3.6k citations · 9.6k across the 48 of their papers we have counts for
52 papers
SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs
Lijun Yu, Yong Cheng, Zhiruo Wang +10
In this work, we introduce Semantic Pyramid AutoEncoder (SPAE) for enabling frozen LLMs to perform both understanding and generation tasks involving non-linguistic modalities such…
Muse: Text-To-Image Generation via Masked Generative Transformers
Huiwen Chang, Han Zhang, Jarred Barber +9
We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregres…
Efficient Online Bayesian Inference for Neural Bandits
Gerardo Duran-Martin, Aleyna Kara, Kevin Murphy
In this paper we present a new algorithm for online (sequential) inference in Bayesian neural networks, and show its suitability for tackling contextual bandit problems. The key id…
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
Ines Chami, Sami Abu-El-Haija, Bryan Perozzi +2
There has been a surge of recent interest in learning representations for graph-structured data. Graph representation learning methods have generally fallen into three main categor…
Towards Differentiable Resampling
Michael Zhu, Kevin Murphy, Rico Jonschkowski
Resampling is a key component of sample-based recursive state estimation in particle filters. Recent work explores differentiable particle filters for end-to-end learning. However,…
Regularized Autoencoders via Relaxed Injective Probability Flow
Abhishek Kumar, Ben Poole, Kevin Murphy
Invertible flow-based generative models are an effective method for learning to generate samples, while allowing for tractable likelihood computation and inference. However, the in…