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08/04/2023
Improved Order Analysis and Design of Exponential Integrator for Diffusion Models Sampling
Efficient differential equation solvers have significantly reduced the s...
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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...
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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...
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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...
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03/30/2023
DiffCollage: Parallel Generation of Large Content with Diffusion Models
We present DiffCollage, a compositional diffusion model that can generat...
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02/14/2023
Score-based Diffusion Models in Function Space
Diffusion models have recently emerged as a powerful framework for gener...
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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 ...
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11/02/2022
Concrete Score Matching: Generalized Score Matching for Discrete Data
Representing probability distributions by the gradient of their density ...
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09/23/2022
JPEG Artifact Correction using Denoising Diffusion Restoration Models
Diffusion models can be used as learned priors for solving various inver...
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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...
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03/16/2022
Dual Diffusion Implicit Bridges for Image-to-Image Translation
Common image-to-image translation methods rely on joint training over da...
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02/28/2022
LISA: Learning Interpretable Skill Abstractions from Language
Learning policies that effectually utilize language instructions in comp...
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01/27/2022
Denoising Diffusion Restoration Models
Many interesting tasks in image restoration can be cast as linear invers...
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11/08/2021
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems
We introduce a curriculum learning algorithm, Variational Automatic Curr...
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06/23/2021
IQ-Learn: Inverse soft-Q Learning for Imitation
In many sequential decision-making problems (e.g., robotics control, gam...
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06/12/2021
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation
Conditional generative models of high-dimensional images have many appli...
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02/09/2021
Negative Data Augmentation
Data augmentation is often used to enlarge datasets with synthetic sampl...
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10/06/2020
Denoising Diffusion Implicit Models
Denoising diffusion probabilistic models (DDPMs) have achieved high qual...
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08/21/2020
Privacy Preserving Recalibration under Domain Shift
Classifiers deployed in high-stakes real-world applications must output ...
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07/20/2020
Multi-label Contrastive Predictive Coding
Variational mutual information (MI) estimators are widely used in unsupe...
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07/01/2020
Belief Propagation Neural Networks
Learned neural solvers have successfully been used to solve combinatoria...
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06/23/2020
Experience Replay with Likelihood-free Importance Weights
The use of past experiences to accelerate temporal difference (TD) learn...
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03/24/2020
Robust and On-the-fly Dataset Denoising for Image Classification
Memorization in over-parameterized neural networks could severely hurt g...
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03/06/2020
Training Deep Energy-Based Models with f-Divergence Minimization
Deep energy-based models (EBMs) are very flexible in distribution parame...
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03/04/2020
Gaussianization Flows
Iterative Gaussianization is a fixed-point iteration procedure that can ...
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03/02/2020
Permutation Invariant Graph Generation via Score-Based Generative Modeling
Learning generative models for graph-structured data is challenging beca...
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02/25/2020
A Theory of Usable Information Under Computational Constraints
We propose a new framework for reasoning about information in complex sy...
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10/22/2019
Bridging the Gap Between f-GANs and Wasserstein GANs
Generative adversarial networks (GANs) have enjoyed much success in lear...
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10/21/2019
Unsupervised Out-of-Distribution Detection with Batch Normalization
Likelihood from a generative model is a natural statistic for detecting ...
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10/14/2019
Understanding the Limitations of Variational Mutual Information Estimators
Variational approaches based on neural networks are showing promise for ...
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09/30/2019
Cross Domain Imitation Learning
We study the question of how to imitate tasks across domains with discre...
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07/30/2019
Multi-Agent Adversarial Inverse Reinforcement Learning
Reinforcement learning agents are prone to undesired behaviors due to re...
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06/23/2019
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
A learned generative model often produces biased statistics relative to ...
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06/19/2019
Calibrated Model-Based Deep Reinforcement Learning
Estimates of predictive uncertainty are important for accurate model-bas...
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12/11/2018
Learning Controllable Fair Representations
Learning data representations that are transferable and fair with respec...
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11/08/2018
Bias and Generalization in Deep Generative Models: An Empirical Study
In high dimensional settings, density estimation algorithms rely crucial...
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07/26/2018
Multi-Agent Generative Adversarial Imitation Learning
Imitation learning algorithms can be used to learn a policy from expert ...
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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...
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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 ...
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05/27/2018
Adversarial Constraint Learning for Structured Prediction
Constraint-based learning reduces the burden of collecting labels by hav...
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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 ...
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06/23/2017
A-NICE-MC: Adversarial Training for MCMC
Existing Markov Chain Monte Carlo (MCMC) methods are either based on gen...
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06/07/2017
InfoVAE: Information Maximizing Variational Autoencoders
It has been previously observed that variational autoencoders tend to ig...
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03/26/2017
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations
The goal of imitation learning is to mimic expert behavior without acces...
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03/07/2017
On the Limits of Learning Representations with Label-Based Supervision
Advances in neural network based classifiers have transformed automatic ...
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02/28/2017
Towards Deeper Understanding of Variational Autoencoding Models
We propose a new family of optimization criteria for variational auto-en...
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02/27/2017
Learning Hierarchical Features from Generative Models
Deep neural networks have been shown to be very successful at learning f...
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05/22/2016
Factored Temporal Sigmoid Belief Networks for Sequence Learning
Deep conditional generative models are developed to simultaneously learn...
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02/24/2016
Max-Margin Nonparametric Latent Feature Models for Link Prediction
Link prediction is a fundamental task in statistical network analysis. R...
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12/07/2015
