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06/15/2023
Fast Training of Diffusion Models with Masked Transformers
We propose an efficient approach to train large diffusion models with ma...
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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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04/02/2023
Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models
Cascaded computation, whereby predictions are recurrently refined over s...
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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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02/12/2023
I^2SB: Image-to-Image Schrödinger Bridge
We propose Image-to-Image Schrödinger Bridge (I^2SB), a new class of con...
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11/24/2022
Fast Sampling of Diffusion Models via Operator Learning
Diffusion models have found widespread adoption in various areas. Howeve...
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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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10/18/2022
Differentially Private Diffusion Models
While modern machine learning models rely on increasingly large training...
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10/12/2022
LION: Latent Point Diffusion Models for 3D Shape Generation
Denoising diffusion models (DDMs) have shown promising results in 3D poi...
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10/11/2022
GENIE: Higher-Order Denoising Diffusion Solvers
Denoising diffusion models (DDMs) have emerged as a powerful class of ge...
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09/30/2022
Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models
Molecular complexes formed by proteins and small-molecule ligands are ub...
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05/16/2022
Diffusion Models for Adversarial Purification
Adversarial purification refers to a class of defense methods that remov...
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12/15/2021
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
A wide variety of deep generative models has been developed in the past ...
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12/14/2021
AdaViT: Adaptive Tokens for Efficient Vision Transformer
We introduce AdaViT, a method that adaptively adjusts the inference cost...
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12/14/2021
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion
Score-based generative models (SGMs) have demonstrated remarkable synthe...
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11/01/2021
Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Although machine learning models trained on massive data have led to bre...
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10/21/2021
Controllable and Compositional Generation with Latent-Space Energy-Based Models
Controllable generation is one of the key requirements for successful ad...
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06/10/2021
Score-based Generative Modeling in Latent Space
Score-based generative models (SGMs) have recently demonstrated impressi...
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06/07/2021
Multi-task Transformation Learning for Robust Out-of-Distribution Detection
Detecting out-of-distribution (OOD) samples plays a key role in open-wor...
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04/15/2021
See through Gradients: Image Batch Recovery via GradInversion
Training deep neural networks requires gradient estimation from data bat...
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10/06/2020
NCP-VAE: Variational Autoencoders with Noise Contrastive Priors
Variational autoencoders (VAEs) are one of the powerful likelihood-based...
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10/01/2020
VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models
Energy-based models (EBMs) have recently been successful in representing...
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07/08/2020
NVAE: A Deep Hierarchical Variational Autoencoder
Normalizing flows, autoregressive models, variational autoencoders (VAEs...
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06/17/2020
Contrastive Learning for Weakly Supervised Phrase Grounding
Phrase grounding, the problem of associating image regions to caption wo...
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02/09/2020
On the distance between two neural networks and the stability of learning
How far apart are two neural networks? This is a foundational question i...
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12/16/2019
UNAS: Differentiable Architecture Search Meets Reinforcement Learning
Neural architecture search (NAS) aims to discover network architectures ...
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04/04/2019
A Robust Learning Approach to Domain Adaptive Object Detection
Domain shift is unavoidable in real-world applications of object detecti...
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01/11/2019
Learning Undirected Posteriors by Backpropagation through MCMC Updates
The representation of the posterior is a critical aspect of effective va...
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11/17/2018
Weakly Supervised Semantic Image Segmentation with Self-correcting Networks
Building a large image dataset with high-quality object masks for semant...
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09/29/2018
Improved Gradient-Based Optimization Over Discrete Distributions
In many applications we seek to maximize an expectation with respect to ...
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05/18/2018
DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors
Boltzmann machines are powerful distributions that have been shown to be...
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02/14/2018
DVAE++: Discrete Variational Autoencoders with Overlapping Transformations
Training of discrete latent variable models remains challenging because ...
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05/31/2017
Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks
Collecting large training datasets, annotated with high-quality labels, ...
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07/09/2016
Hierarchical Deep Temporal Models for Group Activity Recognition
In this paper we present an approach for classifying the activity perfor...
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11/13/2015
Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition
Rich semantic relations are important in a variety of visual recognition...
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02/12/2015
Discovering Human Interactions in Videos with Limited Data Labeling
We present a novel approach for discovering human interactions in videos...
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02/06/2015
