Search Results for author:
Found 17 papers, 4 papers with code
Learning to Drive via Asymmetric Self-Play
Large-scale data is crucial for learning realistic and capable driving policies.
Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them
Through our comprehensive review and analysis, this paper seeks to contribute to the ongoing discourse on ML-based IoT security, offering valuable insights and practical solutions to secure ML models and data in the rapidly expanding field of artificial intelligence in IoT.
Learning Realistic Traffic Agents in Closed-loop
Our experiments show that RTR learns more realistic and generalizable traffic simulation policies, achieving significantly better tradeoffs between human-like driving and traffic compliance in both nominal and long-tail scenarios.
MixSim: A Hierarchical Framework for Mixed Reality Traffic Simulation
Towards this goal, we propose to leverage the wealth of interesting scenarios captured in the real world and make them reactive and controllable to enable closed-loop SDV evaluation in what-if situations.
GoRela: Go Relative for Viewpoint-Invariant Motion Forecasting
However, this approach is computationally expensive for multi-agent prediction as inference needs to be run for each agent.
Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-Contrast CT Scans
In this paper, we propose a novel Asymmetry Disentanglement Network (ADN) to automatically separate pathological asymmetries and intrinsic anatomical asymmetries in NCCTs for more effective and interpretable AIS segmentation.
Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation
We also propose a new mixture-of-experts (MoE) based decoder, which treats the feature maps from the encoder as experts and selects a suitable set of expert features to predict the label for each pixel.
DeepStroke: An Efficient Stroke Screening Framework for Emergency Rooms with Multimodal Adversarial Deep Learning
In an emergency room (ER) setting, stroke triage or screening is a common challenge.
LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images
Thus, it is not ideal to apply most existing segmentation methods as they are designed for either 2D or 3D images.
Non-parametric Memory for Spatio-Temporal Segmentation of Construction Zones for Self-Driving
In this paper, we introduce a non-parametric memory representation for spatio-temporal segmentation that captures the local space and time around an autonomous vehicle (AV).
SceneGen: Learning to Generate Realistic Traffic Scenes
Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics and are limited in their ability to model the true complexity and diversity of real traffic scenes, thus inducing a content gap between synthesized traffic scenes versus real ones.
MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models
Our model exploits spatio-temporal relationships across multiple LiDAR sweeps to reduce the bitrate of both geometry and intensity values.
Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction
We present a novel method for testing the safety of self-driving vehicles in simulation.
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
We first utilize ray casting over the 3D scene and then use a deep neural network to produce deviations from the physics-based simulation, producing realistic LiDAR point clouds.
OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression
We present a novel deep compression algorithm to reduce the memory footprint of LiDAR point clouds.
Identifying Unknown Instances for Autonomous Driving
In the past few years, we have seen great progress in perception algorithms, particular through the use of deep learning.
Deformable Filter Convolution for Point Cloud Reasoning
Point clouds are the native output of many real-world 3D sensors.
