92 citations · 310 across the 16 of their papers we have counts for
16 papers
Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicing
Han Zhang, Hao Zhou, Melike Erol-Kantarci
Recently, open radio access network (O-RAN) has become a promising technology to provide an open environment for network vendors and operators. Coordinating the x-applications (xAP…
MaskGIT: Masked Generative Image Transformer
Huiwen Chang, Han Zhang, Lu Jiang +2
Generative transformers have experienced rapid popularity growth in the computer vision community in synthesizing high-fidelity and high-resolution images. The best generative tran…
Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN)
Han Zhang, Hao Zhou, Melike Erol-Kantarci
Recently, the concept of open radio access network (O-RAN) has been proposed, which aims to adopt intelligence and openness in the next generation radio access networks (RAN). It p…
MAXIM: Multi-Axis MLP for Image Processing
Zhengzhong Tu, Hossein Talebi, Han Zhang +4
Recent progress on Transformers and multi-layer perceptron (MLP) models provide new network architectural designs for computer vision tasks. Although these models proved to be effe…
BLT: Bidirectional Layout Transformer for Controllable Layout Generation
Xiang Kong, Lu Jiang, Huiwen Chang +4
Creating visual layouts is a critical step in graphic design. Automatic generation of such layouts is essential for scalable and diverse visual designs. To advance conditional layo…
Vector-quantized Image Modeling with Improved VQGAN
Jiahui Yu, Xin Li, Jing Yu Koh +7
Pretraining language models with next-token prediction on massive text corpora has delivered phenomenal zero-shot, few-shot, transfer learning and multi-tasking capabilities on bot…
ViTGAN: Training GANs with Vision Transformers
Kwonjoon Lee, Huiwen Chang, Lu Jiang +3
Recently, Vision Transformers (ViTs) have shown competitive performance on image recognition while requiring less vision-specific inductive biases. In this paper, we investigate if…
Improved Transformer for High-Resolution GANs
Long Zhao, Zizhao Zhang, Ting Chen +2
Attention-based models, exemplified by the Transformer, can effectively model long range dependency, but suffer from the quadratic complexity of self-attention operation, making th…
Transfer training from smaller language model
Han Zhang
Large language models have led to state-of-the-art accuracies across a range of tasks. However,training large language model needs massive computing resource, as more and more open…
Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction
Wonkwang Lee, Whie Jung, Han Zhang +6
Learning to predict the long-term future of video frames is notoriously challenging due to inherent ambiguities in the distant future and dramatic amplifications of prediction erro…
Cross-Modal Contrastive Learning for Text-to-Image Generation
Han Zhang, Jing Yu Koh, Jason Baldridge +2
The output of text-to-image synthesis systems should be coherent, clear, photo-realistic scenes with high semantic fidelity to their conditioned text descriptions. Our Cross-Modal…
Improving NER's Performance with Massive financial corpus
Han Zhang
Training large deep neural networks needs massive high quality annotation data, but the time and labor costs are too expensive for small business. We start a company-name recogniti…
Consistency Regularization for Generative Adversarial Networks
Han Zhang, Zizhao Zhang, Augustus Odena +1
Generative Adversarial Networks (GANs) are known to be difficult to train, despite considerable research effort. Several regularization techniques for stabilizing training have bee…
Differentiable Combinatorial Losses through Generalized Gradients of Linear Programs
Xi Gao, Han Zhang, Aliakbar Panahi +1
When samples have internal structure, we often see a mismatch between the objective optimized during training and the model's goal during inference. For example, in sequence-to-seq…
Distilling Effective Supervision from Severe Label Noise
Zizhao Zhang, Han Zhang, Sercan O. Arik +2
Collecting large-scale data with clean labels for supervised training of neural networks is practically challenging. Although noisy labels are usually cheap to acquire, existing me…
Approximation Capabilities of Neural ODEs and Invertible Residual Networks
Han Zhang, Xi Gao, Jacob Unterman +1
Neural ODEs and i-ResNet are recently proposed methods for enforcing invertibility of residual neural models. Having a generic technique for constructing invertible models can open…