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• Meta-Learning in Neural Networks

  AI methods are advancing across a range of applications from computer vision and natural language processing to autonomous control. There are many facets to AI’s capabilities that determine how useful it is in our lives. Besides the obvious metrics of peak accuracy or efficacy of an AI system at its task, other facets include: How effectively can it learn a new task from a small amount of data or experience? Can it perform, or even learn, within the limited hardware and battery power available on a handheld device?

• SmoGVLM: A Small, Graph-Enhanced Vision-Language Model

  Large vision-language models (VLMs) have achieved impressive performance across a wide range of multimodal tasks, from visual question answering (VQA) to reasoning over images and text [1, 2]. However, these models often suffer from hallucinations and poor grounding when faced with knowledge-intensive queries.

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