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Found 8 papers, 3 papers with code
A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications
For this reason, we previously created the RFRL Gym: a standardized, accessible tool for the development and testing of reinforcement learning (RL) algorithms in the wireless communications space.
Lifelong Learning of Video Diffusion Models From a Single Video Stream
This work demonstrates that training autoregressive video diffusion models from a single, continuous video stream is not only possible but remarkably can also be competitive with standard offline training approaches given the same number of gradient steps.
TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters
Moreover, because of the high variability between different problems presented in different autonomous systems, these simulators need to be easy to use, and easy to modify.
Semantically Consistent Video Inpainting with Conditional Diffusion Models
Current state-of-the-art methods for video inpainting typically rely on optical flow or attention-based approaches to inpaint masked regions by propagating visual information across frames.
Nearest Neighbour Score Estimators for Diffusion Generative Models
Score function estimation is the cornerstone of both training and sampling from diffusion generative models.
Algorithms for Non-Negative Matrix Factorization on Noisy Data With Negative Values
In this paper we present two algorithms, Shift-NMF and Nearly-NMF, that can handle both the noisiness of the input data and also any introduced negativity.
Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models
When training object detection models on synthetic data, it is important to make the distribution of synthetic data as close as possible to the distribution of real data.
Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images
The development of algorithms that learn multi-agent behavioral models using human demonstrations has led to increasingly realistic simulations in the field of autonomous driving.
