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This paper proposes a zero-shot learning framework for multivariate IoT traffic anomaly detection using adversarial and contrastive learning within a variational autoencoder, enabling domain adaptation without labeled data and demonstrating strong performance across diverse datasets.
Luce unifies geometry and PBR materials in a voxelized Gaussian cloud to generate relightable 3D assets from single images, achieving state-of-the-art results on benchmarks like Toys4K.
CLaST introduces a context-aware contrastive VAE framework for probabilistic multivariate time series forecasting, demonstrating significant performance improvements over baseline methods on multiple benchmarks.
This paper releases an open eleven-structure cardiac CT statistical shape model and compares shape completion methods, demonstrating that a closed-form conditional-Gaussian estimator outperforms deep learning approaches like variational autoencoders.
This paper studies how β-VAEs act as effective theories where the KL weight acts as a spectral cutoff, and analyzes how nonlinear interactions and network depth affect the tolerance-dependent effective dimension of representations.
Introduces ELVAE, a VAE with evidential learning that models latent coordinates with normal-inverse-gamma posteriors to obtain explicit uncertainty estimates. Experiments on MNIST show that within-class uncertainty ranking can stratify synthetic sample reliability and stress-testing, though results require class-wise normalization and vary across seeds.
This paper presents an AI-driven multimodal mediation framework using variational autoencoders to discover latent pathways linking socioeconomic disadvantage, psychosocial factors, and cardiometabolic multimorbidity in the All of Us Research Program cohort.
Proposes a two-timescale multi-layer deep reinforcement learning framework with latent action space for joint service placement, computational delegation, and power control in hierarchical edge-cloud computing, achieving up to 20.8% latency reduction and 13% resource utilization improvement.
This paper presents BattVAE-GP, a hybrid physics-probabilistic framework that combines a Variational Autoencoder with a sparse multitask Gaussian Process to generate and interpolate long-horizon battery degradation trajectories with uncertainty estimates, enabling efficient surrogate modeling for lithium-ion battery health prediction.
Presents a Variational Graph Autoencoder (VGAE) for detecting power flow solution feasibility in electric power networks, using the IEEE 118-bus case. The method distinguishes between problem infeasibility and algorithm non-convergence.
Identifies two coupled causes of posterior collapse in VAEs and introduces λ-VAE, a modification to the reparameterization step that equalizes variance across latent dimensions, reducing collapse and improving information capacity.
Proposes an end-to-end agentic pipeline combining SSA-LSTM forecasting, LSTM VAE anomaly detection, and LLM-based reasoning with dynamic retrieval to generate prioritized maintenance recommendations for appliance-level energy anomalies, achieving a 90.4/100 score on a 16-scenario benchmark.
This paper presents a discrete autoregressive transformer that generates planar mechanisms from target coupler curves, using variational autoencoder latents and tokenized joint coordinates to achieve diverse, accurate designs across multiple topologies.
This paper proposes a hybrid classical-quantum variational autoencoder for neural topic modeling, embedding parameterized quantum circuits in the inference network. Experiments on the AgNews dataset demonstrate improved topic coherence and diversity compared to state-of-the-art classical models, showing viability on NISQ-era quantum devices.
DeepRHP is a hybrid variational autoencoder that guides the design of random heteropolymers as protein mimics, demonstrated by stabilizing membrane proteins like Aquaporin Z in non-native environments.
This paper presents a Mahalanobis-guided latent out-of-distribution detection method using a VAE to switch between a reinforcement learning controller and an extremum seeking controller in time-varying systems, validated in particle accelerator control.
This paper introduces GNOVA, a GRU-Neural ODE Variational Autoencoder framework for reconstructing and forecasting Alzheimer's disease cognitive trajectories from routine clinical data without expensive neuroimaging or biomarkers, achieving low error and uncertainty estimation on the ADNI dataset.
This paper presents a probabilistic post-processing framework using conditional variational autoencoders (cVAEs) to bias-adjust seasonal forecasts of Arctic sea ice, improving calibration, sharpness, and spectral power over standard methods.
This paper introduces the neural codebook channel diagnostic for VAEs, which measures encoder-decoder disagreement and provides a certificate bounded by the variational gap, enabling detection of mismatched decoding in deep generative models.
This paper presents Conv-VaDE, a variational deep embedding model for interpretable EEG microstate discovery that jointly learns topographic reconstruction and probabilistic soft clustering. It includes a systematic architecture search evaluated on resting-state EEG data to determine optimal model configurations for stability and interpretability.