Search Results - "Dhariwal, Prafulla"

Improved Techniques for Training Consistency Models by Song, Yang, Dhariwal, Prafulla

“…Consistency models are a nascent family of generative models that can sample high quality data in one step without the need for adversarial training. Current…”
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Diffusion Models Beat GANs on Image Synthesis by Dhariwal, Prafulla, Nichol, Alex

“…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…”
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Improved Denoising Diffusion Probabilistic Models by Nichol, Alex, Dhariwal, Prafulla

“…Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with…”
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Consistency Models by Song, Yang, Dhariwal, Prafulla, Chen, Mark, Sutskever, Ilya

“…Diffusion models have significantly advanced the fields of image, audio, and video generation, but they depend on an iterative sampling process that causes…”
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Point-E: A System for Generating 3D Point Clouds from Complex Prompts by Nichol, Alex, Jun, Heewoo, Dhariwal, Prafulla, Mishkin, Pamela, Chen, Mark

“…While recent work on text-conditional 3D object generation has shown promising results, the state-of-the-art methods typically require multiple GPU-hours to…”
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Hierarchical Text-Conditional Image Generation with CLIP Latents by Ramesh, Aditya, Dhariwal, Prafulla, Nichol, Alex, Chu, Casey, Chen, Mark

“…Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations…”
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Glow: Generative Flow with Invertible 1x1 Convolutions by Kingma, Diederik P, Dhariwal, Prafulla

“…Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact…”
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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models by Nichol, Alex, Dhariwal, Prafulla, Ramesh, Aditya, Shyam, Pranav, Mishkin, Pamela, McGrew, Bob, Sutskever, Ilya, Chen, Mark

“…Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity…”
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Jukebox: A Generative Model for Music by Dhariwal, Prafulla, Jun, Heewoo, Payne, Christine, Kim, Jong Wook, Radford, Alec, Sutskever, Ilya

“…We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to…”
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GamePad: A Learning Environment for Theorem Proving by Huang, Daniel, Dhariwal, Prafulla, Song, Dawn, Sutskever, Ilya

“…In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof…”
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Proximal Policy Optimization Algorithms by Schulman, John, Wolski, Filip, Dhariwal, Prafulla, Radford, Alec, Klimov, Oleg

“…We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment,…”
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Scaling Laws for Autoregressive Generative Modeling by Henighan, Tom, Kaplan, Jared, Katz, Mor, Chen, Mark, Hesse, Christopher, Jackson, Jacob, Jun, Heewoo, Brown, Tom B, Dhariwal, Prafulla, Gray, Scott, Hallacy, Chris, Mann, Benjamin, Radford, Alec, Ramesh, Aditya, Ryder, Nick, Ziegler, Daniel M, Schulman, John, Amodei, Dario, McCandlish, Sam

“…We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal image$\leftrightarrow$text…”
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Language Models are Few-Shot Learners by Brown, Tom B, Mann, Benjamin, Ryder, Nick, Subbiah, Melanie, Kaplan, Jared, Dhariwal, Prafulla, Neelakantan, Arvind, Shyam, Pranav, Sastry, Girish, Askell, Amanda, Agarwal, Sandhini, Herbert-Voss, Ariel, Krueger, Gretchen, Henighan, Tom, Child, Rewon, Ramesh, Aditya, Ziegler, Daniel M, Wu, Jeffrey, Winter, Clemens, Hesse, Christopher, Chen, Mark, Sigler, Eric, Litwin, Mateusz, Gray, Scott, Chess, Benjamin, Clark, Jack, Berner, Christopher, McCandlish, Sam, Radford, Alec, Sutskever, Ilya, Amodei, Dario

“…Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific…”
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Parameter Space Noise for Exploration by Plappert, Matthias, Houthooft, Rein, Dhariwal, Prafulla, Sidor, Szymon, Chen, Richard Y, Chen, Xi, Asfour, Tamim, Abbeel, Pieter, Andrychowicz, Marcin

“…Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise…”
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Variational Lossy Autoencoder by Chen, Xi, Kingma, Diederik P, Salimans, Tim, Duan, Yan, Dhariwal, Prafulla, Schulman, John, Sutskever, Ilya, Abbeel, Pieter

“…Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification…”
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