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Found 12 papers, 6 papers with code
Beyond the Mud: Datasets and Benchmarks for Computer Vision in Off-Road Racing
With these datasets and analysis of model limitations, we aim to foster innovations in handling real-world conditions like mud and complex poses to drive progress in robust computer vision.
Optical Character Recognition
Optical Character Recognition (OCR)
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Reading Between the Mud: A Challenging Motorcycle Racer Number Dataset
RnD represents a valuable new benchmark to drive innovation in real-world OCR capabilities.
Optical Character Recognition
Optical Character Recognition (OCR)
MUDD: A New Re-Identification Dataset with Efficient Annotation for Off-Road Racers in Extreme Conditions
We analyze the impact of real-world factors including mud, pose, lighting, and more.
Parity Calibration
In a sequential regression setting, a decision-maker may be primarily concerned with whether the future observation will increase or decrease compared to the current one, rather than the actual value of the future observation.
Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning
Without manually-designed controller nor human demonstrations, we demonstrate that with careful Sim2Real considerations, our policies trained with RL in simulation enable two xArm6 robots to solve the U-shape assembly task with a success rate of above90% in simulation, and 50% on real hardware without any additional real-world fine-tuning.
How Useful are Gradients for OOD Detection Really?
One critical challenge in deploying highly performant machine learning models in real-life applications is out of distribution (OOD) detection.
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification
With increasing deployment of machine learning systems in various real-world tasks, there is a greater need for accurate quantification of predictive uncertainty.
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification
However, this loss restricts the scope of applicable regression models, limits the ability to target many desirable properties (e. g. calibration, sharpness, centered intervals), and may produce poor conditional quantiles.
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction
We introduce Neural Dynamical Systems (NDS), a method of learning dynamical models in various gray-box settings which incorporates prior knowledge in the form of systems of ordinary differential equations.
Neural Dynamical Systems
We introduce Neural Dynamical Systems (NDS), a method of learning dynamical models which incorporates prior knowledge in the form of systems of ordinary differential equations.
Offline Contextual Bayesian Optimization for Nuclear Fusion
One obstacle in utilizing fusion as a feasible energy source is the stability of the reaction.
Offline Contextual Bayesian Optimization
In black-box optimization, an agent repeatedly chooses a configuration to test, so as to find an optimal configuration.
