b'Jiaming Song'

research

∙ 08/04/2023

Improved Order Analysis and Design of Exponential Integrator for Diffusion Models Sampling

Efficient differential equation solvers have significantly reduced the s...

0 Qinsheng Zhang, et al. ∙

research

∙ 06/30/2023

Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions

Generating learning-friendly representations for points in space is a fu...

0 Gengchen Mai, et al. ∙

research

∙ 05/07/2023

A Variational Perspective on Solving Inverse Problems with Diffusion Models

Diffusion models have emerged as a key pillar of foundation models in vi...

0 Morteza Mardani, et al. ∙

research

∙ 05/01/2023

CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations

Geo-tagged images are publicly available in large quantities, whereas la...

5 Gengchen Mai, et al. ∙

research

∙ 03/30/2023

DiffCollage: Parallel Generation of Large Content with Diffusion Models

We present DiffCollage, a compositional diffusion model that can generat...

0 Qinsheng Zhang, et al. ∙

research

∙ 02/14/2023

Score-based Diffusion Models in Function Space

Diffusion models have recently emerged as a powerful framework for gener...

0 Jae Hyun Lim, et al. ∙

research

∙ 11/02/2022

eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

Large-scale diffusion-based generative models have led to breakthroughs ...

0 Yogesh Balaji, et al. ∙

research

∙ 11/02/2022

Concrete Score Matching: Generalized Score Matching for Discrete Data

Representing probability distributions by the gradient of their density ...

6 Chenlin Meng, et al. ∙

research

∙ 09/23/2022

JPEG Artifact Correction using Denoising Diffusion Restoration Models

Diffusion models can be used as learned priors for solving various inver...

10 Bahjat Kawar, et al. ∙

research

∙ 04/03/2022

BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster

Most AI projects start with a Python notebook running on a single laptop...

29 Jason Dai, et al. ∙

research

∙ 03/16/2022

Dual Diffusion Implicit Bridges for Image-to-Image Translation

Common image-to-image translation methods rely on joint training over da...

3 Xuan Su, et al. ∙

research

∙ 02/28/2022

LISA: Learning Interpretable Skill Abstractions from Language

Learning policies that effectually utilize language instructions in comp...

1 Divyansh Garg, et al. ∙

research

∙ 01/27/2022

Denoising Diffusion Restoration Models

Many interesting tasks in image restoration can be cast as linear invers...

2 Bahjat Kawar, et al. ∙

research

∙ 11/08/2021

Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems

We introduce a curriculum learning algorithm, Variational Automatic Curr...

4 Jiayu Chen, et al. ∙

research

∙ 06/23/2021

IQ-Learn: Inverse soft-Q Learning for Imitation

In many sequential decision-making problems (e.g., robotics control, gam...

9 Divyansh Garg, et al. ∙

research

∙ 06/12/2021

D2C: Diffusion-Denoising Models for Few-shot Conditional Generation

Conditional generative models of high-dimensional images have many appli...

6 Abhishek Sinha, et al. ∙

research

∙ 02/09/2021

Negative Data Augmentation

Data augmentation is often used to enlarge datasets with synthetic sampl...

16 Abhishek Sinha, et al. ∙

research

∙ 10/06/2020

Denoising Diffusion Implicit Models

Denoising diffusion probabilistic models (DDPMs) have achieved high qual...

7 Jiaming Song, et al. ∙

research

∙ 08/21/2020

Privacy Preserving Recalibration under Domain Shift

Classifiers deployed in high-stakes real-world applications must output ...

4 Rachel Luo, et al. ∙

research

∙ 07/20/2020

Multi-label Contrastive Predictive Coding

Variational mutual information (MI) estimators are widely used in unsupe...

12 Jiaming Song, et al. ∙

research

∙ 07/01/2020

Belief Propagation Neural Networks

Learned neural solvers have successfully been used to solve combinatoria...

14 Jonathan Kuck, et al. ∙

research

∙ 06/23/2020

Experience Replay with Likelihood-free Importance Weights

The use of past experiences to accelerate temporal difference (TD) learn...

12 Samarth Sinha, et al. ∙

research

∙ 03/24/2020

Robust and On-the-fly Dataset Denoising for Image Classification

Memorization in over-parameterized neural networks could severely hurt g...

0 Jiaming Song, et al. ∙

research

∙ 03/06/2020

Training Deep Energy-Based Models with f-Divergence Minimization

Deep energy-based models (EBMs) are very flexible in distribution parame...

14 Lantao Yu, et al. ∙

research

∙ 03/04/2020

Gaussianization Flows

Iterative Gaussianization is a fixed-point iteration procedure that can ...

7 Chenlin Meng, et al. ∙

research

∙ 03/02/2020

Permutation Invariant Graph Generation via Score-Based Generative Modeling

Learning generative models for graph-structured data is challenging beca...

18 Chenhao Niu, et al. ∙

research

∙ 02/25/2020

A Theory of Usable Information Under Computational Constraints

We propose a new framework for reasoning about information in complex sy...

8 Yilun Xu, et al. ∙

research

∙ 10/22/2019

Bridging the Gap Between f-GANs and Wasserstein GANs

Generative adversarial networks (GANs) have enjoyed much success in lear...

34 Jiaming Song, et al. ∙

research

∙ 10/21/2019

Unsupervised Out-of-Distribution Detection with Batch Normalization

Likelihood from a generative model is a natural statistic for detecting ...

48 Jiaming Song, et al. ∙

research

∙ 10/14/2019

Understanding the Limitations of Variational Mutual Information Estimators

Variational approaches based on neural networks are showing promise for ...

54 Jiaming Song, et al. ∙

research

∙ 09/30/2019

Cross Domain Imitation Learning

We study the question of how to imitate tasks across domains with discre...

41 Kun ho Kim, et al. ∙

research

∙ 07/30/2019

Multi-Agent Adversarial Inverse Reinforcement Learning

Reinforcement learning agents are prone to undesired behaviors due to re...

8 Lantao Yu, et al. ∙

research

∙ 06/23/2019

Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting

A learned generative model often produces biased statistics relative to ...

8 Aditya Grover, et al. ∙

research

∙ 06/19/2019

Calibrated Model-Based Deep Reinforcement Learning

Estimates of predictive uncertainty are important for accurate model-bas...

11 Ali Malik, et al. ∙

research

∙ 12/11/2018

Learning Controllable Fair Representations

Learning data representations that are transferable and fair with respec...

12 Jiaming Song, et al. ∙

research

∙ 11/08/2018

Bias and Generalization in Deep Generative Models: An Empirical Study

In high dimensional settings, density estimation algorithms rely crucial...

6 Shengjia Zhao, et al. ∙

research

∙ 07/26/2018

Multi-Agent Generative Adversarial Imitation Learning

Imitation learning algorithms can be used to learn a policy from expert ...

0 Jiaming Song, et al. ∙

research

∙ 07/20/2018

gSMat: A Scalable Sparse Matrix-based Join for SPARQL Query Processing

Resource Description Framework (RDF) has been widely used to represent i...

0 Xiaowang Zhang, et al. ∙

research

∙ 06/18/2018

The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models

A variety of learning objectives have been proposed for training latent ...

2 Shengjia Zhao, et al. ∙

research

∙ 05/27/2018

Adversarial Constraint Learning for Structured Prediction

Constraint-based learning reduces the burden of collecting labels by hav...

0 Hongyu Ren, et al. ∙

research

∙ 01/17/2018

An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients

In this technical report, we consider an approach that combines the PPO ...

0 Jiaming Song, et al. ∙

research

∙ 06/23/2017

A-NICE-MC: Adversarial Training for MCMC

Existing Markov Chain Monte Carlo (MCMC) methods are either based on gen...

0 Jiaming Song, et al. ∙

research

∙ 06/07/2017

InfoVAE: Information Maximizing Variational Autoencoders

It has been previously observed that variational autoencoders tend to ig...

0 Shengjia Zhao, et al. ∙

research

∙ 03/26/2017

InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations

The goal of imitation learning is to mimic expert behavior without acces...

0 Yunzhu Li, et al. ∙

research

∙ 03/07/2017

On the Limits of Learning Representations with Label-Based Supervision

Advances in neural network based classifiers have transformed automatic ...

0 Jiaming Song, et al. ∙

research

∙ 02/28/2017

Towards Deeper Understanding of Variational Autoencoding Models

We propose a new family of optimization criteria for variational auto-en...

0 Shengjia Zhao, et al. ∙

research

∙ 02/27/2017

Learning Hierarchical Features from Generative Models

Deep neural networks have been shown to be very successful at learning f...

0 Shengjia Zhao, et al. ∙

research

∙ 05/22/2016

Factored Temporal Sigmoid Belief Networks for Sequence Learning

Deep conditional generative models are developed to simultaneously learn...

0 Jiaming Song, et al. ∙

research

∙ 02/24/2016

Max-Margin Nonparametric Latent Feature Models for Link Prediction

Link prediction is a fundamental task in statistical network analysis. R...

0 Jun Zhu, et al. ∙

research

∙ 12/07/2015

Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation

We present a discriminative nonparametric latent feature relational mode...

0 Bei Chen, et al. ∙

Jiaming Song

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