no code implementations • 15 Feb 2024 • Xinchi Qiu, Yan Gao, Lorenzo Sani, Heng Pan, Wanru Zhao, Pedro P. B. Gusmao, Mina Alibeigi, Alex Iacob, Nicholas D. Lane
Federated learning (FL) is a distributed learning paradigm that facilitates collaborative training of a shared global model across devices while keeping data localized.
2 code implementations • 16 Aug 2023 • Ali Zoljodi, Sadegh Abadijou, Mina Alibeigi, Masoud Daneshtalab
In this paper, we present a novel self-supervised learning method termed Contrastive Learning for Lane Detection via cross-similarity (CLLD) to enhance the resilience of lane detection models in real-world scenarios, particularly when the visibility of lanes is compromised.
Ranked #5 on
Lane Detection
on TuSimple
1 code implementation • ICCV 2023 • Yasar Abbas Ur Rehman, Yan Gao, Pedro Porto Buarque de Gusmão, Mina Alibeigi, Jiajun Shen, Nicholas D. Lane
The ubiquity of camera-enabled devices has led to large amounts of unlabeled image data being produced at the edge.
1 code implementation • 6 Jun 2023 • Viktor Valadi, Xinchi Qiu, Pedro Porto Buarque de Gusmão, Nicholas D. Lane, Mina Alibeigi
In this paper, we present a novel approach FedVal for both robustness and fairness that does not require any additional information from clients that could raise privacy concerns and consequently compromise the integrity of the FL system.
1 code implementation • ICCV 2023 • Mina Alibeigi, William Ljungbergh, Adam Tonderski, Georg Hess, Adam Lilja, Carl Lindstrom, Daria Motorniuk, Junsheng Fu, Jenny Widahl, Christoffer Petersson
The dataset is composed of Frames, Sequences, and Drives, designed to encompass both data diversity and support for spatio-temporal learning, sensor fusion, localization, and mapping.
no code implementations • 18 Feb 2023 • Hannes Eriksson, Debabrota Basu, Tommy Tram, Mina Alibeigi, Christos Dimitrakakis
Then, we propose a generic two-stage algorithm, MLEMTRL, to address the MTRL problem in discrete and continuous settings.
1 code implementation • 14 Jul 2022 • Hamid Mousavi, Mohammad Loni, Mina Alibeigi, Masoud Daneshtalab
In this paper, we propose a new method to search for sparsity-friendly neural architectures.
no code implementations • 18 Mar 2022 • Hannes Eriksson, Debabrota Basu, Mina Alibeigi, Christos Dimitrakakis
In existing literature, the risk in stochastic games has been studied in terms of the inherent uncertainty evoked by the variability of transitions and actions.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
no code implementations • 22 Feb 2021 • Hannes Eriksson, Debabrota Basu, Mina Alibeigi, Christos Dimitrakakis
In this paper, we consider risk-sensitive sequential decision-making in Reinforcement Learning (RL).
no code implementations • 14 Apr 2017 • Mina Alibeigi, Majid Nili Ahmadabadi, Babak Nadjar Araabi
In ILoCI, observed multimodal spatio-temporal demonstrations are incrementally abstracted and generalized based on both their perceptual and functional similarities during the imitation.