Search Results for author: Linqi Zhou

Found 15 papers, 7 papers with code

DiffusionSat: A Generative Foundation Model for Satellite Imagery

no code implementations6 Dec 2023 Samar Khanna, Patrick Liu, Linqi Zhou, Chenlin Meng, Robin Rombach, Marshall Burke, David Lobell, Stefano Ermon

Our method outperforms previous state-of-the-art methods for satellite image generation and is the first large-scale $\textit{generative}$ foundation model for satellite imagery.

Crop Yield Prediction Image Generation

DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling

1 code implementation28 Nov 2023 Linqi Zhou, Andy Shih, Chenlin Meng, Stefano Ermon

Recent methods such as Score Distillation Sampling (SDS) and Variational Score Distillation (VSD) using 2D diffusion models for text-to-3D generation have demonstrated impressive generation quality.

Text to 3D

Diffusion Model Alignment Using Direct Preference Optimization

no code implementations21 Nov 2023 Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, Nikhil Naik

Large language models (LLMs) are fine-tuned using human comparison data with Reinforcement Learning from Human Feedback (RLHF) methods to make them better aligned with users' preferences.

Denoising Diffusion Bridge Models

1 code implementation29 Sep 2023 Linqi Zhou, Aaron Lou, Samar Khanna, Stefano Ermon

However, for many applications such as image editing, the model input comes from a distribution that is not random noise.

Denoising Image Generation

Deep Latent State Space Models for Time-Series Generation

1 code implementation24 Dec 2022 Linqi Zhou, Michael Poli, Winnie Xu, Stefano Massaroli, Stefano Ermon

Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series.

Time Series Time Series Analysis +1

3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models

no code implementations1 Dec 2022 Gimin Nam, Mariem Khlifi, Andrew Rodriguez, Alberto Tono, Linqi Zhou, Paul Guerrero

We propose a diffusion model for neural implicit representations of 3D shapes that operates in the latent space of an auto-decoder.

3D Shape Generation Image Generation +2

ButterflyFlow: Building Invertible Layers with Butterfly Matrices

no code implementations28 Sep 2022 Chenlin Meng, Linqi Zhou, Kristy Choi, Tri Dao, Stefano Ermon

Normalizing flows model complex probability distributions using maps obtained by composing invertible layers.

Density Estimation

Emergence of Theory of Mind Collaboration in Multiagent Systems

no code implementations30 Sep 2021 Luyao Yuan, Zipeng Fu, Linqi Zhou, Kexin Yang, Song-Chun Zhu

Currently, in the study of multiagent systems, the intentions of agents are usually ignored.

Decision Making

Joint Training of Variational Auto-Encoder and Latent Energy-Based Model

no code implementations CVPR 2020 Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu

This paper proposes a joint training method to learn both the variational auto-encoder (VAE) and the latent energy-based model (EBM).

Anomaly Detection

Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference

no code implementations ECCV 2020 Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu

Learning such a generative model requires inferring the latent variables for each training example based on the posterior distribution of these latent variables.

Deep Unsupervised Clustering with Clustered Generator Model

no code implementations19 Nov 2019 Dandan Zhu, Tian Han, Linqi Zhou, Xiaokang Yang, Ying Nian Wu

We propose the clustered generator model for clustering which contains both continuous and discrete latent variables.

Clustering

Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge

1 code implementation1 Sep 2019 Zijun Zhang, Linqi Zhou, Liangke Gou, Ying Nian Wu

We report a neural architecture search framework, BioNAS, that is tailored for biomedical researchers to easily build, evaluate, and uncover novel knowledge from interpretable deep learning models.

Neural Architecture Search

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