1 citations · 1 across the 9 of their papers we have counts for
10 papers
Turning Video Models into Generalist Robot Policies
Sizhe Lester Li, Evan Kim, Xingjian Bai +4
Video generative models have emerged as a promising robotics backbone, capable of generating videos that depict the completion of complex tasks across embodiments and environments.…
End-to-End Training for Unified Tokenization and Latent Denoising
Shivam Duggal, Xingjian Bai, Zongze Wu +5
Latent diffusion models (LDMs) enable high-fidelity synthesis by operating in learned latent spaces. However, training state-of-the-art LDMs requires complex staging: a tokenizer m…
Causality in Video Diffusers is Separable from Denoising
Xingjian Bai, Guande He, Zhengqi Li +3
Causality -- referring to temporal, uni-directional cause-effect relationships between components -- underlies many complex generative processes, including videos, language, and ro…
GGBench: A Geometric Generative Reasoning Benchmark for Unified Multimodal Models
Jingxuan Wei, Caijun Jia, Xi Bai +7
The advent of Unified Multimodal Models (UMMs) signals a paradigm shift in artificial intelligence, moving from passive perception to active, cross-modal generation. Despite their…
Mean Flows for One-step Generative Modeling
Zhengyang Geng, Mingyang Deng, Xingjian Bai +2
We propose a principled and effective framework for one-step generative modeling. We introduce the notion of average velocity to characterize flow fields, in contrast to instantane…
Wasserstein distributional adversarial training for deep neural networks
Xingjian Bai, Guangyi He, Yifan Jiang +1
Design of adversarial attacks for deep neural networks, as well as methods of adversarial training against them, are subject of intense research. In this paper, we propose methods…
Unweighted Layered Graph Traversal: Passing a Crown via Entropy Maximization
Xingjian Bai, Christian Coester, Romain Cosson
Introduced by Papadimitriou and Yannakakis in 1989, layered graph traversal is a central problem in online algorithms and mobile computing that has been studied for several decades…
Fixed Point Diffusion Models
Xingjian Bai, Luke Melas-Kyriazi
We introduce the Fixed Point Diffusion Model (FPDM), a novel approach to image generation that integrates the concept of fixed point solving into the framework of diffusion-based g…
Sorting with Predictions
Xingjian Bai, Christian Coester
We explore the fundamental problem of sorting through the lens of learning-augmented algorithms, where algorithms can leverage possibly erroneous predictions to improve their effic…
Goodhart's Law in Reinforcement Learning
Jacek Karwowski, Oliver Hayman, Xingjian Bai +3
Implementing a reward function that perfectly captures a complex task in the real world is impractical. As a result, it is often appropriate to think of the reward function as a pr…