HPO enables unbiased policy optimization in hybrid action spaces by mixing differentiable simulation gradients with score-function estimates, outperforming PPO as continuous dimensions increase.
Discrete variational autoencoders
5 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Probabilistic models with discrete latent variables naturally capture datasets composed of discrete classes. However, they are difficult to train efficiently, since backpropagation through discrete variables is generally not possible. We present a novel method to train a class of probabilistic models with discrete latent variables using the variational autoencoder framework, including backpropagation through the discrete latent variables. The associated class of probabilistic models comprises an undirected discrete component and a directed hierarchical continuous component. The discrete component captures the distribution over the disconnected smooth manifolds induced by the continuous component. As a result, this class of models efficiently learns both the class of objects in an image, and their specific realization in pixels, from unsupervised data, and outperforms state-of-the-art methods on the permutation-invariant MNIST, Omniglot, and Caltech-101 Silhouettes datasets.
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A multi-mode quantum annealing approach enables VAEs with Boltzmann priors, showing faster training and better generation than Gaussian-prior VAEs on MNIST, Fashion-MNIST, and CelebA plus improved out-of-distribution detection.
Scaling an autoregressive Transformer to 20B parameters for text-to-image generation using image token sequences achieves new SOTA zero-shot FID of 7.23 and fine-tuned FID of 3.22 on MS-COCO.
Quantum annealing combined with a Neural Hash Function lets generative models create molecules that are more drug-like than classical versions or the training set itself.
A systematic literature survey that categorizes deep learning architectures for point cloud classification, part segmentation, and semantic segmentation, evaluates them on benchmarks, and discusses innovations, limitations, and future directions.
citing papers explorer
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Policy Optimization in Hybrid Discrete-Continuous Action Spaces via Mixed Gradients
HPO enables unbiased policy optimization in hybrid action spaces by mixing differentiable simulation gradients with score-function estimates, outperforming PPO as continuous dimensions increase.
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Multi-Mode Quantum Annealing for Generative Representation Learning with Boltzmann Priors
A multi-mode quantum annealing approach enables VAEs with Boltzmann priors, showing faster training and better generation than Gaussian-prior VAEs on MNIST, Fashion-MNIST, and CelebA plus improved out-of-distribution detection.
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Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
Scaling an autoregressive Transformer to 20B parameters for text-to-image generation using image token sequences achieves new SOTA zero-shot FID of 7.23 and fine-tuned FID of 3.22 on MS-COCO.
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Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer
Quantum annealing combined with a Neural Hash Function lets generative models create molecules that are more drug-like than classical versions or the training set itself.
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A Systematic Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation
A systematic literature survey that categorizes deep learning architectures for point cloud classification, part segmentation, and semantic segmentation, evaluates them on benchmarks, and discusses innovations, limitations, and future directions.