Search Results for author: Ajay Jain

Found 13 papers, 9 papers with code

VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models

no code implementations CVPR 2023 Ajay Jain, Amber Xie, Pieter Abbeel

We show that a text-conditioned diffusion model trained on pixel representations of images can be used to generate SVG-exportable vector graphics.

Image Generation Text to 3D +1

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

4 code implementations29 Sep 2022 Ben Poole, Ajay Jain, Jonathan T. Barron, Ben Mildenhall

Using this loss in a DeepDream-like procedure, we optimize a randomly-initialized 3D model (a Neural Radiance Field, or NeRF) via gradient descent such that its 2D renderings from random angles achieve a low loss.

Denoising Image Generation +1

AdaCat: Adaptive Categorical Discretization for Autoregressive Models

1 code implementation3 Aug 2022 Qiyang Li, Ajay Jain, Pieter Abbeel

Autoregressive generative models can estimate complex continuous data distributions, like trajectory rollouts in an RL environment, image intensities, and audio.

Density Estimation Offline RL

Zero-Shot Text-Guided Object Generation with Dream Fields

4 code implementations CVPR 2022 Ajay Jain, Ben Mildenhall, Jonathan T. Barron, Pieter Abbeel, Ben Poole

Our method, Dream Fields, can generate the geometry and color of a wide range of objects without 3D supervision.

Neural Rendering

Locally Masked Convolution for Autoregressive Models

1 code implementation22 Jun 2020 Ajay Jain, Pieter Abbeel, Deepak Pathak

For tasks such as image completion, these models are unable to use much of the observed context.

Anomaly Detection Density Estimation +2

Denoising Diffusion Probabilistic Models

58 code implementations NeurIPS 2020 Jonathan Ho, Ajay Jain, Pieter Abbeel

We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.

Denoising Image Generation

Sparse Graphical Memory for Robust Planning

1 code implementation NeurIPS 2020 Scott Emmons, Ajay Jain, Michael Laskin, Thanard Kurutach, Pieter Abbeel, Deepak Pathak

To operate effectively in the real world, agents should be able to act from high-dimensional raw sensory input such as images and achieve diverse goals across long time-horizons.

Imitation Learning Visual Navigation

Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization

2 code implementations7 Oct 2019 Paras Jain, Ajay Jain, Aniruddha Nrusimha, Amir Gholami, Pieter Abbeel, Kurt Keutzer, Ion Stoica, Joseph E. Gonzalez

We formalize the problem of trading-off DNN training time and memory requirements as the tensor rematerialization optimization problem, a generalization of prior checkpointing strategies.

Using effective dimension to analyze feature transformations in deep neural networks

no code implementations ICML Workshop Deep_Phenomen 2019 Kavya Ravichandran, Ajay Jain, Alexander Rakhlin

In a typical deep learning approach to a computer vision task, Convolutional Neural Networks (CNNs) are used to extract features at varying levels of abstraction from an image and compress a high dimensional input into a lower dimensional decision space through a series of transformations.

The OoO VLIW JIT Compiler for GPU Inference

no code implementations28 Jan 2019 Paras Jain, Xiangxi Mo, Ajay Jain, Alexey Tumanov, Joseph E. Gonzalez, Ion Stoica

Current trends in Machine Learning~(ML) inference on hardware accelerated devices (e. g., GPUs, TPUs) point to alarmingly low utilization.

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