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pro vyhledávání: '"Nissani, Daniel N."'
Autor:
Nissani, Daniel N.
Contrastive Learning (CL) has been successfully applied to classification and other downstream tasks related to concrete concepts, such as objects contained in the ImageNet dataset. No attempts seem to have been made so far in applying this promising
Externí odkaz:
http://arxiv.org/abs/2408.02247
Autor:
Nissani, Daniel N.
In response to an object presentation, supervised learning schemes generally respond with a parsimonious label. Upon a similar presentation we humans respond again with a label, but are flooded, in addition, by a myriad of associations. A significant
Externí odkaz:
http://arxiv.org/abs/2302.10763
Autor:
Nissani, Daniel N.
Generative neural networks are able to mimic intricate probability distributions such as those of handwritten text, natural images, etc. Since their inception several models were proposed. The most successful of these were based on adversarial (GAN),
Externí odkaz:
http://arxiv.org/abs/2106.09330
Autor:
Nissani, Daniel N.
After four decades of research there still exists a Classification accuracy gap of about 20% between our best Unsupervisedly Learned Representations methods and the accuracy rates achieved by intelligent animals. It thus may well be that we are looki
Externí odkaz:
http://arxiv.org/abs/2001.07495
Autor:
Nissani, Daniel N.
An unsupervised learning classification model is described. It achieves classification error probability competitive with that of popular supervised learning classifiers such as SVM or kNN. The model is based on the incremental execution of small ste
Externí odkaz:
http://arxiv.org/abs/1806.09385
Conference
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