Deep learning methods exhibit excellent performance in diagnosing cardiovascular diseases from ECGs. Nevertheless, their "black-box" nature contributes to their limited integrations into clinical scenarios because a lack of interpretability hinders their broader adoption. To overcome this limitation, researchers from the Institute of Biomedical Engineering, TU Dresden, developed xECGArch, a deep learning architecture designed specifically for…
While at MIT Media Lab in 2010, Karthik Dinakar and Birago Jones developed a machine learning tool destined to help content moderation teams at tech companies like Twitter and YouTube. The project excited many, leading to a demonstration at a White House cyberbullying summit. However, the system tripped over unconventional wording in teenage vernacular, revealing…
MIT researchers have developed a deep-learning model to improve the efficiency of warehouse robots. The team used a neural network architecture to encode features including the robots' paths, tasks, and obstacles in the warehouse. This enabled the model to predict where congestion was most likely to occur and take measures to counteract it.
The groundbreaking method…
Natural Language Processing (NLP) faces major challenges in addressing the limitations of decoder-only Transformers, which are the backbone of large language models (LLMs). These models contend with issues like representational collapse and over-squashing, which severely hinder their functionality. Representational collapse happens when different sequences produce nearly the same results, while over-squashing occurs when the model…