2.3k citations · 8.4k across the 16 of their papers we have counts for
6 papers · 1 filter
Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun +55
We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex p…
Diffusion Models Beat GANs on Image Synthesis
Prafulla Dhariwal, Alex Nichol
We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by findi…
Improved Denoising Diffusion Probabilistic Models
Alex Nichol, Prafulla Dhariwal
Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modific…
VQ-DRAW: A Sequential Discrete VAE
Alex Nichol
In this paper, I present VQ-DRAW, an algorithm for learning compact discrete representations of data. VQ-DRAW leverages a vector quantization effect to adapt the sequential generat…
Gotta Learn Fast: A New Benchmark for Generalization in RL
Alex Nichol, Vicki Pfau, Christopher Hesse +2
In this report, we present a new reinforcement learning (RL) benchmark based on the Sonic the Hedgehog (TM) video game franchise. This benchmark is intended to measure the performa…
On First-Order Meta-Learning Algorithms
Alex Nichol, Joshua Achiam, John Schulman
This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.e., learns quickly) when presented w…