Michael Maire | DeepAI

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Stella X. Yu
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Simon Kornblith
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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...

0 Xin Yuan, et al. ∙

∙ 05/31/2023

Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits

Proper evaluations are crucial for better understanding, troubleshooting...

0 Zhuokai Zhao, et al. ∙

∙ 12/14/2020

Information-Theoretic Segmentation by Inpainting Error Maximization

We study image segmentation from an information-theoretic perspective, p...

23 Pedro Savarese, et al. ∙

∙ 12/05/2020

Self-Supervised Visual Representation Learning from Hierarchical Grouping

We create a framework for bootstrapping visual representation learning f...

0 Xiao Zhang, et al. ∙

∙ 11/23/2020

Boosting Contrastive Self-Supervised Learning with False Negative Cancellation

Self-supervised representation learning has witnessed significant leaps ...

0 Tri Huynh, et al. ∙

∙ 08/15/2020

Orthogonalized SGD and Nested Architectures for Anytime Neural Networks

We propose a novel variant of SGD customized for training network archit...

0 Chengcheng Wan, et al. ∙

∙ 07/30/2020

Growing Efficient Deep Networks by Structured Continuous Sparsification

We develop an approach to training deep networks while dynamically adjus...

9 Xin Yuan, et al. ∙

∙ 04/04/2020

Pixel Consensus Voting for Panoptic Segmentation

The core of our approach, Pixel Consensus Voting, is a framework for ins...

9 Haochen Wang, et al. ∙

∙ 12/10/2019

Winning the Lottery with Continuous Sparsification

The Lottery Ticket Hypothesis from Frankle Carbin (2019) conjectures...

0 Pedro Savarese, et al. ∙

∙ 12/04/2019

Domain-independent Dominance of Adaptive Methods

From a simplified analysis of adaptive methods, we derive AvaGrad, a new...

6 Pedro Savarese, et al. ∙

∙ 10/31/2019

ALERT: Accurate Learning for Energy and Timeliness

An increasing number of software applications incorporate runtime Deep N...

0 Chengcheng Wan, et al. ∙

∙ 10/31/2019

ALERT: Accurate Anytime Learning for Energy and Timeliness

An increasing number of software applications incorporate runtime Deep N...

0 Chengcheng Wan, et al. ∙

∙ 06/13/2019

Multigrid Neural Memory

We introduce a novel architecture that integrates a large addressable me...

0 Tri Huynh, et al. ∙

∙ 02/26/2019

Learning Implicitly Recurrent CNNs Through Parameter Sharing

We introduce a parameter sharing scheme, in which different layers of a ...

0 Pedro Savarese, et al. ∙

∙ 04/05/2018

Regularizing Deep Networks by Modeling and Predicting Label Structure

We construct custom regularization functions for use in supervised train...

0 Mohammadreza Mostajabi, et al. ∙

∙ 12/13/2017

Self-Supervised Depth Learning for Urban Scene Understanding

As an agent moves through the world, the apparent motion of scene elemen...

0 Huaizu Jiang, et al. ∙

∙ 03/11/2017

Colorization as a Proxy Task for Visual Understanding

We investigate and improve self-supervision as a drop-in replacement for...

0 Gustav Larsson, et al. ∙

∙ 11/23/2016

Multigrid Neural Architectures

We propose a multigrid extension of convolutional neural networks (CNNs)...

0 Tsung-Wei Ke, et al. ∙

∙ 05/24/2016

FractalNet: Ultra-Deep Neural Networks without Residuals

We introduce a design strategy for neural network macro-architecture bas...

0 Gustav Larsson, et al. ∙

∙ 03/22/2016

Learning Representations for Automatic Colorization

We develop a fully automatic image colorization system. Our approach lev...

0 Gustav Larsson, et al. ∙

∙ 12/09/2015

Affinity CNN: Learning Pixel-Centric Pairwise Relations for Figure/Ground Embedding

Spectral embedding provides a framework for solving perceptual organizat...

0 Michael Maire, et al. ∙

∙ 12/08/2015

Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression

We introduce a new approach to intrinsic image decomposition, the task o...

0 Takuya Narihira, et al. ∙

∙ 10/16/2014

Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling

We frame the task of predicting a semantic labeling as a sparse reconstr...

0 Michael Maire, et al. ∙

∙ 05/01/2014

Microsoft COCO: Common Objects in Context

We present a new dataset with the goal of advancing the state-of-the-art...

0 Tsung-Yi Lin, et al. ∙

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