16 papers
How Can Driving World Models Do Counterfactual Prediction?
Jiaru Zhang, Can Cui, Yi Xu +3
Driving world models are often interpreted as counterfactual simulators for observed driving episodes: given a factual driving log, they are asked what would have happened under an…
Test Time Training for Supervised Causal Learning
Zizhen Deng, Jiaru Zhang, Rui Ding +5
Supervised Causal Learning (SCL) has shown promise in causal discovery by framing it as a supervised learning problem. However, it suffers from significant out-of-distribution gene…
Accelerating Inference of Discrete Autoregressive Normalizing Flows by Selective Jacobi Decoding
Jiaru Zhang, Juanwu Lu, Xiaoyu Wu +2
Discrete normalizing flows are promising generative models with advantages such as analytical log-likelihood computation and end-to-end training. However, the architectural constra…
Analytical Correction for Subsampling Bias in Drifting Models
Jiaru Zhang, Zeyun Deng, Juanwu Lu +2
Drifting models are capable one-step generative models trained to follow a drifting field. The field combines attractive and repulsive softmax-weighted centroids over the data and…
LLM4AD: Large Language Models for Autonomous Driving -- Concept, Review, Benchmark, Experiments, and Future Trends
Can Cui, Yunsheng Ma, Sung-Yeon Park +14
With the broader adoption and highly successful development of Large Language Models (LLMs), there has been growing interest and demand for applying LLMs to autonomous driving tech…
FedMomentum: Preserving LoRA Training Momentum in Federated Fine-Tuning
Peishen Yan, Yang Hua, Hao Wang +4
Federated fine-tuning of large language models (LLMs) with low-rank adaptation (LoRA) offers a communication-efficient and privacy-preserving solution for task-specific adaptation.…