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An Introduction to Variational Autoencoders
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Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important extensions.
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Cited by 11 Pith papers
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DecNefSimulator: A Modular, Interpretable Framework for Decoded Neurofeedback Simulation Using Generative Models
A generative-model simulator of decoded neurofeedback shows that alternative-class choice, initial cognitive state, and random exploration jointly determine whether simulated participants learn or appear as non-responders.
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Accelerated Sampling from Masked Diffusion Models via Entropy Bounded Unmasking
EB-Sampler dynamically unmasks multiple low-entropy tokens per function evaluation, accelerating masked diffusion model sampling by 2-3x with negligible accuracy loss.
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Thompson Sampling in Online RLHF with General Function Approximation
A model-free posterior sampling algorithm for online RLHF is shown to achieve O(sqrt(T)) regret when the completed function class has low Bellman eluder dimension.
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Variational Autoencoder Layer
VAEs can be recast as individual neural layers and trained without back-propagation via a multimodal ELBO, yet the resulting shallow classifiers reach only modest accuracy on standard image benchmarks.
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Causal Transfer in Medical Image Analysis
Causal Transfer Learning unifies structural causal models, invariant risk minimisation and counterfactuals with transfer learning to produce domain-robust medical image models.
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Case Studies of Generative Machine Learning Models for Dynamical Systems
Physics-informed VAEs with Hamiltonian-based losses generate trajectories that match training distributions and satisfy optimal-control equations from as few as 200 to 500 samples.
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Half-AVAE: Adversarial-Enhanced Factorized and Structured Encoder-Free VAE for Underdetermined Independent Component Analysis
Half-AVAE adds adversarial independence training and hand-tuned external regularizers to an encoder-free VAE and reports improved source recovery on one synthetic underdetermined ICA dataset.
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Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization
GCReinSL adds Q-conditioned maximization to supervised offline RL, using normalizing flows to estimate goal-reaching probabilities and expectile regression to condition actions on the best in-distribution value, impro...
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Latent Sensor Fusion: Multimedia Learning of Physiological Signals for Resource-Constrained Devices
A single image-pretrained VQ-VAE encoder, applied to spectrograms of six physiological signals, matches a modality-specific fusion baseline on WESAD stress classification while using less compute and memory.
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Towards Foundation Auto-Encoders for Time-Series Anomaly Detection
A univariate VAE with dilated convolutions is proposed as a simple 'foundation' model for time-series anomaly detection, with preliminary zero-shot experiments on two datasets.
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A Vision Toward Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents
A position paper proposing compact, domain-specific AI agents as the path to ≥1000× energy efficiency, without demonstrating the claim.
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