AI Briefing#lg-ai-research
The latest AI and developer news about #lg-ai-research, with the original source and a short summary.
The latest AI and developer news about #lg-ai-research, with the original source and a short summary.
The research proposed a new imitation learning methodology that leverages expert demonstrations containing only state information to enable stable learning even in offline settings.
LG AI Research has released research on a model that effectively generates Loops, the core unit of music.
LG AI Research has solved the data scarcity and forgetting problems of existing AI vision inspection with a self-supervised learning-based technology.
LG AI Research has unveiled the architecture of the EXAONE multimodal model, which understands text and images bidirectionally.
This review focuses on the Diffusion Model, one of the most-noticed generative models among the diverse works presented at NeurIPS 2021, and covers the key research on it.
This article examines deep clustering techniques using Variational Autoencoder and the latest research based on VaDE.
This analyzes the latest research trends and cost-reduction techniques for Neural Architecture Search (NAS) presented at NeurIPS 2021.
This introduces research that combines Reinforcement Learning (RL) with Graph Neural Networks (GNN) to process complex data and learn general-purpose policies.
LG AI Research participated in CVPR 2022, showcasing multimodal representation learning and L-Verse research.
LG AI Research announced PSGI technology that efficiently infers hierarchical task structures.
Proposes a new methodology that improves Domain Generalization (DG) performance by using Natural Language Supervision to align visual representations with human reasoning.
This article covers the concept of Long-Tail Distribution (LTD) and various learning methodologies to address data imbalance problems.
LG AI Research introduced key research on self-supervised learning presented at ICML 2022.
This introduces Neural Combinatorial Optimization (NCO) research for efficiently solving combinatorial optimization problems in large-scale graph environments.
LG AI Research collaborated with Elsevier to achieve the top performance in the ChEMU 2022 challenge, which extracts reaction information from chemical patents and journals.
It introduces UniCLIP, a unified contrastive learning framework that breaks down boundaries between domains to enhance data efficiency.
LG AI Research developed a new attention mechanism-based AI model that improves HLA-peptide binding prediction performance.
The paper proposes SBM-Transformer, which adjusts attention sparsity based on data to improve computational efficiency.
We propose a new algorithm that improves the stability of offline Learning from Observation (LfO), which imitates expert behavior using only state information.
LG AI Research is researching a loop-based music generation model to help with motif generation, which is at the core of music composition.
This work proposes BeamCLIP, a methodology for efficiently transferring the representational power of large-scale multimodal models to small target models.
Introduces the latest research trends in generative models (SGM) and LLMs presented at NeurIPS 2022.
Introduces BeamCLIP, a technique that efficiently transfers the representational power of the large-scale multimodal model CLIP to a smaller model.
LG AI Research reviewed major research trends in generative models, LLMs, and reinforcement learning presented at NeurIPS 2022.