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Found 7 papers, 6 papers with code
Beyond The Rainbow: High Performance Deep Reinforcement Learning On A Desktop PC
Rainbow Deep Q-Network (DQN) demonstrated combining multiple independent enhancements could significantly boost a reinforcement learning (RL) agent's performance.
Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification
Land cover classification (LCC), and monitoring how land use changes over time, is an important process in climate change mitigation and adaptation.
Trust in Language Grounding: a new AI challenge for human-robot teams
The challenge of language grounding is to fully understand natural language by grounding language in real-world referents.
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning
We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Markovian rewards.
Model Agnostic Interpretability for Multiple Instance Learning
In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag.
The LOCATA Challenge: Acoustic Source Localization and Tracking
The aim of the LOCAlization and TrAcking (LOCATA) Challenge is an open-access framework for the objective evaluation and benchmarking of broad classes of algorithms for sound source localization and tracking.
Data Augmentation of Room Classifiers using Generative Adversarial Networks
A representation of acoustic environments is proposed, which is used to train the GANs.
Audio and Speech Processing Sound
