Search Results for author:
Found 7 papers, 0 papers with code
X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment
The impressive development of large language models (LLMs) is expanding into the realm of large multimodal models (LMMs), which incorporate multiple types of data beyond text.
BOK-VQA: Bilingual outside Knowledge-Based Visual Question Answering via Graph Representation Pretraining
The current research direction in generative models, such as the recently developed GPT4, aims to find relevant knowledge information for multimodal and multilingual inputs to provide answers.
A human brain atlas of chi-separation for normative iron and myelin distributions
Iron and myelin are primary susceptibility sources in the human brain.
Coil2Coil: Self-supervised MR image denoising using phased-array coil images
Hence, methods such as Noise2Noise (N2N) that require only pairs of noise-corrupted images have been developed to reduce the burden of obtaining training datasets.
Neuro CROSS exchange: Learning to CROSS exchange to solve realistic vehicle routing problems
Inspired by CE, we propose Neuro CE (NCE), a fundamental operator of learned meta-heuristic, to solve various VRPs while overcoming the limitations of CE (i. e., the expensive $\mathcal{O}(n^4)$ search cost).
Capturing the Production of the Innovative Ideas: An Online Social Network Experiment and "Idea Geography" Visualization
To investigate how the collective design and innovation processes would be affected by the diversity of knowledge and background of collective individual members, we conducted three collaborative design task experiments which involved nearly 300 participants who worked together anonymously in a social network structure using a custom-made computer-mediated collaboration platform.
The Power of Communities: A Text Classification Model with Automated Labeling Process Using Network Community Detection
One of the problems in supervised text classification models is that the models' performance depends heavily on the quality of data labeling that is typically done by humans.
