Search Results for author: Mina Alibeigi

Found 10 papers, 5 papers with code

FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients

no code implementations15 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.

Federated Learning

Contrastive Learning for Lane Detection via cross-similarity

2 code implementations16 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.

Contrastive Learning Lane Detection +1

FedVal: Different good or different bad in federated learning

1 code implementation6 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.

Fairness Federated Learning

Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving

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.

Autonomous Driving Diversity +4

DASS: Differentiable Architecture Search for Sparse neural networks

1 code implementation14 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.

Network Pruning

Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty

no code implementations18 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

Incremental learning of high-level concepts by imitation

no code implementations14 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.

Incremental Learning Vocal Bursts Intensity Prediction

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