Andre Cornman — accepted AI-conference papers

2025

The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling

Andre Cornman, Jacob West-Roberts, Antonio Pedro Camargo, Simon Roux, Martin Beracochea, Milot Mirdita +2

ICLR 2025poster

Biological language model performance depends heavily on pretraining data quality, diversity, and size. While metagenomic datasets feature enormous biological diversity, their utilization as pretraining data has been limited due to challenges in data accessibility, quality filtering and deduplicatio…

Cited by 49 Source ↗PDF ↗Scholar ↗

2024

SceneDiffuser: Efficient and Controllable Driving Simulation Initialization and Rollout

Chiyu Max Jiang, Yijing Bai, Andre Cornman, Christopher Davis, Xiukun Huang, Hong Jeon +9

NeurIPS 2024poster

Simulation with realistic and interactive agents represents a key task for autonomous vehicle (AV) software development in order to test AV performance in prescribed, often long-tail scenarios. In this work, we propose SceneDiffuser, a scene-level diffusion prior for traffic simulation. We present a…

Cited by 11 Source ↗PDF ↗Scholar ↗

2023

MotionDiffuser: Controllable Multi-Agent Motion Prediction Using Diffusion

Chiyu “Max” Jiang, Andre Cornman, Cheolho Park, Benjamin Sapp, Yin Zhou, Dragomir Anguelov

CVPR 2023highlight

We present MotionDiffuser, a diffusion based representation for the joint distribution of future trajectories over multiple agents. Such representation has several key advantages: first, our model learns a highly multimodal distribution that captures diverse future outcomes. Second, the simple predi…

Cited by 126 Source ↗PDF ↗Scholar ↗

2022

JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Driving

Wenjie Luo, Cheol Park, Andre Cornman, Benjamin Sapp, Dragomir Anguelov

CoRL 2022poster

We propose \textit{JFP}, a Joint Future Prediction model that can learn to generate accurate and consistent multi-agent future trajectories. For this task, many different methods have been proposed to capture social interactions in the encoding part of the model, however, considerably less focus has…

Cited by 44 Source ↗Scholar ↗

2022

MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction

Balakrishnan Varadarajan, Ahmed Hefny, Avikalp Srivastava, Khaled S. Refaat, Nigamaa Nayakanti, Andre Cornman +5

ICRA 2022poster

Predicting the future behavior of road users is one of the most challenging and important problems in autonomous driving. Applying deep learning to this problem requires fusing heterogeneous world state in the form of rich perception signals and map information, and inferring highly multi-modal dist…

Cited by 366 Source ↗Scholar ↗