Mohammad Rostami

research

∙ 08/15/2023

Improved Region Proposal Network for Enhanced Few-Shot Object Detection

Despite significant success of deep learning in object detection tasks, ...

0 Zeyu Shangguan, et al. ∙

research

∙ 05/30/2023

History Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completion

Temporal knowledge graph (TKG) completion models typically rely on havin...

4 Mehrnoosh Mirtaheri, et al. ∙

research

∙ 05/28/2023

Cognitively Inspired Cross-Modal Data Generation Using Diffusion Models

Most existing cross-modal generative methods based on diffusion models u...

0 Zizhao Hu, et al. ∙

research

∙ 04/05/2023

Low-Shot Learning for Fictional Claim Verification

In this paper, we study the problem of claim verification in the context...

0 Viswanath Chadalapaka, et al. ∙

research

∙ 04/04/2023

I2I: Initializing Adapters with Improvised Knowledge

Adapters present a promising solution to the catastrophic forgetting pro...

5 Tejas Srinivasan, et al. ∙

research

∙ 03/26/2023

Explainable Artificial Intelligence Architecture for Melanoma Diagnosis Using Indicator Localization and Self-Supervised Learning

Melanoma is a prevalent lethal type of cancer that is treatable if diagn...

0 Ruitong Sun, et al. ∙

research

∙ 03/25/2023

Task-Attentive Transformer Architecture for Continual Learning of Vision-and-Language Tasks Using Knowledge Distillation

The size and the computational load of fine-tuning large-scale pre-train...

0 Yuliang Cai, et al. ∙

research

∙ 03/22/2023

Unsupervised Domain Adaptation for Training Event-Based Networks Using Contrastive Learning and Uncorrelated Conditioning

Event-based cameras offer reliable measurements for preforming computer ...

0 Dayuan Jian, et al. ∙

research

∙ 03/22/2023

Encoding Binary Concepts in the Latent Space of Generative Models for Enhancing Data Representation

Binary concepts are empirically used by humans to generalize efficiently...

0 Zizhao Hu, et al. ∙

research

∙ 03/18/2023

Identification of Novel Classes for Improving Few-Shot Object Detection

Conventional training of deep neural networks requires a large number of...

0 Zeyu Shangguan, et al. ∙

research

∙ 01/29/2023

Preserving Fairness in AI under Domain Shift

Existing algorithms for ensuring fairness in AI use a single-shot traini...

0 Serban Stan, et al. ∙

research

∙ 01/29/2023

Unsupervised Domain Adaptation for Graph-Structured Data Using Class-Conditional Distribution Alignment

Adopting deep learning models for graph-structured data is challenging d...

0 Mengxi Wu, et al. ∙

research

∙ 11/02/2022

Unsupervised Model Adaptation for Source-free Segmentation of Medical Images

The recent prevalence of deep neural networks has lead semantic segmenta...

0 Serban Stan, et al. ∙

research

∙ 09/29/2022

Increasing Model Generalizability for Unsupervised Domain Adaptation

A dominant approach for addressing unsupervised domain adaptation is to ...

0 Mohammad Rostami, et al. ∙

research

∙ 07/10/2022

Automating Detection of Papilledema in Pediatric Fundus Images with Explainable Machine Learning

Papilledema is an ophthalmic neurologic disorder in which increased intr...

18 Kleanthis Avramidis, et al. ∙

research

∙ 06/18/2022

CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks

Current state-of-the-art vision-and-language models are evaluated on tas...

7 Tejas Srinivasan, et al. ∙

research

∙ 10/09/2021

Cognitively Inspired Learning of Incremental Drifting Concepts

Humans continually expand their learned knowledge to new domains and lea...

0 Mohammad Rostami, et al. ∙

research

∙ 08/27/2021

Detection and Continual Learning of Novel Face Presentation Attacks

Advances in deep learning, combined with availability of large datasets,...

7 Mohammad Rostami, et al. ∙

research

∙ 07/04/2021

Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separation

Sentiment analysis is a costly yet necessary task for enterprises to stu...

5 Mohammad Rostami, et al. ∙

research

∙ 06/23/2021

Secure Domain Adaptation with Multiple Sources

Multi-source unsupervised domain adaptation (MUDA) is a recently explore...

0 Serban Stan, et al. ∙

research

∙ 04/18/2021

Lifelong Learning of Few-shot Learners across NLP Tasks

Recent advances in large pre-trained language models have greatly improv...

0 Xisen Jin, et al. ∙

research

∙ 01/02/2021

Privacy Preserving Domain Adaptation for Semantic Segmentation of Medical Images

Convolutional neural networks (CNNs) have led to significant improvement...

4 Serban Stan, et al. ∙

research

∙ 09/26/2020

Unsupervised Model Adaptation for Continual Semantic Segmentation

We develop an algorithm for adapting a semantic segmentation model that ...

4 Serban Stan, et al. ∙

research

∙ 07/01/2020

Sequential Unsupervised Domain Adaptation through Prototypical Distributions

We develop an algorithm for unsupervised domain adaptation (UDA) of a cl...

19 Mohammad Rostami, et al. ∙

research

∙ 07/04/2019

Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment

We address the problem of unsupervised domain adaptation (UDA) by learni...

0 Alex Gabourie, et al. ∙

research

∙ 06/10/2019

Generative Continual Concept Learning

After learning a concept, humans are also able to continually generalize...

0 Mohammad Rostami, et al. ∙

research

∙ 03/11/2019

Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay

Despite huge success, deep networks are unable to learn effectively in s...

0 Mohammad Rostami, et al. ∙

research

∙ 10/10/2017

Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer

Knowledge transfer between tasks can improve the performance of learned ...

0 David Isele, et al. ∙

research

∙ 09/12/2017

Joint Dictionaries for Zero-Shot Learning

A classic approach toward zero-shot learning (ZSL) is to map the input d...

0 Soheil Kolouri, et al. ∙

research

∙ 03/22/2016

Image Super-Resolution Based on Sparsity Prior via Smoothed l_0 Norm

In this paper we aim to tackle the problem of reconstructing a high-reso...

0 Mohammad Rostami, et al. ∙

research

∙ 03/08/2016

Testing fine-grained parallelism for the ADMM on a factor-graph

There is an ongoing effort to develop tools that apply distributed compu...

0 Ning Hao, et al. ∙

Mohammad Rostami

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