Christopher Grimm

According to our database, Christopher Grimm authored at least 16 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Leveraging Transformer Decoder for Automotive Radar Object Detection.

Changxu Zhang, Zhaoze Wang, Tai Fei, Christopher Grimm, Yi Jin, Claas Tebruegge, Ernst Warsitz, Markus Gardill

CoRR, January, 2026

Synthetic FMCW Radar Range Azimuth Maps Augmentation with Generative Diffusion Model.

Zhaoze Wang, Changxu Zhang, Tai Fei, Christopher Grimm, Yi Jin, Claas Tebruegge, Ernst Warsitz, Markus Gardill

CoRR, January, 2026

2024

Training Neural Networks With In-Memory-Computing Hardware and Multi-Level Radix-4 Inputs.

Christopher Grimm, Jinseok Lee, Naveen Verma

IEEE Trans. Circuits Syst. I Regul. Pap., April, 2024

2022

The Value Equivalence Principle for Model-Based Reinforcement Learning

Christopher Grimm

PhD thesis, 2022

Warping of Radar Data Into Camera Image for Cross-Modal Supervision in Automotive Applications.

Christopher Grimm, Tai Fei, Ernst Warsitz, Ridha Farhoud, Tobias Breddermann, Reinhold Haeb-Umbach

IEEE Trans. Veh. Technol., 2022

Neural Network Training on In-Memory-Computing Hardware With Radix-4 Gradients.

Christopher Grimm, Naveen Verma

IEEE Trans. Circuits Syst. I Regul. Pap., 2022

Approximate Value Equivalence.

Christopher Grimm, André Barreto, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Proper Value Equivalence.

Christopher Grimm, André Barreto, Gregory Farquhar, David Silver, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

The Value Equivalence Principle for Model-Based Reinforcement Learning.

Christopher Grimm, André Barreto, Satinder Singh, David Silver

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Disentangled Cumulants Help Successor Representations Transfer to New Tasks.

Christopher Grimm, Irina Higgins, André Barreto, Denis Teplyashin, Markus Wulfmeier, Tim Hertweck, Raia Hadsell, Satinder Singh

CoRR, 2019

Learning Independently-Obtainable Reward Functions.

Christopher Grimm, Satinder Singh

CoRR, 2019

2018

Mitigating Planner Overfitting in Model-Based Reinforcement Learning.

Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman

CoRR, 2018

Deep Abstract Q-Networks.

Melrose Roderick, Christopher Grimm, Stefanie Tellex

Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 2018

2017

Learning Approximate Stochastic Transition Models.

Yuhang Song, Christopher Grimm, Xianming Wang, Michael L. Littman

CoRR, 2017

Summable Reparameterizations of Wasserstein Critics in the One-Dimensional Setting.

Christopher Grimm, Yuhang Song, Michael L. Littman

CoRR, 2017

Latent Attention Networks.

Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L. S. Wong, Michael L. Littman

CoRR, 2017