14 citations · 41 across the 9 of their papers we have counts for
9 papers
FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients
Xinchi Qiu, Yan Gao, Lorenzo Sani +6
Federated learning (FL) is a distributed learning paradigm that facilitates collaborative training of a shared global model across devices while keeping data localized. The deploym…
Contrastive Learning for Lane Detection via cross-similarity
Ali Zoljodi, Sadegh Abadijou, Mina Alibeigi +1
Detecting lane markings in road scenes poses a challenge due to their intricate nature, which is susceptible to unfavorable conditions. While lane markings have strong shape priors…
L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning
Yasar Abbas Ur Rehman, Yan Gao, Pedro Porto Buarque de Gusmão +3
The ubiquity of camera-enabled devices has led to large amounts of unlabeled image data being produced at the edge. The integration of self-supervised learning (SSL) and federated…
Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving
Mina Alibeigi, William Ljungbergh, Adam Tonderski +7
Existing datasets for autonomous driving (AD) often lack diversity and long-range capabilities, focusing instead on 360° perception and temporal reasoning. To address this gap, we…
Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer
Hannes Eriksson, Debabrota Basu, Tommy Tram +2
In this paper, we study the problem of transferring the available Markov Decision Process (MDP) models to learn and plan efficiently in an unknown but similar MDP. We refer to it a…
DASS: Differentiable Architecture Search for Sparse neural networks
Hamid Mousavi, Mohammad Loni, Mina Alibeigi +1
The deployment of Deep Neural Networks (DNNs) on edge devices is hindered by the substantial gap between performance requirements and available processing power. While recent resea…
Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty
Hannes Eriksson, Debabrota Basu, Mina Alibeigi +1
In stochastic games with incomplete information, the uncertainty is evoked by the lack of knowledge about a player's own and the other players' types, i.e. the utility function and…
SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning
Hannes Eriksson, Debabrota Basu, Mina Alibeigi +1
In this paper, we consider risk-sensitive sequential decision-making in Reinforcement Learning (RL). Our contributions are two-fold. First, we introduce a novel and coherent quanti…
Incremental learning of high-level concepts by imitation
Mina Alibeigi, Majid Nili Ahmadabadi, Babak Nadjar Araabi
Nowadays, robots become a companion in everyday life. To be well-accepted by humans, robots should efficiently understand meanings of their partners' motions and body language, and…