lsmcolab
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Semantic-Segmentation-under-Adverse-Conditions
Semantic-Segmentation-under-Adverse-Conditions PublicOriginal PyTorch implementation of the paper "Semantic Segmentation under Adverse Conditions: A Weather and Nighttime-aware Synthetic Data-based Approach", published at the British Machine Vision C…
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adleap-mas
adleap-mas PublicThe AdLeap-MAS project proposes a new framework for implementing and simulating Ad-hoc reasoning environments. Built 100% in Python using the Open-AI Gym as its basis, we propose a component-based …
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bae-adversary-detection
bae-adversary-detection PublicBAE is a novel and efficient framework for online planning in ad-hoc teamwork capable of performing estimations about an adversarial agent disguised as a teammate. Our approach considers the identi…
Repositories
- oraa Public
lsmcolab/oraa’s past year of commit activity - DBO-MAB Public
lsmcolab/DBO-MAB’s past year of commit activity - Semantic-Segmentation-under-Adverse-Conditions Public
Original PyTorch implementation of the paper "Semantic Segmentation under Adverse Conditions: A Weather and Nighttime-aware Synthetic Data-based Approach", published at the British Machine Vision Conference (BMVC) 2022.
lsmcolab/Semantic-Segmentation-under-Adverse-Conditions’s past year of commit activity - Silver Public
lsmcolab/Silver’s past year of commit activity - bae-adversary-detection Public
BAE is a novel and efficient framework for online planning in ad-hoc teamwork capable of performing estimations about an adversarial agent disguised as a teammate. Our approach considers the identification of the impostor through a process we term Q-valued Bayesian Estimations. Publicly available to foment new solutions! Cite us :)
lsmcolab/bae-adversary-detection’s past year of commit activity - ib-pomcp Public
IB-POMCP is a novel algorithm for online planning under partial observability, presenting a fresh approach that enhances the decision-making process using the estimations of the world belief's entropy to guide a tree search process and surpass the limitations of sparse reward. Publicly available to foment new solutions! Cite us :)
lsmcolab/ib-pomcp’s past year of commit activity - adleap-mas Public
The AdLeap-MAS project proposes a new framework for implementing and simulating Ad-hoc reasoning environments. Built 100% in Python using the Open-AI Gym as its basis, we propose a component-based architecture to enable quick implementations to perform multi-agents systems experiments.
lsmcolab/adleap-mas’s past year of commit activity - dss-has Public
lsmcolab/dss-has’s past year of commit activity
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