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Workshop
Tue Dec 14 06:00 AM -- 03:00 PM (PST)
Deep Generative Models and Downstream Applications
José Miguel Hernández-Lobato · Yingzhen Li · Yichuan Zhang · Cheng Zhang · Austin Tripp · Weiwei Pan · Oren Rippel





Workshop Home Page

Deep generative models (DGMs) have become an important research branch in deep learning, including a broad family of methods such as variational autoencoders, generative adversarial networks, normalizing flows, energy based models and autoregressive models. Many of these methods have been shown to achieve state-of-the-art results in the generation of synthetic data of different types such as text, speech, images, music, molecules, etc. However, besides just generating synthetic data, DGMs are of particular relevance in many practical downstream applications. A few examples are imputation and acquisition of missing data, anomaly detection, data denoising, compressed sensing, data compression, image super-resolution, molecule optimization, interpretation of machine learning methods, identifying causal structures in data, generation of molecular structures, etc. However, at present, there seems to be a disconnection between researchers working on new DGM-based methods and researchers applying such methods to practical problems (like the ones mentioned above). This workshop aims to fill in this gap by bringing the two aforementioned communities together.

Opening remarks (Presentation)
Invited talk #1: Aapo Hyvärinen (Presentation)
Q&A Invited Talk #1 (Q&A)
Invited talk #2: Finale Doshi-Velez (Presentation)
Q&A Invited Talk #2 (Q&A)
Invited Talk #3: Rianne van den Berg (Presentation)
Q&A Invited Talk #3 (Q&A)
Particle Dynamics for Learning EBMs (Oral)
VAEs meet Diffusion Models: Efficient and High-Fidelity Generation (Oral)
Contributed poster talk #1-2 Q&A (Q&A)
Break #1 (Break)
Invited talk #4: Chris Williams (Presentation)
Q&A Invited Talk #4 (Q&A)
Invited talk #5: Mihaela van der Schaar (Presentation)
Q&A Invited Talk #5 (Q&A)
Invited Talk #6: Luisa Zintgraf (Presentation)
Q&A Invited Talk #6 (Q&A)
Your Dataset is a Multiset and You Should Compress it Like One (Oral)
Contributed poster talk #3 Q&A + Best paper awards (Q&A)
Break #2 (Break)
Poster session #1 (poster session (gathertown))
Panel Discussion (Discussion Panel)
Invited Talk #7: Romain Lopez (Presentation)
Q&A Invited Talk #7 (Q&A)
Break #3 (Break)
Invited talk #8: Alex Anderson (Presentation)
Q&A Invited Talk #8 (Q&A)
AGE: Enhancing the Convergence on GANs using Alternating extra-gradient with Gradient Extrapolation (Oral)
Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model (Oral)
Contributed poster talk #5-6 Q&A (Q&A)
Invited talk #9: Zhifeng Kong (Presentation)
Q&A Invited Talk #9 (Q&A)
Invited talk #10: Johannes Ballé (Presentation)
Q&A Invited Talk #10 (Q&A)
Bayesian Image Reconstruction using Deep Generative Models (Oral)
Grapher: Multi-Stage Knowledge Graph Construction using Pretrained Language Models (Oral)
Contributed poster talk #7-8 Q&A (Q&A)
Poster session #2 (poster session (gathertown))
Content-Based Image Retrieval from Weakly-Supervised Disentangled Representations (Oral)
Classifier-Free Diffusion Guidance (Oral)
Bayesian Image Reconstruction using Deep Generative Models (Poster)
Searching for the Weirdest Stars: A Convolutional Autoencoder-Based Pipeline For Detecting Anomalous Periodic Variable Stars (Oral)
Your Dataset is a Multiset and You Should Compress it Like One (Poster)
Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures (Oral)
Uncertainty-aware Labelled Augmentations for High Dimensional Latent Space Bayesian Optimization (Oral)
A Binded VAE for Inorganic Material Generation (Oral)
Palette: Image-to-Image Diffusion Models (Oral)
Stochastic Video Prediction with Perceptual Loss (Oral)
Particle Dynamics for Learning EBMs (Poster)
Instance Semantic Segmentation Benefits from Generative Adversarial Networks (Oral)
Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model (Poster)
Conditional Generation of Periodic Signals with Fourier-Based Decoder (Oral)
Finding Maximally Informative Patches in Images (Oral)
Preventing posterior collapse in variational autoencoders for text generation via decoder regularization (Oral)
Deep Generative model with Hierarchical Latent Factors for Timeseries Anomaly Detection (Oral)
XCI-Sketch: Extraction of Color Information from Images for Generation of Colored Outlines and Sketches (Oral)
Variational Autoencoder with Differentiable Physics Engine for Human Gait Analysis and Synthesis (Oral)
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation (Oral)
A Generalized and Distributable Generative Model for Private Representation Learning (Oral)
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification (Oral)
Latent Space Refinement for Deep Generative Models (Oral)
Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration (Oral)
Learning Disentangled Representation for Spatiotemporal Graph Generation (Oral)
Score-Based Generative Classifiers (Oral)
Transparent Liquid Segmentation for Robotic Pouring (Oral)
Grapher: Multi-Stage Knowledge Graph Construction using Pretrained Language Models (Poster)
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination (Oral)
Self-Supervised Anomaly Detection via Neural Autoregressive Flows with Active Learning (Oral)
Semi-supervised Multiple Instance Learning using Variational Auto-Encoders (Oral)
Entropic Issues in Likelihood-Based OOD Detection (Oral)
Single Image Super-Resolution with Uncertainty Estimation for Lunar Satellite Images (Oral)
Few-Shot Out-of-Domain Transfer of Natural Language Explanations (Oral)
Towards Lightweight Controllable Audio Synthesis with Conditional Implicit Neural Representations (Poster)
Controllable Network Data Balancing With GANs (Oral)
An Interpretability-augmented Genetic Expert for Deep Molecular Optimization (Oral)
Deep Variational Semi-Supervised Novelty Detection (Oral)
Uncertainty-aware Labelled Augmentations for High Dimensional Latent Space Bayesian Optimization (Poster)
Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration (Poster)
AGE: Enhancing the Convergence on GANs using Alternating extra-gradient with Gradient Extrapolation (Poster)
Certifiably Robust Variational Autoencoders (Oral)
Towards Lightweight Controllable Audio Synthesis with Conditional Implicit Neural Representations (Oral)
VAEs meet Diffusion Models: Efficient and High-Fidelity Generation (Poster)
Few-Shot Out-of-Domain Transfer of Natural Language Explanations (Poster)
An Interpretability-augmented Genetic Expert for Deep Molecular Optimization (Poster)
Transparent Liquid Segmentation for Robotic Pouring (Poster)
XCI-Sketch: Extraction of Color Information from Images for Generation of Colored Outlines and Sketches (Poster)
Conditional Generation of Periodic Signals with Fourier-Based Decoder (Poster)
Palette: Image-to-Image Diffusion Models (Poster)
Semi-supervised Multiple Instance Learning using Variational Auto-Encoders (Poster)
Deep Generative model with Hierarchical Latent Factors for Timeseries Anomaly Detection (Poster)
Variational Autoencoder with Differentiable Physics Engine for Human Gait Analysis and Synthesis (Poster)
A Binded VAE for Inorganic Material Generation (Poster)
Instance Semantic Segmentation Benefits from Generative Adversarial Networks (Poster)
Classifier-Free Diffusion Guidance (Poster)
Finding Maximally Informative Patches in Images (Poster)
Accurate Imputation and Efficient Data Acquisitionwith Transformer-based VAEs (Poster)
Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures (Poster)
Self-Supervised Anomaly Detection via Neural Autoregressive Flows with Active Learning (Poster)
Deep Variational Semi-Supervised Novelty Detection (Poster)
Controllable Network Data Balancing With GANs (Poster)
A Generalized and Distributable Generative Model for Private Representation Learning (Poster)
Score-Based Generative Classifiers (Poster)
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination (Poster)
Preventing posterior collapse in variational autoencoders for text generation via decoder regularization (Poster)
Latent Space Refinement for Deep Generative Models (Poster)
Stochastic Video Prediction with Perceptual Loss (Poster)
Learning Disentangled Representation for Spatiotemporal Graph Generation (Poster)
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification (Poster)
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation (Poster)
How to Reward Your Drug Agent? (Poster)
Searching for the Weirdest Stars: A Convolutional Autoencoder-Based Pipeline For Detecting Anomalous Periodic Variable Stars (Poster)
Certifiably Robust Variational Autoencoders (Poster)
Entropic Issues in Likelihood-Based OOD Detection (Poster)
Single Image Super-Resolution with Uncertainty Estimation for Lunar Satellite Images (Poster)
Content-Based Image Retrieval from Weakly-Supervised Disentangled Representations (Poster)
Accurate Imputation and Efficient Data Acquisitionwith Transformer-based VAEs (Oral)
How to Reward Your Drug Agent? (Oral)