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Found 11 papers, 3 papers with code
EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy
For uncertainty quantification, we efficiently model both aleatoric and epistemic uncertainty by learning discrete traction distributions and probability densities of the traction predictor's latent features.
Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits
To navigate in an environment safely and autonomously, robots must accurately estimate where obstacles are and how they move.
Semi-supervised 3D Object Detection via Temporal Graph Neural Networks
However, these detectors usually require training on large amounts of annotated data that is expensive and time-consuming to collect.
Active Safety Envelopes using Light Curtains with Probabilistic Guarantees
We use light curtains to estimate the safety envelope of a scene: a hypothetical surface that separates the robot from all obstacles.
Exploiting & Refining Depth Distributions With Triangulation Light Curtains
Active sensing through the use of Adaptive Depth Sensors is a nascent field, with potential in areas such as Advanced driver-assistance systems (ADAS).
Robust Instance Tracking via Uncertainty Flow
Current state-of-the-art trackers often fail due to distractorsand large object appearance changes.
Uncertainty-aware Self-supervised 3D Data Association
Instead, we propose leveraging vast unlabeled datasets by self-supervised metric learning of 3D object trackers, with a focus on data association.
Active Perception using Light Curtains for Autonomous Driving
Most real-world 3D sensors such as LiDARs perform fixed scans of the entire environment, while being decoupled from the recognition system that processes the sensor data.
Combining Deep Learning and Verification for Precise Object Instance Detection
For a reliable detection system, if a high confidence detection is made, we would want high certainty that the object has indeed been detected.
Autofocus Layer for Semantic Segmentation
We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.
Ranked #5 on
Brain Tumor Segmentation
on BRATS-2015
Measuring the reliability of MCMC inference with bidirectional Monte Carlo
Markov chain Monte Carlo (MCMC) is one of the main workhorses of probabilistic inference, but it is notoriously hard to measure the quality of approximate posterior samples.
