27 citations · 62 across the 21 of their papers we have counts for
7 papers · 1 filter
On Variance Reduction in Learning Mean Flows
Juanwu Lu, Ziran Wang
One-step generative modeling has emerged as a leading approach for amortizing the inference cost of diffusion and flow-matching models. Among distillation-free methods, MeanFlow tr…
Model Merging on Loss Landscape: A Geometry Perspective
Juanwu Lu, Anand Bhaskar, Brian Axelrod +2
Model merging offers a promising avenue for knowledge integration and parallel development without retraining. Yet, existing methods either ignore the geometry of the loss landscap…
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…
Mollified Value Learning
Hrishikesh Viswanath, Juanwu Lu, S. Talha Bukhari +4
Offline goal-conditioned reinforcement learning (GCRL) learns goal-reaching behaviors from static datasets, but accurate value estimation remains challenging under limited state-ac…
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…
Generalizability Analysis of Graph-based Trajectory Predictor with Vectorized Representation
Juanwu Lu, Wei Zhan, Masayoshi Tomizuka +1
Trajectory prediction is one of the essential tasks for autonomous vehicles. Recent progress in machine learning gave birth to a series of advanced trajectory prediction algorithms…
An Automated Machine Learning (AutoML) Method for Driving Distraction Detection Based on Lane-Keeping Performance
Chen Chai, Juanwu Lu, Xuan Jiang +2
With the enrichment of smartphones, driving distractions caused by phone usages have become a threat to driving safety. A promising way to mitigate driving distractions is to detec…