Search Results for author:
Found 15 papers, 7 papers with code
DiffusionSat: A Generative Foundation Model for Satellite Imagery
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.
DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling
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.
Diffusion Model Alignment Using Direct Preference Optimization
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
However, for many applications such as image editing, the model input comes from a distribution that is not random noise.
Curious Replay for Model-based Adaptation
Agents must be able to adapt quickly as an environment changes.
Deep Latent State Space Models for Time-Series Generation
Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series.
3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models
We propose a diffusion model for neural implicit representations of 3D shapes that operates in the latent space of an auto-decoder.
ButterflyFlow: Building Invertible Layers with Butterfly Matrices
Normalizing flows model complex probability distributions using maps obtained by composing invertible layers.
Emergence of Theory of Mind Collaboration in Multiagent Systems
Currently, in the study of multiagent systems, the intentions of agents are usually ignored.
3D Shape Generation and Completion through Point-Voxel Diffusion
We propose a novel approach for probabilistic generative modeling of 3D shapes.
Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation
Open-domain dialogue generation has gained increasing attention in Natural Language Processing.
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model
This paper proposes a joint training method to learn both the variational auto-encoder (VAE) and the latent energy-based model (EBM).
Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference
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
We propose the clustered generator model for clustering which contains both continuous and discrete latent variables.
Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge
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.
