Weili Nie

According to our database, Weili Nie authored at least 48 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Fast Training of Diffusion Models with Masked Transformers.

Hongkai Zheng, Weili Nie, Arash Vahdat, Anima Anandkumar

Trans. Mach. Learn. Res., 2024

Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled.

Shengchao Liu, Chengpeng Wang, Jiarui Lu, Weili Nie, Hanchen Wang, Zhuoxinran Li, Bolei Zhou, Jian Tang

Trans. Mach. Learn. Res., 2024

State-specific protein-ligand complex structure prediction with a multiscale deep generative model.

Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F. Miller III, Animashree Anandkumar

Nat. Mac. Intell., 2024

Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization.

Siyi Gu, Minkai Xu, Alexander S. Powers, Weili Nie, Tomas Geffner, Karsten Kreis, Jure Leskovec, Arash Vahdat, Stefano Ermon

CoRR, 2024

CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation.

Dejia Xu, Weili Nie, Chao Liu, Sifei Liu, Jan Kautz, Zhangyang Wang, Arash Vahdat

CoRR, 2024

DiffUHaul: A Training-Free Method for Object Dragging in Images.

Omri Avrahami, Rinon Gal, Gal Chechik, Ohad Fried, Dani Lischinski, Arash Vahdat, Weili Nie

CoRR, 2024

T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching.

Zizheng Pan, Bohan Zhuang, De-An Huang, Weili Nie, Zhiding Yu, Chaowei Xiao, Jianfei Cai, Anima Anandkumar

CoRR, 2024

Compositional Text-to-Image Generation with Dense Blob Representations.

Weili Nie, Sifei Liu, Morteza Mardani, Chao Liu, Benjamin Eckart, Arash Vahdat

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition.

Sihyun Yu, Weili Nie, De-An Huang, Boyi Li, Jinwoo Shin, Anima Anandkumar

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Multi-modal molecule structure-text model for text-based retrieval and editing.

Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, Animashree Anandkumar

Nat. Mac. Intell., December, 2023

NeuralPLexer evaluation datasets and predictions.

Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F. Miller III, Animashree Anandkumar

Dataset, December, 2023

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics.

Maxim Zvyagin, Alexander Brace, Kyle Hippe, Yuntian Deng, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, Defne G. Ozgulbas, Natalia Vassilieva, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Sam Foreman, Zhen Xie, Diangen Lin, Maulik Shukla, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, Arvind Ramanathan

Int. J. High Perform. Comput. Appl., November, 2023

Dr-Fairness: Dynamic Data Ratio Adjustment for Fair Training on Real and Generated Data.

Yuji Roh, Weili Nie, De-An Huang, Steven Euijong Whang, Arash Vahdat, Anima Anandkumar

Trans. Mach. Learn. Res., 2023

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies.

Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan A. Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi A. Hanson, Thomas E. Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton D. Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin M. Aji, Angela Dalton, Michael J. Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens

CoRR, 2023

Improving Generative Model-based Unfolding with Schrödinger Bridges.

Sascha Diefenbacher, Guan-Horng Liu, Vinicius Mikuni, Benjamin Nachman, Weili Nie

CoRR, 2023

A Text-guided Protein Design Framework.

Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, Anima Anandkumar

CoRR, 2023

Fast Sampling of Diffusion Models via Operator Learning.

Hongkai Zheng, Weili Nie, Arash Vahdat, Kamyar Azizzadenesheli, Anima Anandkumar

Proceedings of the International Conference on Machine Learning, 2023

A Critical Revisit of Adversarial Robustness in 3D Point Cloud Recognition with Diffusion-Driven Purification.

Jiachen Sun, Jiongxiao Wang, Weili Nie, Zhiding Yu, Zhuoqing Mao, Chaowei Xiao

Proceedings of the International Conference on Machine Learning, 2023

I²SB: Image-to-Image Schrödinger Bridge.

Guan-Horng Liu, Arash Vahdat, De-An Huang, Evangelos A. Theodorou, Weili Nie, Anima Anandkumar

Proceedings of the International Conference on Machine Learning, 2023

DensePure: Understanding Diffusion Models for Adversarial Robustness.

Chaowei Xiao, Zhongzhu Chen, Kun Jin, Jiongxiao Wang, Weili Nie, Mingyan Liu, Anima Anandkumar, Bo Li, Dawn Song

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Defending against Adversarial Audio via Diffusion Model.

Shutong Wu, Jiongxiao Wang, Wei Ping, Weili Nie, Chaowei Xiao

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Retrieval-based Controllable Molecule Generation.

Zichao Wang, Weili Nie, Zhuoran Qiao, Chaowei Xiao, Richard G. Baraniuk, Anima Anandkumar

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning.

Zhuolin Yang, Wei Ping, Zihan Liu, Vijay Korthikanti, Weili Nie, De-An Huang, Linxi Fan, Zhiding Yu, Shiyi Lan, Bo Li, Mohammad Shoeybi, Ming-Yu Liu, Yuke Zhu, Bryan Catanzaro, Chaowei Xiao, Anima Anandkumar

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

2022

DensePure: Understanding Diffusion Models towards Adversarial Robustness.

Chaowei Xiao, Zhongzhu Chen, Kun Jin, Jiongxiao Wang, Weili Nie, Mingyan Liu, Anima Anandkumar, Bo Li, Dawn Song

CoRR, 2022

Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models.

Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F. Miller III, Anima Anandkumar

CoRR, 2022

PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognition.

Jiachen Sun, Weili Nie, Zhiding Yu, Z. Morley Mao, Chaowei Xiao

CoRR, 2022

Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models.

Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, Chaowei Xiao

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Diffusion Models for Adversarial Purification.

Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, Animashree Anandkumar

Proceedings of the International Conference on Machine Learning, 2022

RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning.

Xiaojian Ma, Weili Nie, Zhiding Yu, Huaizu Jiang, Chaowei Xiao, Yuke Zhu, Song-Chun Zhu, Anima Anandkumar

Proceedings of the Tenth International Conference on Learning Representations, 2022

Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions.

Huaizu Jiang, Xiaojian Ma, Weili Nie, Zhiding Yu, Yuke Zhu, Anima Anandkumar

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Controllable and Compositional Generation with Latent-Space Energy-Based Models.

Weili Nie, Arash Vahdat, Anima Anandkumar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

An Improved Semi-Supervised VAE for Learning Disentangled Representations.

Weili Nie, Zichao Wang, Ankit B. Patel, Richard G. Baraniuk

CoRR, 2020

Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning.

Weili Nie, Zhiding Yu, Lei Mao, Ankit B. Patel, Yuke Zhu, Anima Anandkumar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Semi-Supervised StyleGAN for Disentanglement Learning.

Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit B. Patel, Animashree Anandkumar

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Towards a Better Understanding and Regularization of GAN Training Dynamics.

Weili Nie, Ankit Patel

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

RelGAN: Relational Generative Adversarial Networks for Text Generation.

Weili Nie, Nina Narodytska, Ankit Patel

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

JR-GAN: Jacobian Regularization for Generative Adversarial Networks.

Weili Nie, Ankit Patel

CoRR, 2018

QG-net: a data-driven question generation model for educational content.

Zichao Wang, Andrew S. Lan, Weili Nie, Andrew E. Waters, Phillip J. Grimaldi, Richard G. Baraniuk

Proceedings of the Fifth Annual ACM Conference on Learning at Scale, 2018

A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations.

Weili Nie, Yang Zhang, Ankit Patel

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Uplink Performance Improvement by Decoupling Uplink/Downlink Access in HetNets.

Lan Zhang, Weili Nie, Gang Feng, Fu-Chun Zheng, Shuang Qin

IEEE Trans. Veh. Technol., 2017

Toward a Tractable Delay Analysis in Ultra-Dense Networks.

Yi Zhong, Martin Haenggi, Fu-Chun Zheng, Wenyi Zhang, Tony Q. S. Quek, Weili Nie

IEEE Commun. Mag., 2017

2016

HetNets With Random DTX Scheme: Local Delay and Energy Efficiency.

Weili Nie, Yi Zhong, Fu-Chun Zheng, Wenyi Zhang, Timothy O'Farrell

IEEE Trans. Veh. Technol., 2016

User-Centric Cross-Tier Base Station Clustering and Cooperation in Heterogeneous Networks: Rate Improvement and Energy Saving.

Weili Nie, Fu-Chun Zheng, Xiaoming Wang, Wenyi Zhang, Shi Jin

IEEE J. Sel. Areas Commun., 2016

Towards a Tractable Delay Analysis in Large Wireless Networks.

Yi Zhong, Martin Haenggi, Fu-Chun Zheng, Wenyi Zhang, Tony Q. S. Quek, Weili Nie

CoRR, 2016

2015

Local delay and energy efficiency analysis in HetNets with random DTX scheme.

Weili Nie, Yi Zhong, Fu-Chun Zheng, Wenyi Zhang

Proceedings of the 2015 IEEE International Conference on Communications, 2015

A Comparison Study of Coupled and Decoupled Uplink-Downlink Access in Heterogeneous Cellular Networks.

Lan Zhang, Gang Feng, Weili Nie, Shuang Qin

Proceedings of the 2015 IEEE Global Communications Conference, 2015

2014

Energy Efficiency of Cross-Tier Base Station Cooperation in Heterogeneous Cellular Networks.

Weili Nie, Fu-Chun Zheng, Xiaoming Wang, Shi Jin, Wenyi Zhang

CoRR, 2014

Energy-efficient base station cooperation in downlink heterogeneous cellular networks.

Weili Nie, Xiaoming Wang, Fu-Chun Zheng, Wenyi Zhang

Proceedings of the IEEE Global Communications Conference, 2014