![An Iterative Refinement Approach for MLLM: Introducing MLLM-DataEngine (arXiv:2308.13566v1 [cs.LG])](https://instadatanews.com/wp-content/uploads/2023/08/arxiv-logo-fb-1080x675.png)
by instadatahelp | Aug 29, 2023 | AI Blogs
In this paper, we introduce MLLM-DataEngine, a novel closed-loop system that connects data generation, model training, and evaluation. Despite the advancements in Multimodal Large Language Models (MLLMs) in instruction dataset building and benchmarking, the current...
by instadatahelp | Aug 29, 2023 | AI Blogs
Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data Authors: Bingqing Hu, Bin Nan; Volume 24, Issue 223, Pages 1-26, 2023. Abstract The majority of research on neural networks focuses on estimating the conditional mean...
by instadatahelp | Aug 29, 2023 | AI Blogs
HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation Weijie J. Su, Yuancheng Zhu; 24(124):1−53, 2023. Abstract Stochastic gradient descent (SGD) is a widely used method for online learning in cases where data is received in a continuous...
by instadatahelp | Aug 29, 2023 | AI Blogs
We present a new set of algorithms called Stochastic Generalized Method of Moments (SGMM) for estimating and inferring on moment restriction models that are overidentified. Our SGMM algorithm is a novel alternative to the widely used offline GMM algorithm proposed by...
by instadatahelp | Aug 29, 2023 | AI Blogs
Maintaining machine learning (ML) workflows in production can be challenging due to the various tasks involved, such as creating CI/CD pipelines, model versioning, monitoring for data drift, model retraining, and manual approval processes. To address these challenges,...
by instadatahelp | Aug 29, 2023 | AI Blogs
RankSEG: A Consistent Ranking-based Framework for Segmentation Authors: Ben Dai, Chunlin Li; Published in Journal of Machine Learning Research, 24(224):1−50, 2023. Abstract Segmentation is an important field in computer vision and natural language processing, where...
