What if we could peer into the brain and watch how it organizes information as we act, perceive, or make decisions? A new ...

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% ...

Google Research has unveiled Titans, a neural architecture using test-time training to actively memorize data, achieving effective recall at 2 million tokens.

Neuromorphic computing systems, encompassing both digital and analog neural accelerators, promise to revolutionize AI ...

In this architecture, the training process adopts a joint optimization mechanism based on classical cross-entropy loss. WiMi treats the measurement probability distribution output by the quantum ...

Even networks long considered "untrainable" can learn effectively with a bit of a helping hand. Researchers at MIT's Computer ...

TensorFlow is an open-source machine learning framework built by Google, and this 100-second video explains how it works from ...

Layered metasurfaces trained as optical neural networks enable multifunctional holograms and security features, integrating ...

The integration of artificial intelligence (AI), particularly computer vision, into energy systems is revolutionizing power optimization, enabling efficient ...

Abstract: In this paper, an artificial neural network (ANN) guided approach is developed for the repeater optimization in multilayer graphene on-chip interconnect networks. The key attribute of the ...

VFF-Net introduces three new methodologies: label-wise noise labelling (LWNL), cosine similarity-based contrastive loss (CSCL), and layer grouping (LG), addressing the challenges of applying a forward ...