Search Results for author: Jesse Berent

Found 18 papers, 3 papers with code

InkSight: Offline-to-Online Handwriting Conversion by Learning to Read and Write

no code implementations8 Feb 2024 Blagoj Mitrevski, Arina Rak, Julian Schnitzler, Chengkun Li, Andrii Maksai, Jesse Berent, Claudiu Musat

Our work, InkSight, aims to bridge the gap by empowering physical note-takers to effortlessly convert their work (offline handwriting) to digital ink (online handwriting), a process we refer to as Derendering.


When does Privileged Information Explain Away Label Noise?

1 code implementation3 Mar 2023 Guillermo Ortiz-Jimenez, Mark Collier, Anant Nawalgaria, Alexander D'Amour, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou

Leveraging privileged information (PI), or features available during training but not at test time, has recently been shown to be an effective method for addressing label noise.

Massively Scaling Heteroscedastic Classifiers

no code implementations30 Jan 2023 Mark Collier, Rodolphe Jenatton, Basil Mustafa, Neil Houlsby, Jesse Berent, Effrosyni Kokiopoulou

Heteroscedastic classifiers, which learn a multivariate Gaussian distribution over prediction logits, have been shown to perform well on image classification problems with hundreds to thousands of classes.

Classification Contrastive Learning +1

Inkorrect: Online Handwriting Spelling Correction

no code implementations28 Feb 2022 Andrii Maksai, Henry Rowley, Jesse Berent, Claudiu Musat

We show that Inkorrect's Pareto frontier dominates the points that correspond to prior work.

Spelling Correction

Transfer and Marginalize: Explaining Away Label Noise with Privileged Information

no code implementations18 Feb 2022 Mark Collier, Rodolphe Jenatton, Efi Kokiopoulou, Jesse Berent

Supervised learning datasets often have privileged information, in the form of features which are available at training time but are not available at test time e. g. the ID of the annotator that provided the label.

Deep Classifiers with Label Noise Modeling and Distance Awareness

no code implementations6 Oct 2021 Vincent Fortuin, Mark Collier, Florian Wenzel, James Allingham, Jeremiah Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou

Uncertainty estimation in deep learning has recently emerged as a crucial area of interest to advance reliability and robustness in safety-critical applications.

Out-of-Distribution Detection

A Simple Probabilistic Method for Deep Classification under Input-Dependent Label Noise

no code implementations15 Mar 2020 Mark Collier, Basil Mustafa, Efi Kokiopoulou, Rodolphe Jenatton, Jesse Berent

By tuning the softmax temperature, we improve accuracy, log-likelihood and calibration on both image classification benchmarks with controlled label noise as well as Imagenet-21k which has naturally occurring label noise.

General Classification Image Classification +2

Ranking architectures using meta-learning

no code implementations26 Nov 2019 Alina Dubatovka, Efi Kokiopoulou, Luciano Sbaiz, Andrea Gesmundo, Gabor Bartok, Jesse Berent

However, it requires a large amount of computing resources and in order to alleviate this, a performance prediction network has been recently proposed that enables efficient architecture search by forecasting the performance of candidate architectures, instead of relying on actual model training.

Meta-Learning Neural Architecture Search

Flexible Multi-task Networks by Learning Parameter Allocation

no code implementations10 Oct 2019 Krzysztof Maziarz, Efi Kokiopoulou, Andrea Gesmundo, Luciano Sbaiz, Gabor Bartok, Jesse Berent

The binary allocation variables are learned jointly with the model parameters by standard back-propagation thanks to the Gumbel-Softmax reparametrization method.

Multi-Task Learning

Gumbel-Matrix Routing for Flexible Multi-task Learning

no code implementations25 Sep 2019 Krzysztof Maziarz, Efi Kokiopoulou, Andrea Gesmundo, Luciano Sbaiz, Gabor Bartok, Jesse Berent

We propose the Gumbel-Matrix routing, a novel multi-task routing method based on the Gumbel-Softmax, that is designed to learn fine-grained parameter sharing.

Multi-Task Learning

Cap2Det: Learning to Amplify Weak Caption Supervision for Object Detection

1 code implementation ICCV 2019 Keren Ye, Mingda Zhang, Adriana Kovashka, Wei Li, Danfeng Qin, Jesse Berent

Learning to localize and name object instances is a fundamental problem in vision, but state-of-the-art approaches rely on expensive bounding box supervision.

Object object-detection +1

Fast Task-Aware Architecture Inference

no code implementations15 Feb 2019 Efi Kokiopoulou, Anja Hauth, Luciano Sbaiz, Andrea Gesmundo, Gabor Bartok, Jesse Berent

At the core of our framework lies a deep value network that can predict the performance of input architectures on a task by utilizing task meta-features and the previous model training experiments performed on related tasks.

Computational Efficiency Neural Architecture Search

Learning to discover and localize visual objects with open vocabulary

no code implementations25 Nov 2018 Keren Ye, Mingda Zhang, Wei Li, Danfeng Qin, Adriana Kovashka, Jesse Berent

To alleviate the cost of obtaining accurate bounding boxes for training today's state-of-the-art object detection models, recent weakly supervised detection work has proposed techniques to learn from image-level labels.

Object object-detection +1

WebVision Challenge: Visual Learning and Understanding With Web Data

no code implementations16 May 2017 Wen Li, Li-Min Wang, Wei Li, Eirikur Agustsson, Jesse Berent, Abhinav Gupta, Rahul Sukthankar, Luc van Gool

The 2017 WebVision challenge consists of two tracks, the image classification task on WebVision test set, and the transfer learning task on PASCAL VOC 2012 dataset.

Benchmarking Image Classification +1

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