Phenaki generates arbitrary-length videos from sequences of text prompts by tokenizing videos with causal temporal attention and generating tokens with a text-conditioned masked transformer, trained jointly on images and videos.
Kingma and Jimmy Ba
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
The paper argues for stateful defenses over stateless ones to detect adversarial example generation via query history and introduces query blinding as a counter-attack.
Progressive growing stabilizes GAN training to produce high-resolution images of unprecedented quality and achieves a record unsupervised inception score of 8.80 on CIFAR10.
A two-layer network trained on mixed clean and perturbed logits recovers original predictions for a range of adversarial attacks without needing image data.
Hermes is a multi-scale spatial-temporal hypergraph network that improves stock forecasting accuracy by capturing inter-industry lead-lag dependencies and fusing information across scales.
Provides Hessian-based theoretical characterizations of SGD dynamics and a scale-invariant generalization bound for deep nets, backed by experiments on synthetic data, MNIST, and CIFAR-10.
citing papers explorer
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Phenaki: Variable Length Video Generation From Open Domain Textual Description
Phenaki generates arbitrary-length videos from sequences of text prompts by tokenizing videos with causal temporal attention and generating tokens with a text-conditioned masked transformer, trained jointly on images and videos.
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Stateful Detection of Black-Box Adversarial Attacks
The paper argues for stateful defenses over stateless ones to detect adversarial example generation via query history and introduces query blinding as a counter-attack.
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Progressive Growing of GANs for Improved Quality, Stability, and Variation
Progressive growing stabilizes GAN training to produce high-resolution images of unprecedented quality and achieves a record unsupervised inception score of 8.80 on CIFAR10.
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Defending Adversarial Attacks by Correcting logits
A two-layer network trained on mixed clean and perturbed logits recovers original predictions for a range of adversarial attacks without needing image data.
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Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting
Hermes is a multi-scale spatial-temporal hypergraph network that improves stock forecasting accuracy by capturing inter-industry lead-lag dependencies and fusing information across scales.
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Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization
Provides Hessian-based theoretical characterizations of SGD dynamics and a scale-invariant generalization bound for deep nets, backed by experiments on synthetic data, MNIST, and CIFAR-10.