China’s latest generation of open large language models has moved from catching up to actively challenging Western leaders on ...

Step aside, LLMs. The next big step for AI is learning, reconstructing and simulating the dynamics of the real world.

Abstract: In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for solving complex real-world problems, particularly in the domain of image processing. The success of ...

In this video, we will understand all major Optimization in Deep Learning. We will see what is Optimization in Deep Learning ...

Machine learning techniques that make use of tensor networks could manipulate data more efficiently and help open the black ...

Motif-2-12.7B-Reasoning is positioned as competitive with much larger models, but its real value lies in the transparency of ...

The company is positioning its new offerings as a business-ready way for enterprises to build domain-specific agents without first needing to create foundation models.

Unlike other industries, healthcare generates not only numerical and categorical data but also large volumes of unstructured ...

Pairing VL-PRMs trained with abstract reasoning problems results in strong generalization and reasoning performance improvements when used with strong vision-language models in test-time scaling ...

With the growing model size of deep neural networks (DNN), deep learning training is increasingly relying on handcrafted search spaces to find efficient parallelization execution plans. However, our ...

Decreasing Precision with layer Capacity trains deep neural networks with layer-wise shrinking precision, cutting cost by up to 44% and boosting accuracy by up to 0.68% ...

Abstract: The deep reinforcement learning (DRL) based Volt-VAR optimization (VVO) methods have been widely studied for active distribution networks (ADNs). Most approaches neglect power injection ...