Search Results for author:
Found 13 papers, 9 papers with code
VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models
We show that a text-conditioned diffusion model trained on pixel representations of images can be used to generate SVG-exportable vector graphics.
DreamFusion: Text-to-3D using 2D Diffusion
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.
Ranked #5 on
Text to 3D
on T$^3$Bench
AdaCat: Adaptive Categorical Discretization for Autoregressive Models
Autoregressive generative models can estimate complex continuous data distributions, like trajectory rollouts in an RL environment, image intensities, and audio.
Zero-Shot Text-Guided Object Generation with Dream Fields
Our method, Dream Fields, can generate the geometry and color of a wide range of objects without 3D supervision.
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis
We present DietNeRF, a 3D neural scene representation estimated from a few images.
Contrastive Code Representation Learning
Recent work learns contextual representations of source code by reconstructing tokens from their context.
Ranked #1 on
Method name prediction
on CodeSearchNet
Locally Masked Convolution for Autoregressive Models
For tasks such as image completion, these models are unable to use much of the observed context.
Ranked #1 on
Image Generation
on MNIST
Denoising Diffusion Probabilistic Models
We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.
Ranked #2 on
Image Generation
on LSUN Bedroom
Sparse Graphical Memory for Robust Planning
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.
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
Particularly difficult is the prediction of human behavior.
Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization
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
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
Current trends in Machine Learning~(ML) inference on hardware accelerated devices (e. g., GPUs, TPUs) point to alarmingly low utilization.
