Workshop
Deep Generative Models and Downstream Applications
Jos茅 Miguel Hern谩ndez-Lobato 路 Yingzhen Li 路 Yichuan Zhang 路 Cheng Zhang 路 Austin Tripp 路 Weiwei Pan 路 Oren Rippel
Tue 14 Dec, 6 a.m. PST
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.
Schedule
Tue 6:00 a.m. - 6:10 a.m.
|
Opening remarks
(
Presentation
)
>
SlidesLive Video |
馃敆 |
Tue 6:10 a.m. - 6:25 a.m.
|
Invited talk #1: Aapo Hyv盲rinen
(
Presentation
)
>
SlidesLive Video |
Aapo Hyvarinen 馃敆 |
Tue 6:25 a.m. - 6:30 a.m.
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Q&A Invited Talk #1
(
Q&A
)
>
|
馃敆 |
Tue 6:30 a.m. - 6:45 a.m.
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Invited talk #2: Finale Doshi-Velez
(
Presentation
)
>
SlidesLive Video |
Finale Doshi-Velez 馃敆 |
Tue 6:45 a.m. - 6:50 a.m.
|
Q&A Invited Talk #2
(
Q&A
)
>
|
馃敆 |
Tue 6:50 a.m. - 7:05 a.m.
|
Invited Talk #3: Rianne van den Berg
(
Presentation
)
>
SlidesLive Video |
Rianne van den Berg 馃敆 |
Tue 7:05 a.m. - 7:10 a.m.
|
Q&A Invited Talk #3
(
Q&A
)
>
|
馃敆 |
Tue 7:10 a.m. - 7:20 a.m.
|
Particle Dynamics for Learning EBMs
(
Oral
)
>
link
SlidesLive Video |
Kirill Neklyudov 路 Priyank Jaini 路 Max Welling 馃敆 |
Tue 7:20 a.m. - 7:30 a.m.
|
VAEs meet Diffusion Models: Efficient and High-Fidelity Generation
(
Oral
)
>
link
SlidesLive Video |
Kushagra Pandey 路 Avideep Mukherjee 路 Piyush Rai 路 Abhishek Kumar 馃敆 |
Tue 7:30 a.m. - 7:35 a.m.
|
Contributed poster talk #1-2 Q&A
(
Q&A
)
>
|
馃敆 |
Tue 7:35 a.m. - 8:00 a.m.
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Break #1
|
馃敆 |
Tue 8:00 a.m. - 8:15 a.m.
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Invited talk #4: Chris Williams
(
Presentation
)
>
SlidesLive Video |
Chris Williams 馃敆 |
Tue 8:15 a.m. - 8:20 a.m.
|
Q&A Invited Talk #4
(
Q&A
)
>
|
馃敆 |
Tue 8:20 a.m. - 8:35 a.m.
|
Invited talk #5: Mihaela van der Schaar
(
Presentation
)
>
SlidesLive Video |
Mihaela van der Schaar 馃敆 |
Tue 8:35 a.m. - 8:40 a.m.
|
Q&A Invited Talk #5
(
Q&A
)
>
|
馃敆 |
Tue 8:40 a.m. - 8:55 a.m.
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Invited Talk #6: Luisa Zintgraf
(
Presentation
)
>
SlidesLive Video |
Luisa Zintgraf 馃敆 |
Tue 8:55 a.m. - 9:00 a.m.
|
Q&A Invited Talk #6
(
Q&A
)
>
|
馃敆 |
Tue 9:00 a.m. - 9:10 a.m.
|
Your Dataset is a Multiset and You Should Compress it Like One
(
Oral
)
>
link
SlidesLive Video |
Daniel Severo 路 James Townsend 路 Ashish Khisti 路 Alireza Makhzani 路 Karen Ullrich 馃敆 |
Tue 9:10 a.m. - 9:20 a.m.
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Contributed poster talk #3 Q&A + Best paper awards
(
Q&A
)
>
SlidesLive Video |
馃敆 |
Tue 9:20 a.m. - 10:00 a.m.
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Break #2
|
馃敆 |
Tue 10:00 a.m. - 11:00 a.m.
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Poster session #1
(
poster session (gathertown)
)
>
|
馃敆 |
Tue 11:00 a.m. - 11:30 a.m.
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Panel Discussion
(
Discussion Panel
)
>
SlidesLive Video |
馃敆 |
Tue 11:30 a.m. - 11:45 a.m.
|
Invited Talk #7: Romain Lopez
(
Presentation
)
>
SlidesLive Video |
Romain Lopez 馃敆 |
Tue 11:45 a.m. - 11:50 a.m.
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Q&A Invited Talk #7
(
Q&A
)
>
|
馃敆 |
Tue 11:50 a.m. - 12:10 p.m.
|
Break #3
|
馃敆 |
Tue 12:10 p.m. - 12:25 p.m.
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Invited talk #8: Alex Anderson
(
Presentation
)
>
SlidesLive Video |
Alex Anderson 馃敆 |
Tue 12:25 p.m. - 12:30 p.m.
|
Q&A Invited Talk #8
(
Q&A
)
>
|
馃敆 |
Tue 12:30 p.m. - 12:40 p.m.
|
AGE: Enhancing the Convergence on GANs using Alternating extra-gradient with Gradient Extrapolation
(
Oral
)
>
link
SlidesLive Video |
Huan He 路 Shifan Zhao 路 Yuanzhe Xi 路 Joyce Ho 馃敆 |
Tue 12:40 p.m. - 12:50 p.m.
|
Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model
(
Oral
)
>
link
SlidesLive Video |
Samuel Hoffman 路 Vijil Chenthamarakshan 路 Dmitry Zubarev 路 Daniel Sanders 路 Payel Das 馃敆 |
Tue 12:50 p.m. - 12:55 p.m.
|
Contributed poster talk #5-6 Q&A
(
Q&A
)
>
|
馃敆 |
Tue 12:55 p.m. - 1:10 p.m.
|
Invited talk #9: Zhifeng Kong
(
Presentation
)
>
SlidesLive Video |
Zhifeng Kong 馃敆 |
Tue 1:10 p.m. - 1:15 p.m.
|
Q&A Invited Talk #9
(
Q&A
)
>
|
馃敆 |
Tue 1:15 p.m. - 1:30 p.m.
|
Invited talk #10: Johannes Ball茅
(
Presentation
)
>
SlidesLive Video |
Johannes Ball茅 馃敆 |
Tue 1:30 p.m. - 1:35 p.m.
|
Q&A Invited Talk #10
(
Q&A
)
>
|
馃敆 |
Tue 1:35 p.m. - 1:45 p.m.
|
Bayesian Image Reconstruction using Deep Generative Models
(
Oral
)
>
link
SlidesLive Video |
Razvan Marinescu 路 Daniel Moyer 路 Polina Golland 馃敆 |
Tue 1:45 p.m. - 1:55 p.m.
|
Grapher: Multi-Stage Knowledge Graph Construction using Pretrained Language Models
(
Oral
)
>
link
SlidesLive Video |
Igor Melnyk 路 Pierre Dognin 路 Payel Das 馃敆 |
Tue 1:55 p.m. - 2:00 p.m.
|
Contributed poster talk #7-8 Q&A
(
Q&A
)
>
|
馃敆 |
Tue 2:00 p.m. - 3:00 p.m.
|
Poster session #2
(
poster session (gathertown)
)
>
|
馃敆 |
-
|
Transparent Liquid Segmentation for Robotic Pouring ( Poster ) > link | Gautham Narayan Narasimhan 路 Kai Zhang 路 Benjamin Eisner 路 Xingyu Lin 路 David Held 馃敆 |
-
|
Uncertainty-aware Labelled Augmentations for High Dimensional Latent Space Bayesian Optimization ( Poster ) > link | Ekansh Verma 路 Souradip Chakraborty 馃敆 |
-
|
How to Reward Your Drug Agent? ( Poster ) > link | Andrea Karlova 路 Wim Dehaen 路 Andrei Penciu 馃敆 |
-
|
Searching for the Weirdest Stars: A Convolutional Autoencoder-Based Pipeline For Detecting Anomalous Periodic Variable Stars ( Poster ) > link | Ho-Sang Chan 路 Siu Hei Cheung 路 Victoria Villar 路 Shirley Ho 馃敆 |
-
|
XCI-Sketch: Extraction of Color Information from Images for Generation of Colored Outlines and Sketches ( Poster ) > link | V Manushree 路 Sameer Saxena 路 Parna Chowdhury 路 Manisimha Varma Manthena 路 Harsh Rathod 路 Ankita Ghosh 路 Sahil Khose 馃敆 |
-
|
Conditional Generation of Periodic Signals with Fourier-Based Decoder ( Poster ) > link | Jiyoung Lee 路 Wonjae Kim 路 DAEHOON GWAK 路 Edward Choi 馃敆 |
-
|
Palette: Image-to-Image Diffusion Models ( Poster ) > link | Chitwan Saharia 路 William Chan 路 Huiwen Chang 路 Chris Lee 路 Jonathan Ho 路 Tim Salimans 路 David Fleet 路 Mohammad Norouzi 馃敆 |
-
|
Semi-supervised Multiple Instance Learning using Variational Auto-Encoders ( Poster ) > link | Ali Nihat Uzunalioglu 路 Tameem Adel 路 Jakub M. Tomczak 馃敆 |
-
|
Variational Autoencoder with Differentiable Physics Engine for Human Gait Analysis and Synthesis ( Poster ) > link | Naoya Takeishi 路 Alexandros Kalousis 馃敆 |
-
|
A Binded VAE for Inorganic Material Generation ( Poster ) > link | Fouad OUBARI 路 Antoine De mathelin 路 Rodrigue D茅catoire 路 Mathilde MOUGEOT 馃敆 |
-
|
Certifiably Robust Variational Autoencoders ( Poster ) > link | Ben Barrett 路 Alexander Camuto 路 Matthew Willetts 路 Thomas Rainforth 馃敆 |
-
|
Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration ( Poster ) > link | Si-An Chen 路 Chun-Liang Li 路 Hsuan-Tien Lin 馃敆 |
-
|
Instance Semantic Segmentation Benefits from Generative Adversarial Networks ( Poster ) > link | Quang Le 路 KAMAL YOUCEF-TOUMI 路 Dzmitry Tsetserukou 路 Ali Jahanian 馃敆 |
-
|
Classifier-Free Diffusion Guidance ( Poster ) > link | Jonathan Ho 路 Tim Salimans 馃敆 |
-
|
Accurate Imputation and Efficient Data Acquisitionwith Transformer-based VAEs ( Poster ) > link | Sarah Lewis 路 Tatiana Matejovicova 路 Yingzhen Li 路 Angus Lamb 路 Yordan Zaykov 路 Miltiadis Allamanis 路 Cheng Zhang 馃敆 |
-
|
Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures ( Poster ) > link | Kin Olivares 路 Oinam Nganba Meetei 路 Ruijun Ma 路 Rohan Reddy 路 Mengfei Cao 馃敆 |
-
|
Deep Generative model with Hierarchical Latent Factors for Timeseries Anomaly Detection ( Poster ) > link | Cristian Challu 路 Peihong Jiang 路 Ying Nian Wu 路 Laurent Callot 馃敆 |
-
|
Entropic Issues in Likelihood-Based OOD Detection ( Poster ) > link | Anthony Caterini 路 Gabriel Loaiza-Ganem 馃敆 |
-
|
Single Image Super-Resolution with Uncertainty Estimation for Lunar Satellite Images ( Poster ) > link | Jose Delgado-Centeno 路 Paula Harder 路 Ben Moseley 路 Valentin Bickel 路 Siddha Ganju 路 Miguel Olivares 路 Alfredo Kalaitzis 馃敆 |
-
|
Self-Supervised Anomaly Detection via Neural Autoregressive Flows with Active Learning ( Poster ) > link | Jiaxin Zhang 路 Kyle Saleeby 路 Thomas Feldhausen 路 Sirui Bi 路 Alex Plotkowski 路 David Womble 馃敆 |
-
|
Content-Based Image Retrieval from Weakly-Supervised Disentangled Representations ( Poster ) > link | Luis Armando P茅rez Rey 路 Dmitri Jarnikov 路 Mike Holenderski 馃敆 |
-
|
Deep Variational Semi-Supervised Novelty Detection ( Poster ) > link | Tal Daniel 路 Thanard Kurutach 路 Aviv Tamar 馃敆 |
-
|
Controllable Network Data Balancing With GANs ( Poster ) > link | Fares Meghdouri 路 Thomas Schmied 路 Thomas Gaertner 路 Tanja Zseby 馃敆 |
-
|
A Generalized and Distributable Generative Model for Private Representation Learning ( Poster ) > link | Sheikh Shams Azam 路 Taejin Kim 路 Seyyedali Hosseinalipour 路 Carlee Joe-Wong 路 Saurabh Bagchi 路 Christopher Brinton 馃敆 |
-
|
Score-Based Generative Classifiers ( Poster ) > link | Roland S. Zimmermann 路 Lukas Schott 路 Yang Song 路 Benjamin Dunn 路 David Klindt 馃敆 |
-
|
An Interpretability-augmented Genetic Expert for Deep Molecular Optimization ( Poster ) > link | Pierre W眉thrich 路 Jun Jin Choong 路 Shinya Yuki 馃敆 |
-
|
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination ( Poster ) > link | Jongmin Yu 路 Hyeontaek Oh 路 Minkyung Kim 路 Junsik Kim 馃敆 |
-
|
Preventing posterior collapse in variational autoencoders for text generation via decoder regularization ( Poster ) > link | Alban Petit 路 Caio Corro 馃敆 |
-
|
Latent Space Refinement for Deep Generative Models ( Poster ) > link | Ramon Winterhalder 路 Marco Bellagente 路 Benjamin Nachman 馃敆 |
-
|
Stochastic Video Prediction with Perceptual Loss ( Poster ) > link | Donghun Lee 路 Ingook Jang 路 Seonghyun Kim 路 Chanwon Park 路 JUN HEE PARK 馃敆 |
-
|
Few-Shot Out-of-Domain Transfer of Natural Language Explanations ( Poster ) > link | Yordan Yordanov 路 Vid Kocijan 路 Thomas Lukasiewicz 路 Oana M Camburu 馃敆 |
-
|
Learning Disentangled Representation for Spatiotemporal Graph Generation ( Poster ) > link | Yuanqi Du 路 Xiaojie Guo 路 Hengning Cao 路 Yanfang (Fa Ye 路 Liang Zhao 馃敆 |
-
|
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification ( Poster ) > link | Junwen Bai 路 Shufeng Kong 路 Carla Gomes 馃敆 |
-
|
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation ( Poster ) > link | Tobias Weber 路 Michael Ingrisch 路 Bernd Bischl 路 David R眉gamer 馃敆 |
-
|
Finding Maximally Informative Patches in Images ( Poster ) > link | Howard Zhong 路 Guha Balakrishnan 路 Richard Bowen 路 Ramin Zabih 路 Bill Freeman 馃敆 |
-
|
Accurate Imputation and Efficient Data Acquisitionwith Transformer-based VAEs ( Oral ) > link | Sarah Lewis 路 Tatiana Matejovicova 路 Yingzhen Li 路 Angus Lamb 路 Yordan Zaykov 路 Miltiadis Allamanis 路 Cheng Zhang 馃敆 |
-
|
AGE: Enhancing the Convergence on GANs using Alternating extra-gradient with Gradient Extrapolation ( Poster ) > link | Huan He 路 Shifan Zhao 路 Yuanzhe Xi 路 Joyce Ho 馃敆 |
-
|
How to Reward Your Drug Agent? ( Oral ) > link | Andrea Karlova 路 Wim Dehaen 路 Andrei Penciu 馃敆 |
-
|
VAEs meet Diffusion Models: Efficient and High-Fidelity Generation ( Poster ) > link | Kushagra Pandey 路 Avideep Mukherjee 路 Piyush Rai 路 Abhishek Kumar 馃敆 |
-
|
Content-Based Image Retrieval from Weakly-Supervised Disentangled Representations ( Oral ) > link | Luis Armando P茅rez Rey 路 Dmitri Jarnikov 路 Mike Holenderski 馃敆 |
-
|
Classifier-Free Diffusion Guidance ( Oral ) > link | Jonathan Ho 路 Tim Salimans 馃敆 |
-
|
Bayesian Image Reconstruction using Deep Generative Models ( Poster ) > link | Razvan Marinescu 路 Daniel Moyer 路 Polina Golland 馃敆 |
-
|
Searching for the Weirdest Stars: A Convolutional Autoencoder-Based Pipeline For Detecting Anomalous Periodic Variable Stars ( Oral ) > link | Ho-Sang Chan 路 Siu Hei Cheung 路 Victoria Villar 路 Shirley Ho 馃敆 |
-
|
Your Dataset is a Multiset and You Should Compress it Like One ( Poster ) > link | Daniel Severo 路 James Townsend 路 Ashish Khisti 路 Alireza Makhzani 路 Karen Ullrich 馃敆 |
-
|
Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures ( Oral ) > link | Kin Olivares 路 Oinam Nganba Meetei 路 Ruijun Ma 路 Rohan Reddy 路 Mengfei Cao 馃敆 |
-
|
Uncertainty-aware Labelled Augmentations for High Dimensional Latent Space Bayesian Optimization ( Oral ) > link | Ekansh Verma 路 Souradip Chakraborty 馃敆 |
-
|
A Binded VAE for Inorganic Material Generation ( Oral ) > link | Fouad OUBARI 路 Antoine De mathelin 路 Rodrigue D茅catoire 路 Mathilde MOUGEOT 馃敆 |
-
|
Controllable Network Data Balancing With GANs ( Oral ) > link | Fares Meghdouri 路 Thomas Schmied 路 Thomas Gaertner 路 Tanja Zseby 馃敆 |
-
|
Palette: Image-to-Image Diffusion Models ( Oral ) > link | Chitwan Saharia 路 William Chan 路 Huiwen Chang 路 Chris Lee 路 Jonathan Ho 路 Tim Salimans 路 David Fleet 路 Mohammad Norouzi 馃敆 |
-
|
Stochastic Video Prediction with Perceptual Loss ( Oral ) > link | Donghun Lee 路 Ingook Jang 路 Seonghyun Kim 路 Chanwon Park 路 JUN HEE PARK 馃敆 |
-
|
Particle Dynamics for Learning EBMs ( Poster ) > link | Kirill Neklyudov 路 Priyank Jaini 路 Max Welling 馃敆 |
-
|
Instance Semantic Segmentation Benefits from Generative Adversarial Networks ( Oral ) > link | Quang Le 路 KAMAL YOUCEF-TOUMI 路 Dzmitry Tsetserukou 路 Ali Jahanian 馃敆 |
-
|
Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model ( Poster ) > link | Samuel Hoffman 路 Vijil Chenthamarakshan 路 Dmitry Zubarev 路 Daniel Sanders 路 Payel Das 馃敆 |
-
|
Towards Lightweight Controllable Audio Synthesis with Conditional Implicit Neural Representations ( Oral ) > link | Jan Zuiderveld 路 Marco Federici 路 Erik Bekkers 馃敆 |
-
|
Conditional Generation of Periodic Signals with Fourier-Based Decoder ( Oral ) > link | Jiyoung Lee 路 Wonjae Kim 路 DAEHOON GWAK 路 Edward Choi 馃敆 |
-
|
Finding Maximally Informative Patches in Images ( Oral ) > link | Howard Zhong 路 Guha Balakrishnan 路 Richard Bowen 路 Ramin Zabih 路 Bill Freeman 馃敆 |
-
|
Preventing posterior collapse in variational autoencoders for text generation via decoder regularization ( Oral ) > link | Alban Petit 路 Caio Corro 馃敆 |
-
|
An Interpretability-augmented Genetic Expert for Deep Molecular Optimization ( Oral ) > link | Pierre W眉thrich 路 Jun Jin Choong 路 Shinya Yuki 馃敆 |
-
|
Deep Generative model with Hierarchical Latent Factors for Timeseries Anomaly Detection ( Oral ) > link | Cristian Challu 路 Peihong Jiang 路 Ying Nian Wu 路 Laurent Callot 馃敆 |
-
|
XCI-Sketch: Extraction of Color Information from Images for Generation of Colored Outlines and Sketches ( Oral ) > link | V Manushree 路 Sameer Saxena 路 Parna Chowdhury 路 Manisimha Varma Manthena 路 Harsh Rathod 路 Ankita Ghosh 路 Sahil Khose 馃敆 |
-
|
Variational Autoencoder with Differentiable Physics Engine for Human Gait Analysis and Synthesis ( Oral ) > link | Naoya Takeishi 路 Alexandros Kalousis 馃敆 |
-
|
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation ( Oral ) > link | Tobias Weber 路 Michael Ingrisch 路 Bernd Bischl 路 David R眉gamer 馃敆 |
-
|
A Generalized and Distributable Generative Model for Private Representation Learning ( Oral ) > link | Sheikh Shams Azam 路 Taejin Kim 路 Seyyedali Hosseinalipour 路 Carlee Joe-Wong 路 Saurabh Bagchi 路 Christopher Brinton 馃敆 |
-
|
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification ( Oral ) > link | Junwen Bai 路 Shufeng Kong 路 Carla Gomes 馃敆 |
-
|
Deep Variational Semi-Supervised Novelty Detection ( Oral ) > link | Tal Daniel 路 Thanard Kurutach 路 Aviv Tamar 馃敆 |
-
|
Latent Space Refinement for Deep Generative Models ( Oral ) > link | Ramon Winterhalder 路 Marco Bellagente 路 Benjamin Nachman 馃敆 |
-
|
Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration ( Oral ) > link | Si-An Chen 路 Chun-Liang Li 路 Hsuan-Tien Lin 馃敆 |
-
|
Learning Disentangled Representation for Spatiotemporal Graph Generation ( Oral ) > link | Yuanqi Du 路 Xiaojie Guo 路 Hengning Cao 路 Yanfang (Fa Ye 路 Liang Zhao 馃敆 |
-
|
Score-Based Generative Classifiers ( Oral ) > link | Roland S. Zimmermann 路 Lukas Schott 路 Yang Song 路 Benjamin Dunn 路 David Klindt 馃敆 |
-
|
Transparent Liquid Segmentation for Robotic Pouring ( Oral ) > link | Gautham Narayan Narasimhan 路 Kai Zhang 路 Benjamin Eisner 路 Xingyu Lin 路 David Held 馃敆 |
-
|
Grapher: Multi-Stage Knowledge Graph Construction using Pretrained Language Models ( Poster ) > link | Igor Melnyk 路 Pierre Dognin 路 Payel Das 馃敆 |
-
|
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination ( Oral ) > link | Jongmin Yu 路 Hyeontaek Oh 路 Minkyung Kim 路 Junsik Kim 馃敆 |
-
|
Self-Supervised Anomaly Detection via Neural Autoregressive Flows with Active Learning ( Oral ) > link | Jiaxin Zhang 路 Kyle Saleeby 路 Thomas Feldhausen 路 Sirui Bi 路 Alex Plotkowski 路 David Womble 馃敆 |
-
|
Semi-supervised Multiple Instance Learning using Variational Auto-Encoders ( Oral ) > link | Ali Nihat Uzunalioglu 路 Tameem Adel 路 Jakub M. Tomczak 馃敆 |
-
|
Certifiably Robust Variational Autoencoders ( Oral ) > link | Ben Barrett 路 Alexander Camuto 路 Matthew Willetts 路 Thomas Rainforth 馃敆 |
-
|
Entropic Issues in Likelihood-Based OOD Detection ( Oral ) > link | Anthony Caterini 路 Gabriel Loaiza-Ganem 馃敆 |
-
|
Single Image Super-Resolution with Uncertainty Estimation for Lunar Satellite Images ( Oral ) > link | Jose Delgado-Centeno 路 Paula Harder 路 Ben Moseley 路 Valentin Bickel 路 Siddha Ganju 路 Miguel Olivares 路 Alfredo Kalaitzis 馃敆 |
-
|
Few-Shot Out-of-Domain Transfer of Natural Language Explanations ( Oral ) > link | Yordan Yordanov 路 Vid Kocijan 路 Thomas Lukasiewicz 路 Oana M Camburu 馃敆 |
-
|
Towards Lightweight Controllable Audio Synthesis with Conditional Implicit Neural Representations
(
Poster
)
>
|
馃敆 |