b'Arash Vahdat'

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

∙ 02/12/2023

I^2SB: Image-to-Image Schrödinger Bridge

We propose Image-to-Image Schrödinger Bridge (I^2SB), a new class of con...

6 Guan-Horng Liu, et al. ∙

research

∙ 11/24/2022

Fast Sampling of Diffusion Models via Operator Learning

Diffusion models have found widespread adoption in various areas. Howeve...

17 Hongkai Zheng, 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

∙ 10/18/2022

Differentially Private Diffusion Models

While modern machine learning models rely on increasingly large training...

0 Tim Dockhorn, et al. ∙

research

∙ 10/12/2022

LION: Latent Point Diffusion Models for 3D Shape Generation

Denoising diffusion models (DDMs) have shown promising results in 3D poi...

10 Xiaohui Zeng, et al. ∙

research

∙ 10/11/2022

GENIE: Higher-Order Denoising Diffusion Solvers

Denoising diffusion models (DDMs) have emerged as a powerful class of ge...

0 Tim Dockhorn, et al. ∙

research

∙ 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...

31 Zhuoran Qiao, et al. ∙

research

∙ 05/16/2022

Diffusion Models for Adversarial Purification

Adversarial purification refers to a class of defense methods that remov...

38 Weili Nie, et al. ∙

research

∙ 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 ...

0 Zhisheng Xiao, et al. ∙

research

∙ 12/14/2021

AdaViT: Adaptive Tokens for Efficient Vision Transformer

We introduce AdaViT, a method that adaptively adjusts the inference cost...

0 Hongxu Yin, et al. ∙

research

∙ 12/14/2021

Score-Based Generative Modeling with Critically-Damped Langevin Diffusion

Score-based generative models (SGMs) have demonstrated remarkable synthe...

0 Tim Dockhorn, et al. ∙

research

∙ 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...

6 Tianshi Cao, et al. ∙

research

∙ 10/21/2021

Controllable and Compositional Generation with Latent-Space Energy-Based Models

Controllable generation is one of the key requirements for successful ad...

13 Weili Nie, et al. ∙

research

∙ 06/10/2021

Score-based Generative Modeling in Latent Space

Score-based generative models (SGMs) have recently demonstrated impressi...

0 Arash Vahdat, et al. ∙

research

∙ 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...

0 Sina Mohseni, et al. ∙

research

∙ 04/15/2021

See through Gradients: Image Batch Recovery via GradInversion

Training deep neural networks requires gradient estimation from data bat...

0 Hongxu Yin, et al. ∙

research

∙ 10/06/2020

NCP-VAE: Variational Autoencoders with Noise Contrastive Priors

Variational autoencoders (VAEs) are one of the powerful likelihood-based...

29 Jyoti Aneja, et al. ∙

research

∙ 10/01/2020

VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models

Energy-based models (EBMs) have recently been successful in representing...

23 Zhisheng Xiao, et al. ∙

research

∙ 07/08/2020

NVAE: A Deep Hierarchical Variational Autoencoder

Normalizing flows, autoregressive models, variational autoencoders (VAEs...

22 Arash Vahdat, et al. ∙

research

∙ 06/17/2020

Contrastive Learning for Weakly Supervised Phrase Grounding

Phrase grounding, the problem of associating image regions to caption wo...

4 Tanmay Gupta, et al. ∙

research

∙ 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...

79 Jeremy Bernstein, et al. ∙

research

∙ 12/16/2019

UNAS: Differentiable Architecture Search Meets Reinforcement Learning

Neural architecture search (NAS) aims to discover network architectures ...

20 Arash Vahdat, et al. ∙

research

∙ 04/04/2019

A Robust Learning Approach to Domain Adaptive Object Detection

Domain shift is unavoidable in real-world applications of object detecti...

0 Mehran Khodabandeh, et al. ∙

research

∙ 01/11/2019

Learning Undirected Posteriors by Backpropagation through MCMC Updates

The representation of the posterior is a critical aspect of effective va...

0 Arash Vahdat, et al. ∙

research

∙ 11/17/2018

Weakly Supervised Semantic Image Segmentation with Self-correcting Networks

Building a large image dataset with high-quality object masks for semant...

0 Mostafa S. Ibrahim, et al. ∙

research

∙ 09/29/2018

Improved Gradient-Based Optimization Over Discrete Distributions

In many applications we seek to maximize an expectation with respect to ...

0 Evgeny Andriyash, et al. ∙

research

∙ 05/18/2018

DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors

Boltzmann machines are powerful distributions that have been shown to be...

0 Arash Vahdat, et al. ∙

research

∙ 02/14/2018

DVAE++: Discrete Variational Autoencoders with Overlapping Transformations

Training of discrete latent variable models remains challenging because ...

0 Arash Vahdat, et al. ∙

research

∙ 05/31/2017

Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks

Collecting large training datasets, annotated with high-quality labels, ...

0 Arash Vahdat, et al. ∙

research

∙ 07/09/2016

Hierarchical Deep Temporal Models for Group Activity Recognition

In this paper we present an approach for classifying the activity perfor...

0 Mostafa S. Ibrahim, et al. ∙

research

∙ 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...

0 Zhiwei Deng, et al. ∙

research

∙ 02/12/2015

Discovering Human Interactions in Videos with Limited Data Labeling

We present a novel approach for discovering human interactions in videos...

0 Mehran Khodabandeh, et al. ∙

research

∙ 02/06/2015

Visual Recognition by Counting Instances: A Multi-Instance Cardinality Potential Kernel

Many visual recognition problems can be approached by counting instances...

0 Hossein Hajimirsadeghi, et al. ∙

Arash Vahdat

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