In this paper, we present a novel deep unsupervised active learning framework. The proposed method can explicitly learn a nonlinear embedding to map each input into a latent space via a deep neural ...

Block diagram of pattern/background phases and greedy training methods for learning one single image. ... Keywords: unsupervised learning, spiking neural network (SNN), memristor, RRAM (resistive ...

Whether in supervised or unsupervised learning modes, when a neural network learning pass is complete, percentages in each layer are adjusted, either automatically by the system or manually by ...

Artificial neural networks exhibit learning abilities and can perform tasks which are tricky for conventional computing systems, such as pattern recognition. Here, Serb et al. show experimentally ...

The best quantitative models of these areas are deep neural networks trained with human annotations. However, they receive more annotations than infants, making them implausible models of the ventral ...

Exploiting invariances in data is crucial for neural networks to learn efficient representations and to make accurate predictions. Translation invariance is a key symmetry in image processing and lies ...