95 citations · 118 across the 12 of their papers we have counts for
12 papers
BridgeDrive: Diffusion Bridge Policy for Closed-Loop Trajectory Planning in Autonomous Driving
Shu Liu, Wenlin Chen, Weihao Li +7
Diffusion-based planners have shown strong potential for autonomous driving by capturing multi-modal driving behaviors. A key challenge is how to effectively guide these models for…
Progressive Tempering Sampler with Diffusion
Severi Rissanen, RuiKang OuYang, Jiajun He +4
Recent research has focused on designing neural samplers that amortize the process of sampling from unnormalized densities. However, despite significant advancements, they still fa…
DiffRatio: Training One-Step Diffusion Models Without Teacher Supervision
Wenlin Chen, Mingtian Zhang, Jiajun He +4
Score-based distillation methods (e.g., variational score distillation) train one-step diffusion models by first pre-training a teacher score model and then distilling it into a on…
Your Image is Secretly the Last Frame of a Pseudo Video
Wenlong Chen, Wenlin Chen, Lapo Rastrelli +1
Diffusion models, which can be viewed as a special case of hierarchical variational autoencoders (HVAEs), have shown profound success in generating photo-realistic images. In contr…
Training Neural Samplers with Reverse Diffusive KL Divergence
Jiajun He, Wenlin Chen, Mingtian Zhang +2
Training generative models to sample from unnormalized density functions is an important and challenging task in machine learning. Traditional training methods often rely on the re…
Diffusive Gibbs Sampling
Wenlin Chen, Mingtian Zhang, Brooks Paige +2
The inadequate mixing of conventional Markov Chain Monte Carlo (MCMC) methods for multi-modal distributions presents a significant challenge in practical applications such as Bayes…
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation
Wen Wu, Wenlin Chen, Chao Zhang +1
Human annotator simulation (HAS) serves as a cost-effective substitute for human evaluation such as data annotation and system assessment. Human perception and behaviour during hum…
Leveraging Task Structures for Improved Identifiability in Neural Network Representations
Wenlin Chen, Julien Horwood, Juyeon Heo +1
This work extends the theory of identifiability in supervised learning by considering the consequences of having access to a distribution of tasks. In such cases, we show that line…
Neural Characteristic Activation Analysis and Geometric Parameterization for ReLU Networks
Wenlin Chen, Hong Ge
We introduce a novel approach for analyzing the training dynamics of ReLU networks by examining the characteristic activation boundaries of individual ReLU neurons. Our proposed an…
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction
Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato
We propose Adaptive Deep Kernel Fitting with Implicit Function Theorem (ADKF-IFT), a novel framework for learning deep kernel Gaussian processes (GPs) by interpolating between meta…
Optimal Client Sampling for Federated Learning
Wenlin Chen, Samuel Horvath, Peter Richtarik
It is well understood that client-master communication can be a primary bottleneck in Federated Learning. In this work, we address this issue with a novel client subsampling scheme…
To Ensemble or Not Ensemble: When does End-To-End Training Fail?
Andrew M. Webb, Charles Reynolds, Wenlin Chen +4
End-to-End training (E2E) is becoming more and more popular to train complex Deep Network architectures. An interesting question is whether this trend will continue-are there any c…