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2020 – today
- 2023
[i27]Enci Liu, Chenlin Meng, Matthew Kolodner, Eun Jee Sung, Sihang Chen, Marshall Burke, David B. Lobell, Stefano Ermon:
Building Coverage Estimation with Low-resolution Remote Sensing Imagery. CoRR abs/2301.01449 (2023)- 2022
- [c22]Chenlin Meng, Enci Liu, Willie Neiswanger, Jiaming Song, Marshall Burke, David B. Lobell, Stefano Ermon:
IS-Count: Large-Scale Object Counting from Satellite Images with Covariate-Based Importance Sampling. AAAI 2022: 12034-12042 - [c21]Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon:
Density Ratio Estimation via Infinitesimal Classification. AISTATS 2022: 2552-2573 - [c20]Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon:
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations. ICLR 2022 - [c19]Chenlin Meng, Linqi Zhou, Kristy Choi, Tri Dao, Stefano Ermon:
ButterflyFlow: Building Invertible Layers with Butterfly Matrices. ICML 2022: 15360-15375 - [c18]Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David B. Lobell, Stefano Ermon:
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery. NeurIPS 2022 - [c17]Muyang Li, Ji Lin, Chenlin Meng, Stefano Ermon, Song Han, Jun-Yan Zhu:
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models. NeurIPS 2022 - [c16]Chenlin Meng, Kristy Choi, Jiaming Song, Stefano Ermon:
Concrete Score Matching: Generalized Score Matching for Discrete Data. NeurIPS 2022 - [c15]Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon:
Generalizing Bayesian Optimization with Decision-theoretic Entropies. NeurIPS 2022 - [c14]Michael Poli, Winnie Xu, Stefano Massaroli, Chenlin Meng, Kuno Kim, Stefano Ermon:
Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations. NeurIPS 2022 - [i26]Xuan Su, Jiaming Song, Chenlin Meng, Stefano Ermon:
Dual Diffusion Implicit Bridges for Image-to-Image Translation. CoRR abs/2203.08382 (2022) - [i25]Yutong He, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon:
Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution. CoRR abs/2204.01736 (2022) - [i24]Michael Poli, Winnie Xu, Stefano Massaroli, Chenlin Meng, Kuno Kim, Stefano Ermon:
Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations. CoRR abs/2204.07673 (2022) - [i23]Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David B. Lobell, Stefano Ermon:
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery. CoRR abs/2207.08051 (2022) - [i22]Chenlin Meng, Linqi Zhou, Kristy Choi, Tri Dao, Stefano Ermon:
ButterflyFlow: Building Invertible Layers with Butterfly Matrices. CoRR abs/2209.13774 (2022) - [i21]Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon:
Generalizing Bayesian Optimization with Decision-theoretic Entropies. CoRR abs/2210.01383 (2022) - [i20]Chenlin Meng, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans:
On Distillation of Guided Diffusion Models. CoRR abs/2210.03142 (2022) - [i19]Chenlin Meng, Kristy Choi, Jiaming Song, Stefano Ermon:
Concrete Score Matching: Generalized Score Matching for Discrete Data. CoRR abs/2211.00802 (2022) - [i18]Muyang Li, Ji Lin, Chenlin Meng, Stefano Ermon, Song Han, Jun-Yan Zhu:
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models. CoRR abs/2211.02048 (2022) - 2021
- [c13]Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David B. Lobell
, Stefano Ermon:
Geography-Aware Self-Supervised Learning. ICCV 2021: 10161-10170 - [c12]Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon:
Improved Autoregressive Modeling with Distribution Smoothing. ICLR 2021 - [c11]Jiaming Song, Chenlin Meng, Stefano Ermon:
Denoising Diffusion Implicit Models. ICLR 2021 - [c10]Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon:
Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving. ICML 2021: 9791-9800 - [c9]Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon:
D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation. NeurIPS 2021: 12533-12548 - [c8]Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon:
Estimating High Order Gradients of the Data Distribution by Denoising. NeurIPS 2021: 25359-25369 - [c7]Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon:
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis. NeurIPS 2021: 27903-27915 - [c6]Christopher Yeh, Chenlin Meng, Sherrie Wang, Anne Driscoll, Erik Rozi, Patrick Liu, Jihyeon Janel Lee, Marshall Burke, David B. Lobell, Stefano Ermon:
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning. NeurIPS Datasets and Benchmarks 2021 - [i17]Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, Stefano Ermon:
Improved Autoregressive Modeling with Distribution Smoothing. CoRR abs/2103.15089 (2021) - [i16]Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon:
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation. CoRR abs/2106.06819 (2021) - [i15]Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon:
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis. CoRR abs/2106.11485 (2021) - [i14]Chenlin Meng, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon:
SDEdit: Image Synthesis and Editing with Stochastic Differential Equations. CoRR abs/2108.01073 (2021) - [i13]Christopher Yeh, Chenlin Meng, Sherrie Wang, Anne Driscoll, Erik Rozi, Patrick Liu, Jihyeon Janel Lee, Marshall Burke, David B. Lobell, Stefano Ermon:
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning. CoRR abs/2111.04724 (2021) - [i12]Chenlin Meng, Yang Song, Wenzhe Li, Stefano Ermon:
Estimating High Order Gradients of the Data Distribution by Denoising. CoRR abs/2111.04726 (2021) - [i11]Kristy Choi, Chenlin Meng, Yang Song, Stefano Ermon:
Density Ratio Estimation via Infinitesimal Classification. CoRR abs/2111.11010 (2021) - [i10]Chenlin Meng, Enci Liu, Willie Neiswanger, Jiaming Song, Marshall Burke, David B. Lobell, Stefano Ermon:
IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling. CoRR abs/2112.09126 (2021) - 2020
- [c5]Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon:
Gaussianization Flows. AISTATS 2020: 4336-4345 - [c4]Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon:
Autoregressive Score Matching. NeurIPS 2020 - [i9]Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon:
Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation. CoRR abs/2002.03629 (2020) - [i8]Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon:
Gaussianization Flows. CoRR abs/2003.01941 (2020) - [i7]Jiaming Song, Chenlin Meng, Stefano Ermon:
Denoising Diffusion Implicit Models. CoRR abs/2010.02502 (2020) - [i6]Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon:
Autoregressive Score Matching. CoRR abs/2010.12810 (2020) - [i5]Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David B. Lobell, Stefano Ermon:
Geography-Aware Self-Supervised Learning. CoRR abs/2011.09980 (2020)
2010 – 2019
- 2019
- [c3]Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, Marshall Burke, David B. Lobell, Stefano Ermon:
Learning to Interpret Satellite Images using Wikipedia. IJCAI 2019: 3620-3626 - [c2]Evan Sheehan, Chenlin Meng, Matthew Tan, Burak Uzkent, Neal Jean, Marshall Burke, David B. Lobell, Stefano Ermon:
Predicting Economic Development using Geolocated Wikipedia Articles. KDD 2019: 2698-2706 - [c1]Yang Song, Chenlin Meng, Stefano Ermon:
MintNet: Building Invertible Neural Networks with Masked Convolutions. NeurIPS 2019: 11002-11012 - [i4]Evan Sheehan, Chenlin Meng, Matthew Tan, Burak Uzkent, Neal Jean, David B. Lobell, Marshall Burke, Stefano Ermon:
Predicting Economic Development using Geolocated Wikipedia Articles. CoRR abs/1905.01627 (2019) - [i3]Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, Marshall Burke, David B. Lobell, Stefano Ermon:
Learning to Interpret Satellite Images in Global Scale Using Wikipedia. CoRR abs/1905.02506 (2019) - [i2]Yang Song, Chenlin Meng, Stefano Ermon:
MintNet: Building Invertible Neural Networks with Masked Convolutions. CoRR abs/1907.07945 (2019) - 2018
- [i1]Evan Sheehan, Burak Uzkent, Chenlin Meng, Zhongyi Tang, Marshall Burke, David B. Lobell, Stefano Ermon:
Learning to Interpret Satellite Images Using Wikipedia. CoRR abs/1809.10236 (2018)
Coauthor Index
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last updated on 2023-05-12 21:04 CEST by the dblp team
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