Abstract: With the wide application of graph neural network (GNN) in many fields, how to extract and aggregate node features effectively has become a hot research issue. In this paper, we propose a ...

For Gabriel Petersson, the path to becoming a research scientist at OpenAI didn’t start in a lecture hall but began with a ...

Here are 11 free NPTEL data science and analytics courses from leading IITs cover graph theory, Bayesian modelling, Python, R ...

We re-created TikTok’s algorithm based on 1,100 users’ feeds. Explore which topics are in your feed and see what the ...

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter ...

Overview: AI, data, finance, and digital skills are essential for high-demand jobs in 2025.Professional courses significantly enhance employability and career g ...

Two important architectures are Artificial Neural Networks and Long Short-Term Memory networks. LSTM networks are especially useful for financial applications because they are designed to work with ...

Personalized algorithms may quietly sabotage how people learn, nudging them into narrow tunnels of information even when they start with zero prior knowledge. In the study, participants using ...

Abstract: Recent advancements in deep neural networks heavily rely on large-scale labeled datasets. However, acquiring annotations for large datasets can be challenging due to annotation constraints.

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...