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Found 8 papers, 2 papers with code
LTNER: Large Language Model Tagging for Named Entity Recognition with Contextualized Entity Marking
The use of LLMs for natural language processing has become a popular trend in the past two years, driven by their formidable capacity for context comprehension and learning, which has inspired a wave of research from academics and industry professionals.
Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle
In the wake of relentless digital transformation, data-driven solutions are emerging as powerful tools to address multifarious industrial tasks such as forecasting, anomaly detection, planning, and even complex decision-making.
Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems
Simultaneously, the INN is designed to partition the parameter vector linked to the input physical field into two distinct components: the expansion coefficients representing the forward problem solution and the Gaussian latent noise.
Bayes Optimal Informer Sets for Early-Stage Drug Discovery
An IBR method selects an informer set of compounds, and then prioritizes the remaining compounds on the basis of new bioactivity experiments performed with the informer set on the target.
Methodology
On a scalable problem transformation method for multi-label learning
Binary relevance is a simple approach to solve multi-label learning problems where an independent binary classifier is built per each label.
Negative Log Likelihood Ratio Loss for Deep Neural Network Classification
In deep neural network, the cross-entropy loss function is commonly used for classification.
