This introduces the Continual Learning research trends based on Transformers and self-supervised learning presented at CVPR 2022.
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.
Through an NVIDIA GTC 2022 session, the growth of the digital avatar market and its core technology, Omniverse ACE, are introduced.
This explores the latest trends in personalized image/language models and NeRF technology through NVIDIA GTC 2022.
At the IJCAI 2022 conference, research trends emphasizing the importance of reinforcement learning, federated learning, and reasoning capabilities were confirmed.
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.
This piece covers the necessity and principles of Causal Representation Learning, which reasons about causality beyond correlation to overcome the limitations of existing machine learning.
This paper analyzes the limitations of existing machine learning from a Causal Inference perspective and presents the need for causal representation learning.
This introduces PromptRank, a technique that uses LLMs to find efficient document paths for complex Multi-hop Question Answering (MQA).
PromptRank is a technique that combines unsupervised retrieval with an LLM reranker to improve data efficiency in multi-hop question answering.
This introduces the MAMA framework, which maximizes forecasting performance by leveraging multi-resolution information in time series data.
AI Briefing