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Found 8 papers, 0 papers with code
Does Synthetic Data Make Large Language Models More Efficient?
Natural Language Processing (NLP) has undergone transformative changes with the advent of deep learning methodologies.
Can pruning make Large Language Models more efficient?
Transformer models have revolutionized natural language processing with their unparalleled ability to grasp complex contextual relationships.
Can a student Large Language Model perform as well as it's teacher?
The burgeoning complexity of contemporary deep learning models, while achieving unparalleled accuracy, has inadvertently introduced deployment challenges in resource-constrained environments.
Do Generative Large Language Models need billions of parameters?
This paper presents novel systems and methodologies for the development of efficient large language models (LLMs).
Harnessing the Power of Decision Trees to Detect IoT Malware
Due to its simple installation and connectivity, the Internet of Things (IoT) is susceptible to malware attacks.
New Approach to Malware Detection Using Optimized Convolutional Neural Network
This paper is different than most other papers in the literature in that it uses an expert data science approach by developing a convolutional neural network from scratch to establish a baseline of the performance model first, explores and implements an improvement model from the baseline model, and finally it evaluates the performance of the final model.
Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions
Recent natural language processing (NLP) techniques have accomplished high performance on benchmark datasets, primarily due to the significant improvement in the performance of deep learning.
Adversarial Training: A simple and efficient technique to Improving NLP Robustness
NLP models are shown to be prone to adversarial attacks which undermines their robustness, i. e. a small perturbation to the input text can fool an NLP model to incorrectly classify text.
