Tue 3:00 a.m. - 3:10 a.m.
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Opening Remarks
(
Opening remarks (zoom)
)
>
link
SlidesLive Video
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🔗
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Tue 3:10 a.m. - 3:30 a.m.
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Adaptive and Robust Learning with Bayes
(
Invited talk
)
>
link
SlidesLive Video
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🔗
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Tue 3:30 a.m. - 3:50 a.m.
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A Bayesian Perspective on Meta-Learning
(
Invited talk
)
>
link
SlidesLive Video
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🔗
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Tue 3:50 a.m. - 4:10 a.m.
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Shifts Challenge: Robustness and Uncertainty under Real-World Distributional Shift
(
Competition talk
)
>
link
SlidesLive Video
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🔗
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Tue 4:10 a.m. - 4:30 a.m.
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Gaussian Dropout as an Information Bottleneck Layer
(
Contributed talk
)
>
link
SlidesLive Video
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🔗
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Tue 4:20 a.m. - 4:30 a.m.
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Funnels: Exact Maximum Likelihood with Dimensionality Reduction
(
Contributed talk
)
>
link
SlidesLive Video
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🔗
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Tue 4:30 a.m. - 5:30 a.m.
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Posters (gather town link to the right) and lunch break
(
Poster
)
>
link
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🔗
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Tue 5:30 a.m. - 5:50 a.m.
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Spacecraft Collision Avoidance with Bayesian Deep Learning
(
Invited talk
)
>
link
SlidesLive Video
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🔗
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Tue 5:50 a.m. - 6:10 a.m.
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Inference & Sampling with Symmetries
(
Invited talk
)
>
link
SlidesLive Video
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🔗
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Tue 6:10 a.m. - 6:30 a.m.
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Bayesian Neural Networks, Andversarial Attacks, and How the Amount of Samples Matters
(
Invited talk
)
>
link
SlidesLive Video
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🔗
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Tue 6:30 a.m. - 8:00 a.m.
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Posters (gather town)
(
Poster
)
>
link
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🔗
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Tue 8:00 a.m. - 8:20 a.m.
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Quantified Uncertainty for Safe Operation of Particle Accelerators
(
Invited talk
)
>
link
SlidesLive Video
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🔗
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Tue 8:20 a.m. - 8:30 a.m.
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Diversity is All You Need to Improve Bayesian Model Averaging
(
Contributed talk
)
>
link
SlidesLive Video
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🔗
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Tue 8:30 a.m. - 8:40 a.m.
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Structure Stochastic Gradient MCMC: a hybrid VI and MCMC approach
(
Contributed talk
)
>
link
SlidesLive Video
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🔗
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Tue 8:40 a.m. - 9:00 a.m.
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Evaluating Approximate Inference in Bayesian Deep Learning
(
Competition talk
)
>
link
SlidesLive Video
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🔗
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Tue 9:00 a.m. - 9:20 a.m.
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An Automatic Finite-Data Robustness Metric for Bayes and Beyond: Can Dropping a Little Data Change Conclusions?
(
Invited talk
)
>
link
SlidesLive Video
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🔗
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Tue 9:20 a.m. - 9:25 a.m.
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Closing remarks
(
Closing remarks
)
>
link
SlidesLive Video
|
🔗
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Tue 9:25 a.m. - 11:00 a.m.
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Social and Posters (gather town)
(
Poster
)
>
link
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🔗
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-
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Diversity is All You Need to Improve Bayesian Model Averaging
(
Poster
)
>
link
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Yashvir Singh Grewal · Thang Bui
🔗
|
-
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Regularizations Are All You Need: Weather Prediction Under Distributional Shift
(
Poster
)
>
link
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Sankalp Gilda · Neel Bhandari · Wendy Wing Yee Mak · Andrea Panizza
🔗
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-
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Reducing redundancy in Semantic-KITTI: Study on data augmentations within Active Learning
(
Poster
)
>
link
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Alexandre Almin · Anh Duong · Léo Lemarié · Ravi Kiran
🔗
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-
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An Empirical Analysis of Uncertainty Estimation in Genomics Applications
(
Poster
)
>
link
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Sepideh Saran · Mahsa Ghanbari · Uwe Ohler
🔗
|
-
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Hierarchical Topic Evaluation: Statistical vs. Neural Models
(
Poster
)
>
link
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Mayank Kumar Nagda · Charu Karakkaparambil James · Sophie Burkhardt · Marius Kloft
🔗
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-
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Reflected Hamiltonian Monte Carlo
(
Poster
)
>
link
|
Khai Xiang Au · alexandre thiery
🔗
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-
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Federated Functional Variational Inference
(
Poster
)
>
link
|
Michael Hutchinson · Matthias Reisser · Christos Louizos
🔗
|
-
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Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling
(
Poster
)
>
link
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Natalia Khanzhina · Alexey Lapenok · Andrey Filchenkov
🔗
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-
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Stochastic Pruning: Fine-Tuning, and PAC-Bayes bound optimization
(
Poster
)
>
link
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Soufiane Hayou · Bobby He · Gintare Karolina Dziugaite
🔗
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-
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Adversarial Learning of a Variational Generative Model with Succinct Bottleneck Representation
(
Poster
)
>
link
|
Jongha (Jon) Ryu · Yoojin Choi · Young-Han Kim · Mostafa El-Khamy · Jungwon Lee
🔗
|
-
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Posterior Temperature Optimization in Variational Inference for Inverse Problems
(
Poster
)
>
link
|
Max Laves · Malte Tölle · Alexander Schlaefer · Sandy Engelhardt
🔗
|
-
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Revisiting the Structured Variational Autoencoder
(
Poster
)
>
link
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Yixiu Zhao · Scott Linderman
🔗
|
-
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Robust outlier detection by de-biasing VAE likelihoods
(
Poster
)
>
link
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Kushal Chauhan · Pradeep Shenoy · Manish Gupta · Devarajan Sridharan
🔗
|
-
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The Dynamics of Functional Diversity throughout Neural Network Training
(
Poster
)
>
link
|
Lee Zamparo · Marc-Etienne Brunet · Thomas George · Sepideh Kharaghani · Gintare Karolina Dziugaite
🔗
|
-
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Biases in variational Bayesian neural networks
(
Poster
)
>
link
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Thang Bui
🔗
|
-
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Bayesian Inference in Augmented Bow Tie Networks
(
Poster
)
>
link
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Jimmy Smith · Dieterich Lawson · Scott Linderman
🔗
|
-
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Fast Finite Width Neural Tangent Kernel
(
Poster
)
>
link
|
Roman Novak · Jascha Sohl-Dickstein · Samuel Schoenholz
🔗
|
-
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Reliable Uncertainty Quantification of Deep Learning Models for a Free Electron Laser Scientific Facility
(
Poster
)
>
link
|
Lipi Gupta · Aashwin Mishra · Auralee Edelen
🔗
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-
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Latent Goal Allocation for Multi-Agent Goal-Conditioned Self-Supervised Learning
(
Poster
)
>
link
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Laixi Shi · Peide Huang · Rui Chen
🔗
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-
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Constraining cosmological parameters from N-body simulations with Bayesian Neural Networks
(
Poster
)
>
link
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Hector Javier Hortua
🔗
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-
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Evaluating Deep Learning Uncertainty Quantification Methods for Neutrino Physics Applications
(
Poster
)
>
link
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Dae Heun Koh · Aashwin Mishra · Kazuhiro Terao
🔗
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-
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Model-embedding flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling
(
Poster
)
>
link
|
Gianluigi Silvestri · Emily Fertig · Dave Moore · Luca Ambrogioni
🔗
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-
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Likelihood-free Density Ratio Acquisition Functions are not Equivalent to Expected Improvements
(
Poster
)
>
link
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Jiaming Song · Stefano Ermon
🔗
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-
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Object-Factored Models with Partially Observable State
(
Poster
)
>
link
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Isaiah Brand · Michael Noseworthy · Sebastian Castro · Nick Roy
🔗
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-
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On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications
(
Poster
)
>
link
|
Johanna Rock · Tiago Azevedo · René de Jong · Daniel Ruiz · Partha Maji
🔗
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-
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Dropout and Ensemble Networks for Thermospheric Density Uncertainty Estimation
(
Poster
)
>
link
|
Stefano Bonasera · Giacomo Acciarini · Jorge Pérez-Hernández · Bernard Benson · Edward Brown · Eric Sutton · Moriba Jah · Christopher Bridges · Atilim Gunes Baydin
🔗
|
-
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Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning
(
Poster
)
>
link
|
Aaqib Parvez Mohammed · Matias Valdenegro-Toro
🔗
|
-
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Can Network Flatness Explain the Training Speed-Generalisation Connection?
(
Poster
)
>
link
|
Albert Qiaochu Jiang · Clare Lyle · Lisa Schut · Yarin Gal
🔗
|
-
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Mixture-of-experts VAEs can disregard unimodal variation in surjective multimodal data
(
Poster
)
>
link
|
Jannik Wolff · Tassilo Klein · Moin Nabi · Rahul G Krishnan · Shinichi Nakajima
🔗
|
-
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Depth Uncertainty Networks for Active Learning
(
Poster
)
>
link
|
Chelsea Murray · James Allingham · Javier Antorán · José Miguel Hernández-Lobato
🔗
|
-
|
The Peril of Popular Deep Learning Uncertainty Estimation Methods
(
Poster
)
>
link
|
Yehao Liu · Matteo Pagliardini · Tatjana Chavdarova · Sebastian Stich
🔗
|
-
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Dependence between Bayesian neural network units
(
Poster
)
>
link
|
Mariia Vladimirova · Julyan Arbel · Stephane Girard
🔗
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-
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Relaxed-Responsibility Hierarchical Discrete VAEs
(
Poster
)
>
link
|
Matthew Willetts · Xenia Miscouridou · Stephen J Roberts · Chris C Holmes
🔗
|
-
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Precision Agriculture Based on Bayesian Neural Network
(
Poster
)
>
link
|
lei zhao
🔗
|
-
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Decomposing Representations for Deterministic Uncertainty Estimation
(
Poster
)
>
link
|
Haiwen Huang · Joost van Amersfoort · Yarin Gal
🔗
|
-
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Gaussian dropout as an information bottleneck layer
(
Poster
)
>
link
|
Melanie Rey · Andriy Mnih
🔗
|
-
|
Funnels: Exact maximum likelihood with dimensionality reduction
(
Poster
)
>
link
|
Samuel Klein · John Raine · Tobias Golling · Slava Voloshynovskiy · Sebastion Pina-Otey
🔗
|
-
|
Progress in Self-Certified Neural Networks
(
Poster
)
>
link
|
Maria Perez-Ortiz · Omar Rivasplata · Emilio Parrado-Hernández · Benjamin Guedj · John Shawe-Taylor
🔗
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-
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Multimodal Relational VAE
(
Poster
)
>
link
|
Thomas Sutter · Julia Vogt
🔗
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-
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Laplace Approximation with Diagonalized Hessian for Over-parameterized Neural Networks
(
Poster
)
>
link
|
Ming Gui · Ziqing Zhao · Tianming Qiu · Hao Shen
🔗
|
-
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Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings
(
Poster
)
>
link
|
Matias Valdenegro-Toro
🔗
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-
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Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning
(
Poster
)
>
link
|
Runa Eschenhagen · Erik Daxberger · Philipp Hennig · Agustinus Kristiadi
🔗
|
-
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Kronecker-Factored Optimal Curvature
(
Poster
)
>
link
|
Dominik Schnaus · Jongseok Lee · Rudolph Triebel
🔗
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-
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Contrastive Generative Adversarial Network for Anomaly Detection
(
Poster
)
>
link
|
Laya Rafiee Sevyeri · Thomas Fevens
🔗
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-
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Certifiably Robust Variational Autoencoders
(
Poster
)
>
link
|
Ben Barrett · Alexander Camuto · Matthew Willetts · Thomas Rainforth
🔗
|
-
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On Symmetries in Variational Bayesian Neural Nets
(
Poster
)
>
link
|
Richard Kurle · Tim Januschowski · Jan Gasthaus · Bernie Wang
🔗
|
-
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Greedy Bayesian Posterior Approximation with Deep Ensembles
(
Poster
)
>
link
|
Aleksei Tiulpin · Matthew Blaschko
🔗
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-
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On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty
(
Poster
)
>
link
|
Joost van Amersfoort · Lewis Smith · Andrew Jesson · Oscar Key · Yarin Gal
🔗
|
-
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An Empirical Study of Neural Kernel Bandits
(
Poster
)
>
link
|
Michal Lisicki · Arash Afkanpour · Graham Taylor
🔗
|
-
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Structured Stochastic Gradient MCMC: a hybrid VI and MCMC approach
(
Poster
)
>
link
|
Antonios Alexos · Alex Boyd · Stephan Mandt
🔗
|
-
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Contrastive Representation Learning with Trainable Augmentation Channel
(
Poster
)
>
link
|
Masanori Koyama · Kentaro Minami · Takeru Miyato · Yarin Gal
🔗
|
-
|
Power-law asymptotics of the generalization error for GP regression under power-law priors and targets
(
Poster
)
>
link
|
Hui Jin · Pradeep Kr. Banerjee · Guido Montufar
🔗
|
-
|
Deep Bayesian Learning for Car Hacking Detection
(
Poster
)
>
link
|
Laha Ale · Scott King · Ning Zhang
🔗
|
-
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Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
(
Poster
)
>
link
|
25 presenters
Zachary Nado · Neil Band · Mark Collier · Josip Djolonga · Mike Dusenberry · Sebastian Farquhar · Qixuan Feng · Angelos Filos · Marton Havasi · Rodolphe Jenatton · Ghassen Jerfel · Jeremiah Liu · Zelda Mariet · Jeremy Nixon · Shreyas Padhy · Jie Ren · Tim G. J. Rudner · Yeming Wen · Florian Wenzel · Kevin Murphy · D. Sculley · Balaji Lakshminarayanan · Jasper Snoek · Yarin Gal · Dustin Tran
🔗
|
-
|
Generation of data on discontinuous manifolds via continuous stochastic non-invertible networks
(
Poster
)
>
link
|
Mariia Drozdova · Vitaliy Kinakh · Guillaume Quétant · Tobias Golling · Slava Voloshynovskiy
🔗
|
-
|
Uncertainty Quantification in End-to-End Implicit Neural Representations for Medical Imaging
(
Poster
)
>
link
|
Francisca Vasconcelos · Bobby He · Yee Teh
🔗
|
-
|
Evaluating Predictive Uncertainty and Robustness to Distributional Shift Using Real World Data
(
Poster
)
>
link
|
Kumud Lakara · Akshat Bhandari · Pratinav Seth · Ujjwal Verma
🔗
|
-
|
Generalization Gap in Amortized Inference
(
Poster
)
>
link
|
Mingtian Zhang · Peter Hayes · David Barber
🔗
|
-
|
Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN
(
Poster
)
>
link
|
Vitaliy Kinakh · Mariia Drozdova · Guillaume Quétant · Tobias Golling · Slava Voloshynovskiy
🔗
|
-
|
Deep Classifiers with Label Noise Modeling and Distance Awareness
(
Poster
)
>
link
|
Vincent Fortuin · Mark Collier · Florian Wenzel · James Allingham · Jeremiah Liu · Dustin Tran · Balaji Lakshminarayanan · Jesse Berent · Rodolphe Jenatton · Effrosyni Kokiopoulou
🔗
|
-
|
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks
(
Poster
)
>
link
|
Neil Band · Tim G. J. Rudner · Qixuan Feng · Angelos Filos · Zachary Nado · Mike Dusenberry · Ghassen Jerfel · Dustin Tran · Yarin Gal
🔗
|
-
|
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness
(
Poster
)
>
link
|
Konstantinos Panousis · Sotirios Chatzis · Sergios Theodoridis
🔗
|
-
|
Being a Bit Frequentist Improves Bayesian Neural Networks
(
Poster
)
>
link
|
Agustinus Kristiadi · Matthias Hein · Philipp Hennig
🔗
|
-
|
Reproducible, incremental representation learning with Rosetta VAE
(
Poster
)
>
link
|
Miles Martinez · John Pearson
🔗
|
-
|
An Empirical Comparison of GANs and Normalizing Flows for Density Estimation
(
Poster
)
>
link
|
TIanci Liu · Jeffrey Regier
🔗
|
-
|
Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks
(
Poster
)
>
link
|
Ginevra Carbone · Luca Bortolussi · Guido Sanguinetti
🔗
|
-
|
Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data
(
Poster
)
>
link
|
Weichang Yu · Sara Wade · Howard Bondell · Lamiae Azizi
🔗
|
-
|
Pathologies in Priors and Inference for Bayesian Transformers
(
Poster
)
>
link
|
Tristan Cinquin · Alexander Immer · Max Horn · Vincent Fortuin
🔗
|
-
|
Analytically Tractable Inference in Neural Networks - An Alternative to Backpropagation
(
Poster
)
>
link
|
Luong-Ha Nguyen · James-A. Goulet
🔗
|
-
|
Infinite-channel deep convolutional Stable neural networks
(
Poster
)
>
link
|
Daniele Bracale · Stefano Favaro · Sandra Fortini · Stefano Peluchetti
🔗
|
-
|
Unveiling Mode-connectivity of the ELBO Landscape
(
Poster
)
>
link
|
Edith Zhang · David Blei
🔗
|