Auto-Encoding Variational Bayes
2013/12/20 by Diederik P. Kingma, Diederik P Kingma, Max Welling +2 · 3 voices · 1071 citations
Computer Science · #Gaussian Processes and Bayesian Inference #Machine Learning and Algorithms #Bayesian Methods and Mixture Models
paper · pdf · doi:10.48550/arxiv.1312.6114
Abstract
How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions are two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.
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- Modeling Uncertainty with Hedged Instance Embedding
- Class-Conditional VAE-GAN for Local-Ancestry Simulation
- Semi-supervised Neural Chord Estimation Based on a Variational Autoencoder with Latent Chord Labels and Features
- Measuring Dependence with Matrix-based Entropy Functional
- Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
- Diffusion Models in Vision: A Survey
- A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series
- A Correspondence Variational Autoencoder for Unsupervised Acoustic Word Embeddings
- A Theory of Usable Information Under Computational Constraints
- Variational Calibration of Computer Models
- Data Consistent Deep Rigid MRI Motion Correction
- Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
- Anytime Sampling for Autoregressive Models via Ordered Autoencoding
- An Information-Geometric Distance on the Space of Tasks
- Bayesian Meta-reinforcement Learning for Traffic Signal Control
- Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
- From Variational to Deterministic Autoencoders
- DRAW: A Recurrent Neural Network For Image Generation
- Truncated Variational Expectation Maximization
- Sequential Variational Autoencoders for Collaborative Filtering
- NeurIPS 2019 Disentanglement Challenge: Improved Disentanglement through Learned Aggregation of Convolutional Feature Maps
- Deep Facial Expression Recognition: A Survey
- Latent Dirichlet Allocation in Generative Adversarial Networks
- Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learning
- Uncertainty-based Continual Learning with Adaptive Regularization
- Fighting Copycat Agents in Behavioral Cloning from Observation Histories
- Intact-VAE: Estimating Treatment Effects under Unobserved Confounding
- Exploring Autoencoder-based Error-bounded Compression for Scientific Data
- PD-GAN: Probabilistic Diverse GAN for Image Inpainting
- A review of uncertainty quantification in deep learning: Techniques, applications and challenges
- Stochastic Prototype Embeddings
- State representation learning with recurrent capsule networks
- Capacity-Approaching Autoencoders for Communications
- Conditional Variational Autoencoder with Adversarial Learning for\n End-to-End Text-to-Speech
- Noise Aggregation Analysis Driven by Small-Noise Injection: Efficient Membership Inference for Diffusion Models
- Mind the Gap when Conditioning Amortised Inference in Sequential\n Latent-Variable Models
- Block Neural Autoregressive Flow
- Dense Intrinsic Appearance Flow for Human Pose Transfer
- Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach
- Deep Deterministic Information Bottleneck with Matrix-based Entropy Functional
- Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning
- Uniform Discrete Diffusion with Metric Path for Video Generation
- VideoGPT: Video Generation using VQ-VAE and Transformers
- Energy-based Out-of-distribution Detection
- A Comprehensive Evaluation Framework for Synthetic Trip Data Generation in Public Transport
- Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model
- Causal Convolutional Neural Networks as Finite Impulse Response Filters
- Beyond Inference Intervention: Identity-Decoupled Diffusion for Face Anonymization
- WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data
- Hierarchical Autoregressive Image Models with Auxiliary Decoders
- Local Competition and Uncertainty for Adversarial Robustness in Deep Learning
- Advances in Variational Inference
- Disentangling Factors of Variation with Cycle-Consistent Variational\n Auto-Encoders
- How Does GAN-based Semi-supervised Learning Work?
- Disentangled Representation Learning with Wasserstein Total Correlation
- Universally Quantized Neural Compression
- Revealing the Potential of Learnable Perturbation Ensemble Forecast Model for Tropical Cyclone Prediction
- Debiasing Reward Models by Representation Learning with Guarantees
- Autonomous construction of parameterizable 3D leaf models from scanned sweet pepper leaves with deep generative networks
- Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation
- More Than Generation: Unifying Generation and Depth Estimation via Text-to-Image Diffusion Models
- Transporting Causal Mechanisms for Unsupervised Domain Adaptation
- Joint Mapping and Calibration via Differentiable Sensor Fusion
- Character controllers using motion VAEs
- VIKING: Deep variational inference with stochastic projections
- Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach
- Through the Lens: Benchmarking Deepfake Detectors Against Moiré-Induced Distortions
- Matching Reverberant Speech Through Learned Acoustic Embeddings and Feedback Delay Networks
- Coupled Flow Matching
- VALA: Learning Latent Anchors for Training-Free and Temporally Consistent
- Switchable Token-Specific Codebook Quantization For Face Image Compression
- Encoder-Decoder Diffusion Language Models for Efficient Training and Inference
- Explaining by Removing: A Unified Framework for Model Explanation
- Disentangling User Interest and Conformity for Recommendation with Causal Embedding
- LO-SDA: Latent Optimization for Score-based Atmospheric Data Assimilation
- Toward Robust Signed Graph Learning through Joint Input-Target Denoising
- MMbeddings: Parameter-Efficient, Low-Overfitting Probabilistic Embeddings Inspired by Nonlinear Mixed Models
- Scaling Non-Parametric Sampling with Representation
- On Masked Pre-training and the Marginal Likelihood
- Input Adaptive Bayesian Model Averaging
- Predictive Coding Enhances Meta-RL To Achieve Interpretable Bayes-Optimal Belief Representation Under Partial Observability
- Foundation of Intelligence: Review of Math Word Problems from Human Cognition Perspective
- WorldGrow: Generating Infinite 3D World
- Generative Correlation Manifolds: Generating Synthetic Data with Preserved Higher-Order Correlations
- DreamerV3-XP: Optimizing exploration through uncertainty estimation
- Self-diffusion for Solving Inverse Problems
- Low-Complexity MIMO Channel Estimation with Latent Diffusion Models
- Out-of-Distribution Detection for Safety Assurance of AI and Autonomous Systems
- On the flow matching interpretability
- Active Learning: Problem Settings and Recent Developments
- ESCORT: Efficient Stein-variational and Sliced Consistency-Optimized Temporal Belief Representation for POMDPs
- A deep-learning search for technosignatures from 820 nearby stars
- Fisher meets Feynman: score-based variational inference with a product of experts
- Unsupervised Part Discovery from Contrastive Reconstruction
- Fair Representation Learning with Controllable High Confidence Guarantees via Adversarial Inference
- Distilled Decoding 2: One-step Sampling of Image Auto-regressive Models with Conditional Score Distillation
- Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applications
- Exploration through Generation: Applying GFlowNets to Structured Search
- LLM-Integrated Bayesian State Space Models for Multimodal Time-Series Forecasting
- To MCMC or not to MCMC: Evaluating non-MCMC methods for Bayesian penalized regression
- Video-As-Prompt: Unified Semantic Control for Video Generation
- Merlion: A Machine Learning Library for Time Series
- Evidential Turing Processes
- How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation
- Single-View 3D Object Reconstruction from Shape Priors in Memory
- Generating Images with Sparse Representations
- Future Frame Prediction for Robot-assisted Surgery
- Learning Discrete Distributions by Dequantization
- Solving Mixed Integer Programs Using Neural Networks
- Neural Contractive Dynamical Systems
- Face-MakeUpV2: Facial Consistency Learning for Controllable Text-to-Image Generation
- Accelerated WGAN update strategy with loss change rate balancing
- Physics Informed Deep Learning for Transport in Porous Media. Buckley Leverett Problem
- Sparse Graphical Memory for Robust Planning
- Contrastive Variational Reinforcement Learning for Complex Observations
- Continual Density Ratio Estimation in an Online Setting
- CariGAN: Caricature Generation through Weakly Paired Adversarial Learning
- Learning to compress and search visual data in large-scale systems
- Quadratic Autoencoder (Q-AE) for Low-dose CT Denoising
- AliGraph: A Comprehensive Graph Neural Network Platform
- The Evolving Nature of Latent Spaces: From GANs to Diffusion
- Neural Architecture Optimization with Graph VAE
- PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows
- Terra: Explorable Native 3D World Model with Point Latents
- LeapFactual: Reliable Visual Counterfactual Explanation Using Conditional Flow Matching
- An Active Inference Model of Mouse Point-and-Click Behaviour
- Tutorial on Variational Autoencoders
- Exploring Image Representation with Decoupled Classical Visual Descriptors
- Synergistic Integration and Discrepancy Resolution of Contextualized Knowledge for Personalized Recommendation
- Machine Learning Percolation Model
- Overfitting for Fun and Profit: Instance-Adaptive Data Compression
- Edit-Your-Interest: Efficient Video Editing via Feature Most-Similar Propagation
- Isolation-based Spherical Ensemble Representations for Anomaly Detection
- PhysMaster: Mastering Physical Representation for Video Generation via Reinforcement Learning
- Mixture of Dynamical Variational Autoencoders for Multi-Source Trajectory Modeling and Separation
- EMFlow: Data Imputation in Latent Space via EM and Deep Flow Models
- Backpropagation through the Void: Optimizing control variates for black-box gradient estimation
- VCTR: A Transformer-Based Model for Non-parallel Voice Conversion
- Robustness May Be at Odds with Accuracy
- Generalization and Robustness Implications in Object-Centric Learning
- Generic Inference in Latent Gaussian Process Models
- Anomaly Detection with Inexact Labels
- Generative networks as inverse problems with Scattering transforms
- Homotopic Gradients of Generative Density Priors for MR Image Reconstruction
- Unsupervised State Representation Learning in Atari
- GO Gradient for Expectation-Based Objectives
- Learning a Deep ConvNet for Multi-label Classification with Partial Labels
- An Exploration of Learnt Representations of W Jets
- LumièreNet: Lecture Video Synthesis from Audio
- Lossless Compression with Latent Variable Models
- SurpriseNet: Melody Harmonization Conditioning on User-controlled Surprise Contours
- Disentanglement Analysis with Partial Information Decomposition
- A Trifecta of Deep Learning Models: Assessing Brain Health by Integrating Assessment and Neuroimaging Data
- Latent Regression Bayesian Network for Data Representation
- Information Competing Process for Learning Diversified Representations
- Generative Neural Machine Translation
- A Review of Learning with Deep Generative Models from Perspective of Graphical Modeling
- Leveraging the Exact Likelihood of Deep Latent Variable Models
- Detection of Dataset Shifts in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression
- Improving Generalization in Meta Reinforcement Learning using Learned Objectives
- Deep Visual Foresight for Planning Robot Motion
- Physics-Informed High-order Graph Dynamics Identification Learning for Predicting Complex Networks Long-term Dynamics
- Cycle-SUM: Cycle-consistent Adversarial LSTM Networks for Unsupervised Video Summarization
- Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection
- Integer Discrete Flows and Lossless Compression
- Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
- ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis
- Flow-based SVDD for anomaly detection
- Latent Harmony: Synergistic Unified UHD Image Restoration via Latent Space Regularization and Controllable Refinement
- Ctrl-VI: Controllable Video Synthesis via Variational Inference
- Variational Interaction Information Maximization for Cross-domain Disentanglement
- Restrained Generative Adversarial Network against Overfitting in Numeric Data Augmentation
- Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational\n Inference
- Hierarchical Bayesian Model for the Transfer of Knowledge on Spatial Concepts based on Multimodal Information
- OffCon3: What is state of the art anyway?
- Generative Model without Prior Distribution Matching
- Object-Centric Image Generation with Factored Depths, Locations, and\n Appearances
- Towards democratizing music production with AI-Design of Variational Autoencoder-based Rhythm Generator as a DAW plugin
- 3DMolNet: A Generative Network for Molecular Structures
- ReactDiff: Fundamental Multiple Appropriate Facial Reaction Diffusion Model
- Continuous normalizing flows on manifolds
- An End-to-End Framework for Molecular Conformation Generation via\n Bilevel Programming
- Variational Inference with Holder Bounds
- Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?
- Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement
- Causal Discovery from Incomplete Data: A Deep Learning Approach
- DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction
- A gradual, semi-discrete approach to generative network training via\n explicit Wasserstein minimization
- Deep Learning for Physical Processes: Incorporating Prior Scientific\n Knowledge
- Information Potential Auto-Encoders
- Phase Collaborative Network for Two-Phase Medical Image Segmentation
- P-WAE: Generalized Patch-Wasserstein Autoencoder for Anomaly Screening
- Out-of-Sample Testing for GANs
- Adaptive Stress Testing for Autonomous Vehicles
- Masked Autoregressive Flow for Density Estimation
- Variational Bayesian Monte Carlo with Noisy Likelihoods
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
- Expressive Speech Synthesis via Modeling Expressions with Variational Autoencoder
- Can multi-label classification networks know what they don't know?
- DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability
- Act to See, See to Act: Diffusion-Driven Perception-Action Interplay for Adaptive Policies
- SMS: Self-supervised Model Seeding for Verification of Machine Unlearning
- Variational Bayesian Decision-making for Continuous Utilities
- Regularized Autoencoders via Relaxed Injective Probability Flow
- Deep Vocoder: Low Bit Rate Compression of Speech with Deep Autoencoder
- Consensus Message Passing for Layered Graphical Models
- Mixture-of-Variational-Experts for Continual Learning
- Model-Based Reinforcement Learning for Atari
- Deep Auto-encoder with Neural Response
- Hamiltonian Generative Networks
- Learning to Decompose and Disentangle Representations for Video Prediction
- A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning
- Weather GAN: Multi-Domain Weather Translation Using Generative Adversarial Networks
- Anomalous sound detection based on interpolation deep neural network
- Neural Density Estimation and Likelihood-free Inference
- Machine learning for synthetic gene circuit engineering
- DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction
- Unsupervised Recurrent Neural Network Grammars
- A Spectral Regularizer for Unsupervised Disentanglement
- Application-driven validation of posteriors in inverse problems
- Locality and compositionality in zero-shot learning
- A Block-based Generative Model for Attributed Networks Embedding
- Calibration of Model Uncertainty for Dropout Variational Inference
- GO Hessian for Expectation-Based Objectives
- View-Invariant Probabilistic Embedding for Human Pose
- Modeling Grasp Motor Imagery through Deep Conditional Generative Models
- High-Resolution Mammogram Synthesis using Progressive Generative\n Adversarial Networks
- Facing & mitigating common challenges when working with real-world data: The Data Learning Paradigm
- Generative Image Modeling Using Spatial LSTMs
- Model-Augmented Actor-Critic: Backpropagating through Paths
- Learning Product Codebooks using Vector Quantized Autoencoders for Image Retrieval
- Training-Free Synthetic Data Generation with Dual IP-Adapter Guidance
- Deep Temporal Sigmoid Belief Networks for Sequence Modeling
- Community Detection Clustering via Gumbel Softmax
- (Sometimes) Less is More: Mitigating the Complexity of Rule-based Representation for Interpretable Classification
- The molecular memory code and synaptic plasticity: A synthesis
- Graph networks as learnable physics engines for inference and control
- Information bottleneck through variational glasses
- Probabilistic Binary Neural Networks
- Parameter estimation in FACS-seq enables high-throughput characterization of phenotypic heterogeneity
- Robust Locally-Linear Controllable Embedding
- ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked Objects
- Learning Likelihoods with Conditional Normalizing Flows
- Conditional Generative Moment-Matching Networks
- Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
- Parametric UMAP Embeddings for Representation and Semisupervised Learning
- PAC-Bayes Analysis of Sentence Representation
- Weight Uncertainty in Neural Networks
- Canonical Correlation Analysis (CCA) Based Multi-View Learning: An Overview
- Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projections
- Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series
- Sensitivity to geometric shape regularity in humans and baboons: A putative signature of human singularity
- Mathematics for Machine Learning
- Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior
- High Fidelity Face Manipulation with Extreme Poses and Expressions
- Physics-aware, probabilistic model order reduction with guaranteed\n stability
- Efficient Semi-Implicit Variational Inference
- Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty
- Transformations between deep neural networks
- Estimating uncertainty in flood model outputs using machine learning informed by Monte Carlo analysis
- Unsupervised Classification of Street Architectures Based on InfoGAN
- Continual Learning of New Sound Classes using Generative Replay
- TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation
- Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction
- BreGMN: scaled-Bregman Generative Modeling Networks
- ACVAE-VC: Non-parallel many-to-many voice conversion with auxiliary classifier variational autoencoder
- Latent Matters: Learning Deep State-Space Models
- SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation
- EditVAE: Unsupervised Part-Aware Controllable 3D Point Cloud Shape Generation
- Particle Filter Recurrent Neural Networks
- Deep Bayesian Unsupervised Lifelong Learning
- Efficient Heuristic Generation for Robot Path Planning with Recurrent Generative Model
- Generative causal explanations of black-box classifiers
- A Generative Learning Approach for Spatio-temporal Modeling in Connected Vehicular Network
- Semi-supervised Disentanglement with Independent Vector Variational Autoencoders
- Variance Reduction for Evolution Strategies via Structured Control Variates
- Symbolic Music Generation with Diffusion Models
- Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondence
- Practical Lossless Compression with Latent Variables using Bits Back Coding
- Generating and designing DNA with deep generative models
- SSD: A Unified Framework for Self-Supervised Outlier Detection
- A Hierarchical Subspace Model for Language-Attuned Acoustic Unit\n Discovery
- Conditional Generative Modeling via Learning the Latent Space
- The Neglected Sibling: Isotropic Gaussian Posterior for VAE
- VAEs in the Presence of Missing Data
- Adversarial Symmetric Variational Autoencoder
- BEiT: BERT Pre-Training of Image Transformers
- All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference
- Learning geometry-image representation for 3D point cloud generation
- Adversarially Regularized Autoencoders
- Generating unseen complex scenes: are we there yet?
- Learning to Separate Clusters of Adversarial Representations for Robust Adversarial Detection
- Cascaded Text Generation with Markov Transformers
- Focal Frequency Loss for Image Reconstruction and Synthesis
- Generating Sentences by Editing Prototypes
- Variational Latent-State GPT for Semi-Supervised Task-Oriented Dialog Systems
- Structure-Aware Human-Action Generation
- NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
- Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel Attribute Synthesis
- Graph Residual Flow for Molecular Graph Generation
- Deep Amortized Clustering
- Semi-Implicit Generative Model
- Causal Effect Inference with Deep Latent-Variable Models
- Cooperative image captioning
- Deep Active Inference for Autonomous Robot Navigation
- A Unified Particle-Optimization Framework for Scalable Bayesian Sampling
- Cerberus: A Multi-headed Derenderer
- Unsupervised Learning of 3D Structure from Images
- A Model to Search for Synthesizable Molecules
- A deep generative model for gene expression profiles from single-cell RNA sequencing
- Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm
- Angular Dispersion Accelerates k-Nearest Neighbors Machine Translation
- Self-Supervised Adaptation of High-Fidelity Face Models for Monocular\n Performance Tracking
- Variational inference for Monte Carlo objectives
- VAE-based Domain Adaptation for Speaker Verification
- Kuramoto Orientation Diffusion Models
- Computer-Aided Design as Language
- Gradient Boosted Normalizing Flows
- Implicit Greedy Rank Learning in Autoencoders via Overparameterized Linear Networks
- Applying the Information Bottleneck Principle to Prosodic Representation Learning
- G-VAE, a Geometric Convolutional VAE for ProteinStructure Generation
- Learning FRAME Models Using CNN Filters
- Generative Models of Visually Grounded Imagination
- X-Fields: Implicit Neural View-, Light- and Time-Image Interpolation
- Unselfie: Translating Selfies to Neutral-pose Portraits in the Wild
- Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation
- A similarity-based Bayesian mixture-of-experts model
- Unsupervised Discovery of 3D Physical Objects from Video
- CookGAN: Meal Image Synthesis from Ingredients
- Hierarchical Quantized Autoencoders
- From Machine Learning to Robotics: Challenges and Opportunities for\n Embodied Intelligence
- Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks
- Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks
- AGAIN-VC: A One-shot Voice Conversion using Activation Guidance and Adaptive Instance Normalization
- Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization
- Variational Autoencoder for Deep Learning of Images, Labels and Captions
- Identification of Gaussian Process State Space Models
- A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model
- Non-Asymptotic Performance Guarantees for Neural Estimation of\n \f-Divergences
- Learning Disentangled Latent Factors from Paired Data in Cross-Modal Retrieval: An Implicit Identifiable VAE Approach
- Reconstruction and Membership Inference Attacks against Generative\n Models
- Scalable Bayesian Inverse Reinforcement Learning
- TSIT: A Simple and Versatile Framework for Image-to-Image Translation
- Marginalized State Distribution Entropy Regularization in Policy Optimization
- A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification
- Barlow Graph Auto-Encoder for Unsupervised Network Embedding
- Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales
- GLALLM: Adapting LLMs for spatio-temporal wind speed forecasting via global–local aware modeling
- Online Variational Filtering and Parameter Learning
- Conditional Image Generation with Score-Based Diffusion Models
- The continuous Bernoulli: fixing a pervasive error in variational autoencoders
- MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
- Goal-Oriented Gaze Estimation for Zero-Shot Learning
- Diagnosing and Enhancing VAE Models
- A Class of Algorithms for General Instrumental Variable Models
- Black-Box Autoregressive Density Estimation for State-Space Models
- Neural Design Network: Graphic Layout Generation with Constraints
- Rate-Distortion Optimization Guided Autoencoder for Isometric Embedding in Euclidean Latent Space
- Label-Noise Robust Generative Adversarial Networks
- Kernel Exponential Family Estimation via Doubly Dual Embedding
- Artificial Neural Networks for Neuroscientists: A Primer
- Pyro: Deep Universal Probabilistic Programming
- Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition
- Make a Face: Towards Arbitrary High Fidelity Face Manipulation
- Unpaired Image-to-Speech Synthesis with Multimodal Information Bottleneck
- Tackling Over-pruning in Variational Autoencoders
- Learning Disentangled Representations with Reference-Based Variational Autoencoders
- Deep Variational Luenberger-type Observer for Stochastic Video Prediction
- PRRS Outbreak Prediction via Deep Switching Auto-Regressive Factorization Modeling
- A Framework for Interdomain and Multioutput Gaussian Processes
- Neural Autoregressive Distribution Estimation
- On the Effectiveness of Least Squares Generative Adversarial Networks
- A COLD Approach to Generating Optimal Samples
- Learning Robust Feature Representations for Scene Text Detection
- A Graph to Graphs Framework for Retrosynthesis Prediction
- Contrastive Learning with Stronger Augmentations
- Early Visual Concept Learning with Unsupervised Deep Learning
- Unsupervised Word Segmentation from Speech with Attention
- Simple, Scalable, and Stable Variational Deep Clustering
- Disentangling Identifiable Features from Noisy Data with Structured\n Nonlinear ICA
- A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment
- Learning Implicit Generative Models by Teaching Explicit Ones
- Deep Survival Analysis
- Diverse Image Inpainting with Bidirectional and Autoregressive Transformers
- Spherical Sliced-Wasserstein
- Progressive and Aligned Pose Attention Transfer for Person Image Generation
- The Cramer Distance as a Solution to Biased Wasserstein Gradients
- Meta-Learning with Variational Bayes
- Learning Augmentation Distributions using Transformed Risk Minimization
- Can deep learning beat numerical weather prediction?
- Sparse Uncertainty Representation in Deep Learning with Inducing Weights
- iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling
- Learning to Synthesize Programs as Interpretable and Generalizable\n Policies
- Improving Consistency and Correctness of Sequence Inpainting using\n Semantically Guided Generative Adversarial Network
- On the Evaluation of Conditional GANs
- Towards Visually Explaining Variational Autoencoders
- Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder
- Sequential Matrix Completion
- A probabilistic approach to tomography and adjoint state methods, with an application to full waveform inversion in medical ultrasound
- RPC: A Large-Scale Retail Product Checkout Dataset
- Counterfactual Reasoning for Fair Clinical Risk Prediction
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
- Regularization with Latent Space Virtual Adversarial Training
- Sparsity in Variational Autoencoders
- Goal-conditioned dual-action imitation learning for dexterous dual-arm robot manipulation
- Bilevel Continual Learning
- Variational Diffusion Models
- Variational Autoencoder with Learned Latent Structure
- Zero-Shot Semantic Segmentation
- Data Uncertainty Learning in Face Recognition
- Traversing Time with Multi-Resolution Gaussian Process State-Space Models
- Deep Convolutional Inverse Graphics Network
- Generative Models as Distributions of Functions
- Least Squares Generative Adversarial Networks
- Discovering Discrete Latent Topics with Neural Variational Inference
- Texture and geometric feature-fusion-based network for Dunhuang mural inpainting
- Compressing Neural Networks using the Variational Information Bottleneck
- Explaining Visual Models by Causal Attribution
- Deep Proxy Causal Learning and its Application to Confounded Bandit\n Policy Evaluation
- Reconstruction Student with Attention for Student-Teacher Pyramid Matching
- Lower Bounds for Compressed Sensing with Generative Models
- Understanding Instance-based Interpretability of Variational Auto-Encoders
- Stochastic Variational Bayesian Inference for a Nonlinear Forward Model
- Are Disentangled Representations Helpful for Abstract Visual Reasoning?
- TRADI: Tracking deep neural network weight distributions for uncertainty\n estimation
- One-shot emotional voice conversion based on feature separation
- Learning Scalable ℓ_∞-constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compression
- Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance
- Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation
- Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
- Neural Recursive Belief States in Multi-Agent Reinforcement Learning
- Learning Latent Space Energy-Based Prior Model
- A review of radar-based nowcasting of precipitation and applicable machine learning techniques
- NeuralQAAD: An Efficient Differentiable Framework for High Resolution Point Cloud Compression
- SDM-NET: Deep Generative Network for Structured Deformable Mesh
- Estimating Disentangled Belief about Hidden State and Hidden Task for Meta-RL
- PuVAE: A Variational Autoencoder to Purify Adversarial Examples
- Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cables
- Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables
- Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI
- Augmented KRnet for density estimation and approximation
- Causal Discovery in Physical Systems from Videos
- Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors
- DroneSR: Rethinking Few-shot Thermal Image Super-Resolution from Drone-based Perspective
- Neural Topic Model via Optimal Transport
- Variational Mixture of Normalizing Flows
- Jukebox: A Generative Model for Music
- Diverse Plausible Shape Completions from Ambiguous Depth Images
- Stochastic Function Norm Regularization of Deep Networks
- Cause-Effect Deep Information Bottleneck For Systematically Missing Covariates
- DrumGAN: Synthesis of Drum Sounds With Timbral Feature Conditioning Using Generative Adversarial Networks
- Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks
- Online reinforcement learning with sparse rewards through an active inference capsule
- Encoded Prior Sliced Wasserstein AutoEncoder for learning latent\n manifold representations
- Generative Probabilistic Novelty Detection with Adversarial Autoencoders
- We are More than Our Joints: Predicting how 3D Bodies Move
- Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoders
- Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement
- Deep Learning in Mobile and Wireless Networking: A Survey
- Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back
- When Can Neural Networks Learn Connected Decision Regions?
- Independent Prototype Propagation for Zero-Shot Compositionality
- A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
- Graph Generation with Variational Recurrent Neural Network
- Learning an optimal PSF-pair for ultra-dense 3D localization microscopy
- Amortized Inference of Variational Bounds for Learning Noisy-OR
- Controllable Invariance through Adversarial Feature Learning
- Improving Generative Imagination in Object-Centric World Models
- Bigeminal Priors Variational auto-encoder
- Turbulence forecasting via Neural ODE
- Lossless Image Compression through Super-Resolution
- Reliable training and estimation of variance networks
- Deep Involutive Generative Models for Neural MCMC
- Density Deconvolution with Normalizing Flows
- Unsupervised Object Keypoint Learning using Local Spatial Predictability
- Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation
- The Pitfall of More Powerful Autoencoders in Lidar-Based Navigation
- A Chain Graph Interpretation of Real-World Neural Networks
- Learning Robust Representation for Clustering through Locality Preserving Variational Discriminative Network
- On Disentangled Representations Learned From Correlated Data
- Hierarchical Implicit Models and Likelihood-Free Variational Inference
- CoPE: Conditional image generation using Polynomial Expansions
- Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks
- Intriguing Properties of Contrastive Losses
- The continuous categorical: a novel simplex-valued exponential family
- Diffusion Normalizing Flow
- Self-Supervised Learning with Data Augmentations Provably Isolates\n Content from Style
- Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression
- Private-Shared Disentangled Multimodal VAE for Learning of Hybrid Latent\n Representations
- Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic\n Circuits
- Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- Provable Mixed-Noise Learning with Flow-Matching
- Behavior From the Void: Unsupervised Active Pre-Training
- Continual State Representation Learning for Reinforcement Learning using Generative Replay
- Augmenting Monte Carlo Dropout Classification Models with Unsupervised Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults
- Efficient Approximate Inference with Walsh-Hadamard Variational\n Inference
- Symbolic Music Loop Generation with VQ-VAE
- BachGAN: High-Resolution Image Synthesis from Salient Object Layout
- Semantic-Aware Generation for Self-Supervised Visual Representation Learning
- Contrastively Disentangled Sequential Variational Autoencoder
- RoMA: Robust Model Adaptation for Offline Model-based Optimization
- A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction
- NP-ODE: Neural Process Aided Ordinary Differential Equations for Uncertainty Quantification of Finite Element Analysis
- Group Anomaly Detection using Deep Generative Models
- Fairness in rankings and recommendations: an overview
- Measuring the Biases and Effectiveness of Content-Style Disentanglement
- Noise Attention based Spectrum Anomaly Detection Method for Unauthorized Bands
- Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed
- Voice Conversion from Non-parallel Corpora Using Variational Auto-encoder
- Variational Bayesian Context-aware Representation for Grocery Recommendation
- Importance Weighted Hierarchical Variational Inference
- Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes
- A Short Survey On Memory Based Reinforcement Learning
- Replicating Active Appearance Model by Generator Network
- Generative Adversarial Networks
- Asymptotic Consistency of α-Rényi-Approximate Posteriors
- LyricJam: A system for generating lyrics for live instrumental music
- SDEC: Semantic Deep Embedded Clustering
- Conditional Sum-Product Networks: Imposing Structure on Deep\n Probabilistic Architectures
- Generative Moment Matching Networks
- Balancing Reconstruction Quality and Regularisation in ELBO for VAEs
- Maximum-Entropy Adversarial Data Augmentation for Improved\n Generalization and Robustness
- Discrete Word Embedding for Logical Natural Language Understanding
- A Tutorial on Deep Latent Variable Models of Natural Language
- Design by adaptive sampling
- Scaling All-Goals Updates in Reinforcement Learning Using Convolutional\n Neural Networks
- Iterative VAE as a predictive brain model for out-of-distribution generalization
- Normalizing Flows on Riemannian Manifolds
- Multi-Agent Tensor Fusion for Contextual Trajectory Prediction
- Optimal Transport Based Generative Autoencoders
- Probabilistic Re-aggregation Algorithm [First Draft]
- Deep Automodulators
- In Search of Lost Domain Generalization
- Invariant Representations from Adversarially Censored Autoencoders
- Approximate Inference with Amortised MCMC
- Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis
- Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach
- Compositional Modeling of Nonlinear Dynamical Systems with ODE-based\n Random Features
- DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection
- Structured Pruning of a BERT-based Question Answering Model
- Improving Inversion and Generation Diversity in StyleGAN using a Gaussianized Latent Space
- Provable Smoothness Guarantees for Black-Box Variational Inference
- Soft Actor-Critic for Discrete Action Settings
- Graph Embedding VAE: A Permutation Invariant Model of Graph Structure
- Formalising Concepts as Grounded Abstractions
- Decoupling feature propagation from the design of graph auto-encoders
- Joint Variational Autoencoders for Recommendation with Implicit Feedback
- Gaussian Process Bandit Optimization of the Thermodynamic Variational\n Objective
- Robust Pollen Imagery Classification with Generative Modeling and Mixup Training
- Wasserstein Adversarially Regularized Graph Autoencoder
- Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests
- PatchGame: Learning to Signal Mid-level Patches in Referential Games
- AriEL: volume coding for sentence generation
- Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs
- CDVAE: Co-embedding Deep Variational Auto Encoder for Conditional Variational Generation
- Multi-Task Time Series Forecasting With Shared Attention
- How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers
- Enhanced Variational Inference with Dyadic Transformation
- Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
- PAC-GAN: An Effective Pose Augmentation Scheme for Unsupervised Cross-View Person Re-identification
- Biphasic Learning of GANs for High-Resolution Image-to-Image Translation
- Phase Transitions for the Information Bottleneck in Representation Learning
- Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks
- Unsupervised Neural Hidden Markov Models with a Continuous latent state\n space
- Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning
- Distribution Matching in Variational Inference
- Patterns for Learning with Side Information
- Memory and attention in deep learning
- Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN)
- Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks
- Understanding the Limitations of Variational Mutual Information Estimators
- Variational Rejection Sampling
- Graphite: Iterative Generative Modeling of Graphs
- Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer
- Don't Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights
- Physical Layer Authentication Based on Hierarchical Variational Auto-Encoder for Industrial Internet of Things
- KernelNet: A Data-Dependent Kernel Parameterization for Deep Generative Modeling
- Channel Decomposition into Painting Actions
- Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
- Deep Graphics Encoder for Real-Time Video Makeup Synthesis from Example
- Representation Learning for Words and Entities
- Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-Identification
- The Bayesian Committee Approach for Computational Physics Problems
- X-MoGen: Unified Motion Generation across Humans and Animals
- Joint Estimation of Image Representations and their Lie Invariants
- AU-IQA: A Benchmark Dataset for Perceptual Quality Assessment of AI-Enhanced User-Generated Content
- Mixture Representation Learning with Coupled Autoencoders
- von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification
- Auto-Encoding Sequential Monte Carlo
- Modeling documents with Generative Adversarial Networks
- READ: Real-time and Efficient Asynchronous Diffusion for Audio-driven Talking Head Generation
- MU-GAN: Facial Attribute Editing based on Multi-attention Mechanism
- MILD: Multi-Layer Diffusion Strategy for Complex and Precise Multi-IP Aware Human Erasing
- Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
- Guided Variational Autoencoder for Disentanglement Learning
- StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval
- Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space
- Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration
- A Meta-Learning Framework for Generalized Zero-Shot Learning
- Learning to Disentangle Robust and Vulnerable Features for Adversarial Detection
- Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data
- Efficient State-space Exploration in Massively Parallel Simulation Based Inference
- M2VAE: Multi-Modal Multi-View Variational Autoencoder for Cold-start Item Recommendation
- Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning
- Multi-resolution Multi-task Gaussian Processes
- Generative Mixture of Networks
- Probabilistic Video Generation using Holistic Attribute Control
- Generating Smooth Pose Sequences for Diverse Human Motion Prediction
- Source Separation with Deep Generative Priors
- Flow Contrastive Estimation of Energy-Based Models
- CompILE: Compositional Imitation Learning and Execution
- Glow-WaveGAN: Learning Speech Representations from GAN-based Variational Auto-Encoder For High Fidelity Flow-based Speech Synthesis
- WeLa-VAE: Learning Alternative Disentangled Representations Using Weak Labels
- Towards neural networks that provably know when they don't know
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