This academic article explores how to enhance the teachability of large language models (LLMs) used for writing robot codes. These LLMs allow non-experts to direct robot behaviours, modify them based…
Continue readingThe Segment Anything Model (SAM) is a widely used tool for image processing, but its application in ultrasound image segmentation has limitations due to the noisy nature of ultrasound images….
Continue readingThe Segment Anything Model (SAM) is a popular image processing tool for its segmentation accuracy, variety of input prompts, training capabilities, and efficient model design. However, its application to ultrasound…
Continue readingThis research proposes using small pretrained foundational generative language models as a general learning framework for sequence-based tasks. The approach overcomes challenges associated with training neural networks and language models…
Continue readingThe newly released Segment Anything Model (SAM) is a popular tool used in image processing due to its superior segmentation accuracy, input prompts, training capabilities, and efficient model design. However,…
Continue readingThe paper ‘Unlearnable Algorithms for In-context Learning’ focuses on the concept of machine unlearning, a process that modifies a model to behave as if it were trained without including a…
Continue readingThis research discusses the challenge of diagnosing depression, a major global health issue. Many patients turn to social media platforms like Reddit to share their experiences and seek advice due…
Continue readingThis research uses text from Reddit and a proposed labeling approach to facilitate the diagnosis of depression severity. The Longformer model is fine-tuned and its performance compared against baseline models…
Continue readingThe paper proposes a language-based programming assistant for industrial robots. The assistant provides interactive dialogues about skills, example use-cases and expected robot behavior. The authors present three alternative model families,…
Continue readingThe study focuses on the challenge of structural divergence in pre-training and fine-tuning Graph Neural Networks (GNNs). It identifies the root cause as the discrepancy of generative patterns between pre-training…
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