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02/09/2023
Platform Trials: the Impact of common Controls on Type One Error and Power
Platform trials offer a framework to study multiple interventions in a s...
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11/17/2022
On the use of non-concurrent controls in platform trials: A scoping review
Platform trials gained popularity during the last few years as they incr...
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05/12/2022
TaDeR: A New Task Dependency Recommendation for Project Management Platform
Many startups and companies worldwide have been using project management...
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02/18/2021
On Connectivity of Solutions in Deep Learning: The Role of Over-parameterization and Feature Quality
It has been empirically observed that, in deep neural networks, the solu...
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01/24/2021
On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths
This paper studies the global convergence of gradient descent for deep R...
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01/21/2021
A Note on Connectivity of Sublevel Sets in Deep Learning
It is shown that for deep neural networks, a single wide layer of width ...
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12/21/2020
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks
A recent line of work has analyzed the theoretical properties of deep ne...
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02/18/2020
Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology
A recent line of research has provided convergence guarantees for gradie...
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12/23/2019
Jupiter: A Networked Computing Architecture
In the era of Internet of Things, there is an increasing demand for netw...
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01/22/2019
On Connected Sublevel Sets in Deep Learning
We study sublevel sets of the loss function in training deep neural netw...
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09/27/2018
On the loss landscape of a class of deep neural networks with no bad local valleys
We identify a class of over-parameterized deep neural networks with stan...
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02/28/2018
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
In the recent literature the important role of depth in deep learning ha...
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10/30/2017
The loss surface and expressivity of deep convolutional neural networks
We analyze the expressiveness and loss surface of practical deep convolu...
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04/26/2017
The loss surface of deep and wide neural networks
While the optimization problem behind deep neural networks is highly non...
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10/28/2016
Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods
The optimization problem behind neural networks is highly non-convex. Tr...
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03/29/2016
Latent Embeddings for Zero-shot Classification
We present a novel latent embedding model for learning a compatibility f...
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11/09/2015
An Efficient Multilinear Optimization Framework for Hypergraph Matching
Hypergraph matching has recently become a popular approach for solving c...
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04/29/2015