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Found 8 papers, 1 papers with code
OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models
We present OpenMedLM, a prompting platform which delivers state-of-the-art (SOTA) performance for OS LLMs on medical benchmarks.
Quantum Inspired Chaotic Salp Swarm Optimization for Dynamic Optimization
Although some optimization algorithms are proposed to deal with the changes in dynamic environments differently, there are still areas of improvement in existing algorithms due to limitations or drawbacks, especially in terms of locating and following the previously identified optima.
Problems with automating translation of movie/TV show subtitles
We present 27 problems encountered in automating the translation of movie/TV show subtitles.
Smaller Models, Better Generalization
Reducing network complexity has been a major research focus in recent years with the advent of mobile technology.
Effect of Various Regularizers on Model Complexities of Neural Networks in Presence of Input Noise
We show that $L_2$ regularization leads to a simpler hypothesis class and better generalization followed by DARC1 regularizer, both for shallow as well as deeper architectures.
Radius-margin bounds for deep neural networks
Explaining the unreasonable effectiveness of deep learning has eluded researchers around the globe.
Learning Neural Network Classifiers with Low Model Complexity
Our proposed approach yields benefits across a wide range of architectures, in comparison to and in conjunction with methods such as Dropout and Batch Normalization, and our results strongly suggest that deep learning techniques can benefit from model complexity control methods such as the LCNN learning rule.
Scalable Twin Neural Networks for Classification of Unbalanced Data
In this paper, we discuss a Twin Neural Network (Twin NN) architecture for learning from large unbalanced datasets.
