Search Results for author:
Found 17 papers, 14 papers with code
GPT-4o System Card
We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.
Ranked #1 on
Visual Question Answering (VQA)
on VLM2-Bench
Improved Techniques for Training Consistency Models
Consistency models are a nascent family of generative models that can sample high quality data in one step without the need for adversarial training.
Consistency Models
Through extensive experiments, we demonstrate that they outperform existing distillation techniques for diffusion models in one- and few-step sampling, achieving the new state-of-the-art FID of 3. 55 on CIFAR-10 and 6. 20 on ImageNet 64x64 for one-step generation.
Ranked #23 on
Image Generation
on ImageNet 64x64
Point-E: A System for Generating 3D Point Clouds from Complex Prompts
This is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes.
Hierarchical Text-Conditional Image Generation with CLIP Latents
Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style.
Ranked #27 on
Text-to-Image Generation
on MS COCO
(using extra training data)
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity.
Ranked #32 on
Text-to-Image Generation
on MS COCO
(using extra training data)
Diffusion Models Beat GANs on Image Synthesis
Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3. 94 on ImageNet 256$\times$256 and 3. 85 on ImageNet 512$\times$512.
Ranked #1 on
Image Generation
on LSUN Bedroom 256 x 256
(FD metric)
Improved Denoising Diffusion Probabilistic Models
Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples.
Ranked #20 on
Image Generation
on ImageNet 64x64
Scaling Laws for Autoregressive Generative Modeling
The optimal model size also depends on the compute budget through a power-law, with exponents that are nearly universal across all data domains.
Generative Pretraining from Pixels
Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images.
Ranked #14 on
Image Classification
on STL-10
(using extra training data)
Representation Learning
Self-Supervised Image Classification
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
Ranked #1 on
Question Answering
on CoQA
(Overall metric)
Jukebox: A Generative Model for Music
We introduce Jukebox, a model that generates music with singing in the raw audio domain.
Glow: Generative Flow with Invertible 1x1 Convolutions
Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference, and parallelizability of both training and synthesis.
Ranked #5 on
Density Estimation
on ImageNet 32x32
GamePad: A Learning Environment for Theorem Proving
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 assistant.
Proximal Policy Optimization Algorithms
We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent.
Parameter Space Noise for Exploration
Combining parameter noise with traditional RL methods allows to combine the best of both worlds.
Variational Lossy Autoencoder
Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification.
