Abstract: In multi-instance multi-label learning (i.e. MIML), each example is not only represented by multiple instances but also associated with multiple labels. Most existing algorithms solve MIML ...

Deep Learning-based embeddings are used widely for “dense retrieval” in information retrieval, computer vision, NLP, amongst others, owing to capture diverse types of semantic information. This ...

Abstract: An adaptive k-nearest neighbor algorithm (AdaNN) is brought forward in this paper to overcome the limitation of the traditional k-nearest neighbor algorithm (kNN) which usually identifies ...

New SIFT algorithm, developed by ETH computer scientists, continuously reduces uncertainty of AI responses using selected enrichment data tailored to the specific question. Algorithm recognises ...

As we progress into 2025, Artificial Intelligence (AI) continues to reshape industries and revolutionize how we interact with technology. For those starting their journey in AI, it’s essential to ...

The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...

The goal of a machine learning regression problem is to predict a single numeric value. For example, you might want to predict the price of a particular make and model of a used car based on its ...