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∙ 03/24/2023

Leveraging Old Knowledge to Continually Learn New Classes in Medical Images

Class-incremental continual learning is a core step towards developing a...

0 Evelyn Chee, et al. ∙

∙ 12/22/2022

Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation

To reproduce the success of text-to-image (T2I) generation, recent works...

3 Jay Zhangjie Wu, et al. ∙

∙ 11/04/2022

A Deep Learning Approach to Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data

Solar activity is usually caused by the evolution of solar magnetic fiel...

0 Haodi Jiang, et al. ∙

∙ 10/08/2022

Inferring Line-of-Sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks

Obtaining high-quality magnetic and velocity fields through Stokes inver...

0 Haodi Jiang, et al. ∙

∙ 06/01/2022

Label-Efficient Online Continual Object Detection in Streaming Video

To thrive in evolving environments, humans are capable of continual acqu...

0 Jay Zhangjie Wu, et al. ∙

∙ 07/25/2021

Distributional Shifts in Automated Diabetic Retinopathy Screening

Deep learning-based models are developed to automatically detect if a re...

14 Jay Nandy, et al. ∙

∙ 07/21/2021

Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies

Objective: Temporal electronic health records (EHRs) can be a wealth of ...

4 Feng Xie, et al. ∙

∙ 06/24/2021

Towards Fully Interpretable Deep Neural Networks: Are We There Yet?

Despite the remarkable performance, Deep Neural Networks (DNNs) behave a...

0 Sandareka Wickramanayake, et al. ∙

∙ 02/09/2021

Adversarially Robust Classifier with Covariate Shift Adaptation

Existing adversarially trained models typically perform inference on tes...

9 Jay Nandy, et al. ∙

∙ 01/11/2021

Learning Semantically Meaningful Features for Interpretable Classifications

Learning semantically meaningful features is important for Deep Neural N...

20 Sandareka Wickramanayake, et al. ∙

∙ 10/20/2020

Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples

Among existing uncertainty estimation approaches, Dirichlet Prior Networ...

8 Jay Nandy, et al. ∙

∙ 08/28/2020

PREMIER: Personalized REcommendation for Medical prescrIptions from Electronic Records

The broad adoption of Electronic Health Records (EHR) has led to vast am...

0 Suman Bhoi, et al. ∙

∙ 04/05/2020

Approximate Manifold Defense Against Multiple Adversarial Perturbations

Existing defenses against adversarial attacks are typically tailored to ...

0 Jay Nandy, et al. ∙

∙ 05/14/2018

Normal Similarity Network for Generative Modelling

Gaussian distributions are commonly used as a key building block in many...

2 Jay Nandy, et al. ∙

∙ 05/15/2017

Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach

User opinions expressed in the form of ratings can influence an individu...

0 Lahari Poddar, et al. ∙

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