A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
The article discusses the application of Natural Language Processing (NLP) and Deep Learning (DL) in improving the identification of Venous thromboembolism (VTE) from radiology reports. VTE, which includes deep vein…
Continue readingThe study uncovers a surprising failure of logical deduction in large language models (LLMs). This failure, termed as the ‘Reversal Curse’, means that if a model is trained on a…
Continue readingThe article presents LongLoRA, a new efficient fine-tuning method that extends the context sizes of pre-trained large language models (LLMs) without significant computational costs. Traditional methods for training LLMs with…
Continue readingThe paper presents a solution to challenges in Federated Learning (FL) – data heterogeneity and model security. The solution, called Estimated Mean Aggregation (EMA), acts as a reference point for…
Continue readingThis article provides an in-depth review of action recognition, spotting, and spatio-temporal localization in soccer. It discusses the complexities of understanding actions in soccer due to the dynamic nature of…
Continue readingThe article presents a method to improve the accuracy of 3D object detection systems for self-driving cars across diverse driving environments. The approach utilizes repeated traversals of multiple locations and…
Continue readingThe article presents a new opportunity in creating computational models of social systems using generative artificial intelligence. This is done through Generative Agent-Based Models (GABMs) that use large language models…
Continue readingIn the pursuit of mastering intricate multimodal tasks, the paper introduces MM-Vet, an evaluation benchmark tailored for Large Multimodal Models (LMMs). LMMs, which enhance large language models (LLMs) with multimodal…
Continue reading“Revisiting Deformable Convolution for Depth Completion” is a research project that proposes an innovative approach to depth completion, a task which aims to produce high-quality dense depth maps from sparse…
Continue readingThis paper explores the capacity of large language models (LLMs) for reasoning in math word problems (MWPs) by employing symbolic representations of numeric problems. The authors find that the GPT-3’s…
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