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06/22/2023
Accelerated Training via Incrementally Growing Neural Networks using Variance Transfer and Learning Rate Adaptation
We develop an approach to efficiently grow neural networks, within which...
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12/14/2020
Information-Theoretic Segmentation by Inpainting Error Maximization
We study image segmentation from an information-theoretic perspective, p...
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07/30/2020
Growing Efficient Deep Networks by Structured Continuous Sparsification
We develop an approach to training deep networks while dynamically adjus...
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02/20/2020
Kernel and Rich Regimes in Overparametrized Models
A recent line of work studies overparametrized neural networks in the "k...
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12/10/2019
Winning the Lottery with Continuous Sparsification
The Lottery Ticket Hypothesis from Frankle Carbin (2019) conjectures...
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12/04/2019
Domain-independent Dominance of Adaptive Methods
From a simplified analysis of adaptive methods, we derive AvaGrad, a new...
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08/13/2019
Building a Massive Corpus for Named Entity Recognition using Free Open Data Sources
With the recent progress in machine learning, boosted by techniques such...
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08/13/2019
On the Convergence of AdaBound and its Connection to SGD
Adaptive gradient methods such as Adam have gained extreme popularity du...
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02/26/2019
Learning Implicitly Recurrent CNNs Through Parameter Sharing
We introduce a parameter sharing scheme, in which different layers of a ...
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02/13/2019