​
Login / Signup
Sara Babakniya
Publication Activity (10 Years)
Years Active: 2021-2024
Publications (10 Years): 9
Top Topics
Incremental Learning
Okapi Bm
Language Model
Deep Learning
Top Venues
CoRR
SN Comput. Sci.
ACM Trans. Intell. Syst. Technol.
Trans. Mach. Learn. Res.
</>
Publications
</>
Asal Mehradfar
,
Xuzhe Zhao
,
Yue Niu
,
Sara Babakniya
,
Mahdi Alesheikh
,
Hamidreza Aghasi
,
Salman Avestimehr
AICircuit: A Multi-Level Dataset and Benchmark for AI-Driven Analog Integrated Circuit Design.
CoRR
(2024)
Sara Babakniya
,
Zalan Fabian
,
Chaoyang He
,
Mahdi Soltanolkotabi
,
Salman Avestimehr
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks.
NeurIPS
(2023)
Sara Babakniya
,
Zalan Fabian
,
Chaoyang He
,
Mahdi Soltanolkotabi
,
Salman Avestimehr
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory.
CoRR
(2023)
Sara Babakniya
,
Zalan Fabian
,
Chaoyang He
,
Mahdi Soltanolkotabi
,
Salman Avestimehr
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks.
CoRR
(2023)
Sara Babakniya
,
Souvik Kundu
,
Saurav Prakash
,
Yue Niu
,
Salman Avestimehr
Revisiting Sparsity Hunting in Federated Learning: Why does Sparsity Consensus Matter?
Trans. Mach. Learn. Res.
2023 (2023)
Sara Babakniya
,
Ahmed Roushdy Elkordy
,
Yahya H. Ezzeldin
,
Qingfeng Liu
,
Kee-Bong Song
,
Mostafa El-Khamy
,
Salman Avestimehr
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models.
CoRR
(2023)
Sara Babakniya
,
Souvik Kundu
,
Saurav Prakash
,
Yue Niu
,
Salman Avestimehr
Federated Sparse Training: Lottery Aware Model Compression for Resource Constrained Edge.
CoRR
(2022)
Chien-Lun Chen
,
Sara Babakniya
,
Marco Paolieri
,
Leana Golubchik
Defending against Poisoning Backdoor Attacks on Federated Meta-learning.
ACM Trans. Intell. Syst. Technol.
13 (5) (2022)
Sourya Dey
,
Sara Babakniya
,
Saikrishna C. Kanala
,
Marco Paolieri
,
Leana Golubchik
,
Peter A. Beerel
,
Keith M. Chugg
Deep-n-Cheap: An Automated Efficient and Extensible Search Framework for Cost-Effective Deep Learning.
SN Comput. Sci.
2 (4) (2021)