Sayna Ebrahimi | DeepAI

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∙ 05/26/2023

LANISTR: Multimodal Learning from Structured and Unstructured Data

Multimodal large-scale pretraining has shown impressive performance gain...

0 Sayna Ebrahimi, et al. ∙

∙ 04/07/2023

ASPEST: Bridging the Gap Between Active Learning and Selective Prediction

Selective prediction aims to learn a reliable model that abstains from m...

2 Jiefeng Chen, et al. ∙

∙ 11/28/2022

Beyond Invariance: Test-Time Label-Shift Adaptation for Distributions with "Spurious" Correlations

Spurious correlations, or correlations that change across domains where ...

0 Qingyao Sun, et al. ∙

∙ 06/15/2022

Test-Time Adaptation for Visual Document Understanding

Self-supervised pretraining has been able to produce transferable repres...

0 Sayna Ebrahimi, et al. ∙

∙ 04/21/2022

Contrastive Test-Time Adaptation

Test-time adaptation is a special setting of unsupervised domain adaptat...

0 Dian Chen, et al. ∙

∙ 09/02/2021

On-target Adaptation

Domain adaptation seeks to mitigate the shift between training on the so...

0 Dequan Wang, et al. ∙

∙ 08/20/2021

Region-level Active Learning for Cluttered Scenes

Active learning for object detection is conventionally achieved by apply...

1 Michael Laielli, et al. ∙

∙ 07/07/2021

Predicting with Confidence on Unseen Distributions

Recent work has shown that the performance of machine learning models ca...

25 Devin Guillory, et al. ∙

∙ 03/23/2021

Self-Supervised Pretraining Improves Self-Supervised Pretraining

While self-supervised pretraining has proven beneficial for many compute...

45 Colorado J. Reed, et al. ∙

∙ 12/18/2020

Minimax Active Learning

Active learning aims to develop label-efficient algorithms by querying t...

0 Sayna Ebrahimi, et al. ∙

∙ 10/04/2020

Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting

The goal of continual learning (CL) is to learn a sequence of tasks with...

9 Sayna Ebrahimi, et al. ∙

∙ 03/21/2020

Adversarial Continual Learning

Continual learning aims to learn new tasks without forgetting previously...

8 Sayna Ebrahimi, et al. ∙

∙ 09/23/2019

WiCV 2019: The Sixth Women In Computer Vision Workshop

In this paper we present the Women in Computer Vision Workshop - WiCV 20...

8 Irene Amerini, et al. ∙

∙ 06/06/2019

Uncertainty-guided Continual Learning with Bayesian Neural Networks

Continual learning aims to learn new tasks without forgetting previously...

0 Sayna Ebrahimi, et al. ∙

∙ 03/31/2019

Variational Adversarial Active Learning

Active learning aims to develop label-efficient algorithms by sampling t...

0 Samarth Sinha, et al. ∙

∙ 12/05/2018

Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders

Many approaches in generalized zero-shot learning rely on cross-modal ma...

0 Edgar Schönfeld, et al. ∙

∙ 07/19/2018

Compositional GAN: Learning Conditional Image Composition

Generative Adversarial Networks (GANs) can produce images of surprising ...

16 Samaneh Azadi, et al. ∙

∙ 10/16/2017

Gradient-free Policy Architecture Search and Adaptation

We develop a method for policy architecture search and adaptation via gr...

0 Sayna Ebrahimi, et al. ∙