290 citations · 947 across the 26 of their papers we have counts for
11 papers · 1 filter
Multi-task multi-station earthquake monitoring: An all-in-one seismic Phase picking, Location, and Association Network (PLAN)
Xu Si, Xinming Wu, Zefeng Li +2
Earthquake monitoring is vital for understanding the physics of earthquakes and assessing seismic hazards. A standard monitoring workflow includes the interrelated and interdepende…
Training Transformers with 4-bit Integers
Haocheng Xi, Changhao Li, Jianfei Chen +1
Quantizing the activation, weight, and gradient to 4-bit is promising to accelerate neural network training. However, existing 4-bit training methods require custom numerical forma…
Stabilizing GANs' Training with Brownian Motion Controller
Tianjiao Luo, Ziyu Zhu, Jianfei Chen +1
The training process of generative adversarial networks (GANs) is unstable and does not converge globally. In this paper, we examine the stability of GANs from the perspective of c…
Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models
Guande He, Jianfei Chen, Jun Zhu
Large pre-trained language models (PLMs) have demonstrated strong performance on natural language understanding (NLU) tasks through fine-tuning. However, fine-tuned models still su…
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning
Cheng Lu, Huayu Chen, Jianfei Chen +3
Guided sampling is a vital approach for applying diffusion models in real-world tasks that embeds human-defined guidance during the sampling procedure. This paper considers a gener…
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
Zhengyi Wang, Cheng Lu, Yikai Wang +4
Score distillation sampling (SDS) has shown great promise in text-to-3D generation by distilling pretrained large-scale text-to-image diffusion models, but suffers from over-satura…
Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs
Kaiwen Zheng, Cheng Lu, Jianfei Chen +1
Diffusion models have exhibited excellent performance in various domains. The probability flow ordinary differential equation (ODE) of diffusion models (i.e., diffusion ODEs) is a…
A Closer Look at Parameter-Efficient Tuning in Diffusion Models
Chendong Xiang, Fan Bao, Chongxuan Li +2
Large-scale diffusion models like Stable Diffusion are powerful and find various real-world applications while customizing such models by fine-tuning is both memory and time ineffi…
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale
Fan Bao, Shen Nie, Kaiwen Xue +7
This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fit all distributions relevant to a set of multi-modal data in one model. Our key insight is -- learning d…
Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels
Zebin You, Yong Zhong, Fan Bao +3
In an effort to further advance semi-supervised generative and classification tasks, we propose a simple yet effective training strategy called dual pseudo training (DPT), built up…
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
Chenyu Zheng, Guoqiang Wu, Fan Bao +3
A large-scale deep model pre-trained on massive labeled or unlabeled data transfers well to downstream tasks. Linear evaluation freezes parameters in the pre-trained model and trai…