research
∙
08/23/2023
Graph Neural Stochastic Differential Equations
We present a novel model Graph Neural Stochastic Differential Equations ...
share
research
∙
08/20/2023
SE(3) Equivariant Augmented Coupling Flows
Coupling normalizing flows allow for fast sampling and density evaluatio...
research
∙
07/15/2023
Minimal Random Code Learning with Mean-KL Parameterization
This paper studies the qualitative behavior and robustness of two varian...
research
∙
07/12/2023
Online Laplace Model Selection Revisited
The Laplace approximation provides a closed-form model selection objecti...
research
∙
06/26/2023
Leveraging Task Structures for Improved Identifiability in Neural Network Representations
This work extends the theory of identifiability in supervised learning b...
research
∙
06/26/2023
Tanimoto Random Features for Scalable Molecular Machine Learning
The Tanimoto coefficient is commonly used to measure the similarity betw...
research
∙
06/20/2023
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
Gaussian processes are a powerful framework for quantifying uncertainty ...
research
∙
05/30/2023
Compression with Bayesian Implicit Neural Representations
Many common types of data can be represented as functions that map coord...
research
∙
02/20/2023
Fast and Painless Image Reconstruction in Deep Image Prior Subspaces
The deep image prior (DIP) is a state-of-the-art unsupervised approach f...
research
∙
10/10/2022
Sampling-based inference for large linear models, with application to linearised Laplace
Large-scale linear models are ubiquitous throughout machine learning, wi...
research
∙
08/03/2022
Flow Annealed Importance Sampling Bootstrap
Normalizing flows are tractable density models that can approximate comp...
research
∙
07/11/2022
Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior
We investigate adaptive design based on a single sparse pilot scan for g...
research
∙
06/17/2022
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
The linearised Laplace method for estimating model uncertainty has recei...
research
∙
05/05/2022
Meta-learning Feature Representations for Adaptive Gaussian Processes via Implicit Differentiation
We propose Adaptive Deep Kernel Fitting (ADKF), a general framework for ...
research
∙
02/28/2022
A Probabilistic Deep Image Prior for Computational Tomography
Existing deep-learning based tomographic image reconstruction methods do...
research
∙
02/09/2022
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo
Variational Autoencoders (VAEs) have recently been highly successful at ...
research
∙
01/30/2022
Fast Relative Entropy Coding with A* coding
Relative entropy coding (REC) algorithms encode a sample from a target d...
research
∙
12/13/2021
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not
Farquhar et al. [2021] show that correcting for active learning bias wit...
research
∙
12/13/2021
Depth Uncertainty Networks for Active Learning
In active learning, the size and complexity of the training dataset chan...
research
∙
11/22/2021
Bootstrap Your Flow
Normalizing flows are flexible, parameterized distributions that can be ...
research
∙
10/29/2021
Resampling Base Distributions of Normalizing Flows
Normalizing flows are a popular class of models for approximating probab...
research
∙
10/29/2021
DOCKSTRING: easy molecular docking yields better benchmarks for ligand design
The field of machine learning for drug discovery is witnessing an explos...
research
∙
10/20/2021
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation
Machine learning training methods depend plentifully and intricately on ...
research
∙
10/12/2021
Action-Sufficient State Representation Learning for Control with Structural Constraints
Perceived signals in real-world scenarios are usually high-dimensional a...
research
∙
06/30/2021
Improving black-box optimization in VAE latent space using decoder uncertainty
Optimization in the latent space of variational autoencoders is a promis...
research
∙
02/24/2021
Nonlinear Invariant Risk Minimization: A Causal Approach
Due to spurious correlations, machine learning systems often fail to gen...
research
∙
02/05/2021
Active Slices for Sliced Stein Discrepancy
Sliced Stein discrepancy (SSD) and its kernelized variants have demonstr...
research
∙
12/21/2020
Barking up the right tree: an approach to search over molecule synthesis DAGs
When designing new molecules with particular properties, it is not only ...
research
∙
12/16/2020
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation
Reinforcement learning (RL) algorithms usually require a substantial amo...
research
∙
12/02/2020
FIT: a Fast and Accurate Framework for Solving Medical Inquiring and Diagnosing Tasks
Automatic self-diagnosis provides low-cost and accessible healthcare via...
research
∙
11/25/2020
Symmetry-Aware Actor-Critic for 3D Molecular Design
Automating molecular design using deep reinforcement learning (RL) has t...
research
∙
10/28/2020
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference
The Bayesian paradigm has the potential to solve some of the core issues...
research
∙
10/02/2020
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding
Variational Autoencoders (VAEs) have seen widespread use in learned imag...
research
∙
07/23/2020
Diagnostic Questions:The NeurIPS 2020 Education Challenge
Digital technologies are becoming increasingly prevalent in education, e...
research
∙
07/16/2020
DRIFT: Deep Reinforcement Learning for Functional Software Testing
Efficient software testing is essential for productive software developm...
research
∙
06/30/2020
Sliced Kernelized Stein Discrepancy
Kernelized Stein discrepancy (KSD), though being extensively used in goo...
research
∙
06/21/2020
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
Deep generative models often perform poorly in real-world applications d...
research
∙
06/18/2020
Predictive Complexity Priors
Specifying a Bayesian prior is notoriously difficult for complex models ...
research
∙
06/16/2020
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
Many important problems in science and engineering, such as drug design,...
research
∙
06/15/2020
Depth Uncertainty in Neural Networks
Existing methods for estimating uncertainty in deep learning tend to req...
research
∙
06/11/2020
Getting a CLUE: A Method for Explaining Uncertainty Estimates
Both uncertainty estimation and interpretability are important factors f...
research
∙
05/15/2020
Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures
When learning to ride a bike, a child falls down a number of times befor...
research
∙
03/12/2020
Large-Scale Educational Question Analysis with Partial Variational Auto-encoders
Online education platforms enable teachers to share a large number of ed...
research
∙
02/18/2020
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Automating molecular design using deep reinforcement learning (RL) holds...
research
∙
02/06/2020
Variational Depth Search in ResNets
One-shot neural architecture search allows joint learning of weights and...
research
∙
12/11/2019
Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection
Despite their successes, deep neural networks still make unreliable pred...
research
∙
09/25/2019
A Generative Model for Molecular Distance Geometry
Computing equilibrium states for many-body systems, such as molecules, i...
research
∙
08/13/2019
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
In this paper we introduce the ice-start problem, i.e., the challenge of...
research
∙
08/06/2019
Bayesian Batch Active Learning as Sparse Subset Approximation
Leveraging the wealth of unlabeled data produced in recent years provide...
research
∙
06/27/2019
