Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Eban, Elad"'
Committee-based models (ensembles or cascades) construct models by combining existing pre-trained ones. While ensembles and cascades are well-known techniques that were proposed before deep learning, they are not considered a core building block of d
Externí odkaz:
http://arxiv.org/abs/2012.01988
End-to-end training with back propagation is the standard method for training deep neural networks. However, as networks become deeper and bigger, end-to-end training becomes more challenging: highly non-convex models gets stuck easily in local optim
Externí odkaz:
http://arxiv.org/abs/2010.01189
State-of-the-art deep networks are often too large to deploy on mobile devices and embedded systems. Mobile neural architecture search (NAS) methods automate the design of small models but state-of-the-art NAS methods are expensive to run. Differenti
Externí odkaz:
http://arxiv.org/abs/2006.09581
Autor:
Liba, Orly, Cai, Longqi, Tsai, Yun-Ta, Eban, Elad, Movshovitz-Attias, Yair, Pritch, Yael, Chen, Huizhong, Barron, Jonathan T.
The sky is a major component of the appearance of a photograph, and its color and tone can strongly influence the mood of a picture. In nighttime photography, the sky can also suffer from noise and color artifacts. For this reason, there is a strong
Externí odkaz:
http://arxiv.org/abs/2006.10172
Image compression using neural networks have reached or exceeded non-neural methods (such as JPEG, WebP, BPG). While these networks are state of the art in ratedistortion performance, computational feasibility of these models remains a challenge. We
Externí odkaz:
http://arxiv.org/abs/1912.08771
Autor:
Eban, Elad, Movshovitz-Attias, Yair, Wu, Hao, Sandler, Mark, Poon, Andrew, Idelbayev, Yerlan, Carreira-Perpinan, Miguel A.
Despite the success of deep neural networks (DNNs), state-of-the-art models are too large to deploy on low-resource devices or common server configurations in which multiple models are held in memory. Model compression methods address this limitation
Externí odkaz:
http://arxiv.org/abs/1911.11177
Autor:
Bello, Irwan, Kulkarni, Sayali, Jain, Sagar, Boutilier, Craig, Chi, Ed, Eban, Elad, Luo, Xiyang, Mackey, Alan, Meshi, Ofer
Ranking is a central task in machine learning and information retrieval. In this task, it is especially important to present the user with a slate of items that is appealing as a whole. This in turn requires taking into account interactions between i
Externí odkaz:
http://arxiv.org/abs/1810.02019
In most machine learning applications, classification accuracy is not the primary metric of interest. Binary classifiers which face class imbalance are often evaluated by the $F_\beta$ score, area under the precision-recall curve, Precision at K, and
Externí odkaz:
http://arxiv.org/abs/1803.00067
We present MorphNet, an approach to automate the design of neural network structures. MorphNet iteratively shrinks and expands a network, shrinking via a resource-weighted sparsifying regularizer on activations and expanding via a uniform multiplicat
Externí odkaz:
http://arxiv.org/abs/1711.06798
The widely held belief that BQP strictly contains BPP raises fundamental questions: if we cannot efficiently compute predictions for the behavior of quantum systems, how can we test their behavior? In other words, is quantum mechanics falsifiable? In
Externí odkaz:
http://arxiv.org/abs/1704.04487