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Variational Inference

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TitleStatusHype
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process—0
Learning the joint distribution of two sequences using little or no paired data—0
Interpretable Dynamics Models for Data-Efficient Reinforcement Learning—0
Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder—0
Interpretable User Models via Decision-rule Gaussian Processes: Preliminary Results on Energy Storage—0
An Equivalence between Bayesian Priors and Penalties in Variational Inference—0
IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data—0
Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images—0
Introducing Routing Uncertainty in Capsule Networks—0
Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification—0
ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks—0
ISA-VAE: Independent Subspace Analysis with Variational Autoencoders—0
Is MC Dropout Bayesian?—0
Isometric Gaussian Process Latent Variable Model for Dissimilarity Data—0
D-GAN: Deep Generative Adversarial Nets for Spatio-Temporal Prediction—0
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels—0
A Variational Perspective on Generative Flow Networks—0
Portfolio Stress Testing and Value at Risk (VaR) Incorporating Current Market Conditions—0
Analog Bayesian neural networks are insensitive to the shape of the weight distribution—0
Mixture Representation Learning with Coupled Autoencoders—0
Joint Mapping and Calibration via Differentiable Sensor Fusion—0
Joint Modeling of Visual Objects and Relations for Scene Graph Generation—0
A Factorial Mixture Prior for Compositional Deep Generative Models—0
Learning to Dequantise with Truncated Flows—0
Learning with Importance Weighted Variational Inference: Asymptotics for Gradient Estimators of the VR-IWAE Bound—0
A Deterministic Approximation to Neural SDEs—0
Deterministic Fokker-Planck Transport -- With Applications to Sampling, Variational Inference, Kernel Mean Embeddings & Sequential Monte Carlo—0
An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications—0
Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation—0
Learning Optimal Filters Using Variational Inference—0
Dependency Grammar Induction with a Neural Variational Transition-based Parser—0
Bayesian Hypernetworks—0
Deep Variational Inference Without Pixel-Wise Reconstruction—0
Bayesian Hierarchical Mixtures of Experts—0
An Instability in Variational Inference for Topic Models—0
Learning proposals for sequential importance samplers using reinforced variational inference—0
An Information Theoretic Approach to Distributed Representation Learning—0
Bayesian Exploration Networks—0
A Dynamic Edge Exchangeable Model for Sparse Temporal Networks—0
Deep Transformed Gaussian Processes—0
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences—0
Learning noisy-OR Bayesian Networks with Max-Product Belief Propagation—0
Bayesian Estimation and Tuning-Free Rank Detection for Probability Mass Function Tensors—0
Deep State Space Models for Unconditional Word Generation—0
Bayesian Domain Invariant Learning via Posterior Generalization of Parameter Distributions—0
An information-theoretic analysis of deep latent-variable models—0
Learning Robot Skills with Temporal Variational Inference—0
Deep Reinforcement Learning with Weighted Q-Learning—0
Learning Invariances using the Marginal Likelihood—0
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model—0
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