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Information-Theoretic Segmentation by Inpainting Error Maximization
We study image segmentation from an information-theoretic perspective, p...
12/14/2020 ∙ by Pedro Savarese, et al. ∙
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Growing Efficient Deep Networks by Structured Continuous Sparsification
We develop an approach to training deep networks while dynamically adjus...
07/30/2020 ∙ by Xin Yuan, et al. ∙
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Kernel and Rich Regimes in Overparametrized Models
A recent line of work studies overparametrized neural networks in the "k...
02/20/2020 ∙ by Blake Woodworth, et al. ∙
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Winning the Lottery with Continuous Sparsification
The Lottery Ticket Hypothesis from Frankle Carbin (2019) conjectures...
12/10/2019 ∙ by Pedro Savarese, et al. ∙
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Domain-independent Dominance of Adaptive Methods
From a simplified analysis of adaptive methods, we derive AvaGrad, a new...
12/04/2019 ∙ by Pedro Savarese, et al. ∙
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Building a Massive Corpus for Named Entity Recognition using Free Open Data Sources
With the recent progress in machine learning, boosted by techniques such...
08/13/2019 ∙ by Daniel Specht Menezes, et al. ∙
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On the Convergence of AdaBound and its Connection to SGD
Adaptive gradient methods such as Adam have gained extreme popularity du...
08/13/2019 ∙ by Pedro Savarese, et al. ∙
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Learning Implicitly Recurrent CNNs Through Parameter Sharing
We introduce a parameter sharing scheme, in which different layers of a ...
02/26/2019 ∙ by Pedro Savarese, et al. ∙
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How do infinite width bounded norm networks look in function space?
We consider the question of what functions can be captured by ReLU netwo...
02/13/2019 ∙ by Pedro Savarese, et al. ∙
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