Search Results for author:
Found 10 papers, 5 papers with code
FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients
Federated learning (FL) is a distributed learning paradigm that facilitates collaborative training of a shared global model across devices while keeping data localized.
Contrastive Learning for Lane Detection via Cross-Similarity
CLLD is a novel multitask contrastive learning that trains lane detection approaches to detect lane markings even in low visible situations by integrating local feature contrastive learning (CL) with our new proposed operation cross-similarity.
Ranked #4 on
Lane Detection
on TuSimple
L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning
The ubiquity of camera-enabled devices has led to large amounts of unlabeled image data being produced at the edge.
FedVal: Different good or different bad in federated learning
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.
Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving
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.
Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer
Then, we propose a generic two-stage algorithm, MLEMTRL, to address the MTRL problem in discrete and continuous settings.
DASS: Differentiable Architecture Search for Sparse neural networks
In this paper, we propose a new method to search for sparsity-friendly neural architectures.
Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty
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
SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning
In this paper, we consider risk-sensitive sequential decision-making in Reinforcement Learning (RL).
Incremental learning of high-level concepts by imitation
In ILoCI, observed multimodal spatio-temporal demonstrations are incrementally abstracted and generalized based on both their perceptual and functional similarities during the imitation.
