b'Ben Poole'

research

∙ 07/14/2023

Variational Prediction

Bayesian inference offers benefits over maximum likelihood, but it also ...

0 Alexander A. Alemi, et al. ∙

research

∙ 06/01/2023

Diffusion Self-Guidance for Controllable Image Generation

Large-scale generative models are capable of producing high-quality imag...

0 Dave Epstein, et al. ∙

research

∙ 04/27/2023

Learning a Diffusion Prior for NeRFs

Neural Radiance Fields (NeRFs) have emerged as a powerful neural 3D repr...

0 Guandao Yang, et al. ∙

research

∙ 03/23/2023

DreamBooth3D: Subject-Driven Text-to-3D Generation

We present DreamBooth3D, an approach to personalize text-to-3D generativ...

0 Amit Raj, et al. ∙

research

∙ 11/17/2022

VeLO: Training Versatile Learned Optimizers by Scaling Up

While deep learning models have replaced hand-designed features across m...

0 Luke Metz, et al. ∙

research

∙ 10/05/2022

Imagen Video: High Definition Video Generation with Diffusion Models

We present Imagen Video, a text-conditional video generation system base...

16 Jonathan Ho, et al. ∙

research

∙ 09/29/2022

DreamFusion: Text-to-3D using 2D Diffusion

Recent breakthroughs in text-to-image synthesis have been driven by diff...

16 Ben Poole, et al. ∙

research

∙ 12/02/2021

Zero-Shot Text-Guided Object Generation with Dream Fields

We combine neural rendering with multi-modal image and text representati...

0 Ajay Jain, et al. ∙

research

∙ 10/05/2021

Autoregressive Diffusion Models

We introduce Autoregressive Diffusion Models (ARDMs), a model class enco...

3 Emiel Hoogeboom, et al. ∙

research

∙ 07/01/2021

Variational Diffusion Models

Diffusion-based generative models have demonstrated a capacity for perce...

7 Diederik P. Kingma, et al. ∙

research

∙ 11/17/2020

VIB is Half Bayes

In discriminative settings such as regression and classification there a...

0 Alexander A. Alemi, et al. ∙

research

∙ 10/15/2020

Non-saturating GAN training as divergence minimization

Non-saturating generative adversarial network (GAN) training is widely u...

0 Matt Shannon, et al. ∙

research

∙ 09/23/2020

Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves

Much as replacing hand-designed features with learned functions has revo...

12 Luke Metz, et al. ∙

research

∙ 05/20/2020

What makes for good views for contrastive learning

Contrastive learning between multiple views of the data has recently ach...

16 Yonglong Tian, et al. ∙

research

∙ 02/27/2020

Using a thousand optimization tasks to learn hyperparameter search strategies

We present TaskSet, a dataset of tasks for use in training and evaluatin...

71 Luke Metz, et al. ∙

research

∙ 02/20/2020

Regularized Autoencoders via Relaxed Injective Probability Flow

Invertible flow-based generative models are an effective method for lear...

5 Abhishek Kumar, et al. ∙

research

∙ 02/07/2020

Weakly-Supervised Disentanglement Without Compromises

Intelligent agents should be able to learn useful representations by obs...

20 Francesco Locatello, et al. ∙

research

∙ 01/31/2020

On Implicit Regularization in β-VAEs

While the impact of variational inference (VI) on posterior inference in...

9 Abhishek Kumar, et al. ∙

research

∙ 10/22/2019

Weakly Supervised Disentanglement with Guarantees

Learning disentangled representations that correspond to factors of vari...

12 Rui Shu, et al. ∙

research

∙ 10/21/2019

On Predictive Information Sub-optimality of RNNs

Certain biological neurons demonstrate a remarkable capability to optima...

0 Zhe Dong, et al. ∙

research

∙ 06/06/2019

Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation

Deploying machine learning systems in the real world requires both high ...

0 Raphael Gontijo Lopes, et al. ∙

research

∙ 05/24/2019

Discrete Flows: Invertible Generative Models of Discrete Data

While normalizing flows have led to significant advances in modeling hig...

6 Dustin Tran, et al. ∙

research

∙ 05/16/2019

On Variational Bounds of Mutual Information

Estimating and optimizing Mutual Information (MI) is core to many proble...

8 Ben Poole, et al. ∙

research

∙ 01/10/2019

Preventing Posterior Collapse with delta-VAEs

Due to the phenomenon of "posterior collapse," current latent variable g...

4 Ali Razavi, et al. ∙

research

∙ 11/01/2017

An Information-Theoretic Analysis of Deep Latent-Variable Models

We present an information-theoretic framework for understanding trade-of...

0 Alexander A. Alemi, et al. ∙

research

∙ 03/13/2017

Continual Learning Through Synaptic Intelligence

While deep learning has led to remarkable advances across diverse applic...

0 Friedemann Zenke, et al. ∙

research

∙ 11/24/2016

Survey of Expressivity in Deep Neural Networks

We survey results on neural network expressivity described in "On the Ex...

0 Jojo Yun, et al. ∙

research

∙ 11/07/2016

Unrolled Generative Adversarial Networks

We introduce a method to stabilize Generative Adversarial Networks (GANs...

0 Luke Metz, et al. ∙

research

∙ 11/03/2016

Categorical Reparameterization with Gumbel-Softmax

Categorical variables are a natural choice for representing discrete str...

0 Eric Jang, et al. ∙

research

∙ 06/16/2016

Exponential expressivity in deep neural networks through transient chaos

We combine Riemannian geometry with the mean field theory of high dimens...

0 Ben Poole, et al. ∙

research

∙ 06/16/2016

On the Expressive Power of Deep Neural Networks

We propose a new approach to the problem of neural network expressivity,...

0 Jojo Yun, et al. ∙

research

∙ 06/02/2016

Adversarially Learned Inference

We introduce the adversarially learned inference (ALI) model, which join...

0 Vincent Dumoulin, et al. ∙

research

∙ 11/10/2015

The Fast Bilateral Solver

We present the bilateral solver, a novel algorithm for edge-aware smooth...

0 Jonathan T. Barron, et al. ∙

research

∙ 06/06/2014

Analyzing noise in autoencoders and deep networks

Autoencoders have emerged as a useful framework for unsupervised learnin...

0 Ben Poole, et al. ∙

Ben Poole

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