CONCLUSION: A cancer training course is both highly needed and acceptable to medical and nursing students. Implementation of this cancer training course will improve students’ knowledge and skills and eventually improve the quality of cancer care and patients’ quality of life by ensuring early diagnosis and management. →
Keras is a widely used machine learning tool known for its high-level abstractions and ease of use, enabling rapid experimentation. Recent advances in CV and NLP have introduced challenges, such as the prohibitive cost of training large, state-of-the-art models. Access to open-source pretrained models is crucial. Additionally, preprocessing and metrics computation complexity has increased due… →
Integrating multiple generative foundation models helps by combining the strengths of models trained on different modalities, such as text, speech, and images, enabling the system to perform cross-modal tasks effectively. This integration allows for the efficient generation of outputs across multiple modalities simultaneously, leveraging the specific capabilities of each model. The two key issues in… →
INTRODUCTION: Tranexamic acid (TXA) is an inexpensive and widely available medication that reduces blood loss and red blood cell (RBC) transfusion in cardiac and orthopaedic surgeries. While the use of TXA in these surgeries is routine, its efficacy and safety in other surgeries, including oncologic surgeries, with comparable rates of transfusion are uncertain. Our primary… →
Training deep learning DL models is time-consuming and unpredictable. It is often hard to know precisely when a model will finish training or if it might crash unexpectedly. This uncertainty can lead to inefficiencies, especially when monitoring training manually. Some solutions exist to manage training times and failures, such as early stopping techniques and logging… →
Modern Deep Neural Networks (DNNs) are inherently opaque; we do not know how or why these computers arrive at the predictions they do. This is a major barrier to the broader use of Machine Learning techniques in many domains. An emerging area of study called Explainable AI (XAI) has arisen to shed light on how… →
Large Language Models (LLMs) have significantly advanced in recent times, primarily because of their increased capacity to follow human commands efficiently. Reinforcement Learning from Human Feedback (RLHF) is the main technique for matching LLMs to human intent. This method operates by optimizing a reward function, which can be reparameterized within the LLM’s policy or be… →
Sparse neural networks aim to optimize computational efficiency by reducing the number of active weights in the model. This technique is vital as it addresses the escalating computational costs associated with training and inference in deep learning. Sparse networks enhance performance without dense connections, reducing computational resources and energy consumption. The main problem addressed in… →
LLMs need to generate text reflecting the diverse views of multifaceted personas. Prior studies on bias in LLMs have focused on simplistic, one-dimensional personas or multiple-choice formats. However, many applications require LLMs to generate open-ended text based on complex personas. The ability to steer LLMs to represent these multifaceted personas accurately is critical to avoid… →
AI legal research and document drafting tools promise to enhance efficiency and accuracy in performing complex legal tasks. However, these tools need help with their reliability in producing accurate legal information. Lawyers increasingly use AI to augment their practice, from drafting contracts to analyzing discovery productions and conducting legal research. As of January 2024, 41… →