Search Results for author:
Found 18 papers, 10 papers with code
Conditional Augmentation for Generative Modeling
We present conditional augmentation (CondAugment), a simple and powerful method of regularizing generative models.
Exploring through Random Curiosity with General Value Functions
Furthermore, RC-GVF significantly outperforms previous methods in the absence of ground-truth episodic counts in the partially observable MiniGrid environments.
The Benefits of Model-Based Generalization in Reinforcement Learning
Model-Based Reinforcement Learning (RL) is widely believed to have the potential to improve sample efficiency by allowing an agent to synthesize large amounts of imagined experience.
Model-based Reinforcement Learning
reinforcement-learning
+1
Goal-Conditioned Generators of Deep Policies
A form of weight-sharing HyperNetworks and policy embeddings scales our method to generate deep NNs.
General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States
In continuous control problems with infinitely many states, our value function minimizes its prediction error by simultaneously learning a small set of `probing states' and a mapping from actions produced in probing states to the policy's return.
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 #20 on
Text-to-Image Generation
on COCO
(using extra training data)
Conditional Image Generation
Zero-Shot Text-to-Image Generation
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 #23 on
Text-to-Image Generation
on COCO
(using extra training data)
Unsupervised Neural Machine Translation with Generative Language Models Only
We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models.
Learning Transferable Visual Models From Natural Language Supervision
State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
(using extra training data)
Zero-Shot Text-to-Image Generation
Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset.
Ranked #35 on
Text-to-Image Generation
on COCO
(using extra training data)
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.
Recurrent Neural-Linear Posterior Sampling for Non-Stationary Contextual Bandits
An agent in a non-stationary contextual bandit problem should balance between exploration and the exploitation of (periodic or structured) patterns present in its previous experiences.
CompressNet: Generative Compression at Extremely Low Bitrates
Compressing images at extremely low bitrates (< 0. 1 bpp) has always been a challenging task since the quality of reconstruction significantly reduces due to the strong imposed constraint on the number of bits allocated for the compressed data.
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.
A Spectral Regularizer for Unsupervised Disentanglement
A generative model with a disentangled representation allows for independent control over different aspects of the output.
Backpropagation for Implicit Spectral Densities
We introduce a tool that allows us to do this even when the likelihood is not explicitly set, by instead using the implicit likelihood of the model.
Disentangling factors of variation in deep representation using adversarial training
The only available source of supervision during the training process comes from our ability to distinguish among different observations belonging to the same category.
Disentangling factors of variation in deep representations using adversarial training
During training, the only available source of supervision comes from our ability to distinguish among different observations belonging to the same class.
