Neural simpletrons: Learning in the limit of few labels with directed generative networks
We explore classifier training for data sets with very few labels. We investigate this task
using a neural network for nonnegative data. The network is derived from a hierarchical …
using a neural network for nonnegative data. The network is derived from a hierarchical …
Models of acetylcholine and dopamine signals differentially improve neural representations
R Holca-Lamarre, J Lücke… - Frontiers in computational …, 2017 - frontiersin.org
Biological and artificial neural networks (ANNs) represent input signals as patterns of neural
activity. In biology, neuromodulators can trigger important reorganizations of these neural …
activity. In biology, neuromodulators can trigger important reorganizations of these neural …
Truncated variational em for semi-supervised neural simpletrons
Inference and learning for probabilistic generative networks is often very challenging and
typically prevents scalability to as large networks as used for deep discriminative …
typically prevents scalability to as large networks as used for deep discriminative …
Slidenet: Fast and accurate slide quality assessment based on deep neural networks
This work tackles the automatic fine-grained slide quality assessment problem for digitized
direct smears test using the Gram staining protocol. Automatic quality assessment can …
direct smears test using the Gram staining protocol. Automatic quality assessment can …
[PDF][PDF] Learning representations with neuromodulators
R Holca-Lamarre - 2017 - depositonce.tu-berlin.de
Neurons in the cortex and in multi-layer perceptrons (MLPs) represent inputs as patterns of
activity. In both systems, the nature of a neural representation carries important …
activity. In both systems, the nature of a neural representation carries important …