Artificial intelligence from Google DeepMind and OpenAI has reached a new benchmark in competitive programming, with both groups reporting that their latest models would have placed ...

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy ...

Imagine you are training an AI to play chess. Whenever it makes a wrong move, you point out the mistake and explain the reason. In the world of deep learning, the Loss Function plays a similar role.

Deep learning models have shown great potential in predicting and engineering functional enzymes and proteins. Does this prowess extend to other fields of biology as well? Contrary to expectations, a ...

Abstract: Neural networks (NNs) based wind power forecasting (WPF) under extreme weather conditions faces challenges, including limited sample sizes, domain shift problem between conventional and ...

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Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

Regularization in Deep Learning is very important to overcome overfitting. When your training accuracy is very high, but test accuracy is very low, the model highly overfits the training dataset set ...

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Switzerland pours more of its venture capital into deep tech than any other country, according to new data. The Swiss Deep Tech Report 2025 found that 60% of all Swiss venture funding between 2019 and ...