at the latest research trends in LLMs presented at NeurIPS 2023, including performance evaluation, safety, tool use, and efficient fine-tuning.
This summarizes research trends in general-purpose and domain-specific LLM-based autonomous agents presented at NeurIPS 2023.
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.
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