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07/27/2023
To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation
The goal of Online Domain Adaptation for semantic segmentation is to han...
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04/06/2023
Logistic-Normal Likelihoods for Heteroscedastic Label Noise in Classification
A natural way of estimating heteroscedastic label noise in regression is...
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03/01/2023
Predicting the wall-shear stress and wall pressure through convolutional neural networks
The objective of this study is to assess the capability of convolution-b...
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01/28/2023
On the Lipschitz Constant of Deep Networks and Double Descent
Existing bounds on the generalization error of deep networks assume some...
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10/18/2022
Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks
We propose Dense FixMatch, a simple method for online semi-supervised le...
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09/21/2022
Deep Double Descent via Smooth Interpolation
Overparameterized deep networks are known to be able to perfectly fit th...
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08/10/2022
PatchDropout: Economizing Vision Transformers Using Patch Dropout
Vision transformers have demonstrated the potential to outperform CNNs i...
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03/11/2022
Towards Self-Supervised Learning of Global and Object-Centric Representations
Self-supervision allows learning meaningful representations of natural i...
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02/23/2022
Are All Linear Regions Created Equal?
The number of linear regions has been studied as a proxy of complexity f...
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01/03/2022
An analysis of over-sampling labeled data in semi-supervised learning with FixMatch
Most semi-supervised learning methods over-sample labeled data when cons...
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12/02/2021
CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer
Interval and large invasive breast cancers, which are associated with wo...
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10/04/2021
Consistency Regularization Can Improve Robustness to Label Noise
Consistency regularization is a commonly-used technique for semi-supervi...
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05/10/2021
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels
We propose two novel loss functions based on Jensen-Shannon divergence f...
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07/11/2020
Decoupling Inherent Risk and Early Cancer Signs in Image-based Breast Cancer Risk Models
The ability to accurately estimate risk of developing breast cancer woul...
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06/16/2020
Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks
Visual relationship detection is fundamental for holistic image understa...
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03/17/2020
Hyperplane Arrangements of Trained ConvNets Are Biased
We investigate the geometric properties of the functions learned by trai...
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02/04/2020
On the use of recurrent neural networks for predictions of turbulent flows
In this paper, the prediction capabilities of recurrent neural networks ...
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06/12/2019
Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation
In this work we aim to obtain computationally-efficient uncertainty esti...
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05/31/2019
Explainability Techniques for Graph Convolutional Networks
Graph Networks are used to make decisions in potentially complex scenari...
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04/30/2019
The role of artificial intelligence in achieving the Sustainable Development Goals
The emergence of artificial intelligence (AI) and its progressively wide...
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02/18/2018
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
Deep neural networks have led to a series of breakthroughs, dramatically...
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07/08/2015
Spotlight the Negatives: A Generalized Discriminative Latent Model
Discriminative latent variable models (LVM) are frequently applied to va...
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11/24/2014
Persistent Evidence of Local Image Properties in Generic ConvNets
Supervised training of a convolutional network for object classification...
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06/22/2014
Factors of Transferability for a Generic ConvNet Representation
Evidence is mounting that Convolutional Networks (ConvNets) are the most...
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05/22/2014