Patrick van der Smagt
17 papers · 2015–2024 · 8 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+7 more ↓ Show less ↑
πΊοΈ Taxonomy Completionist (36) π Conference Polyglot Β· Silver π Academic Marathon Β· Sprint π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
π
Grand Slam
π§¬
Topic Evolution
π
Renaissance Researcher
(5)
π₯
Unstoppable Β· Bronze
π
Conference Pioneer
π
Century Club Β· Bronze
ποΈ
Keyword Collector
(79)
Top co-authors
Research topics
Machine Learning
(11)
Artificial Intelligence
(10)
Deep Learning
(8)
Core AI
(7)
Optimization & Theory
(7)
Models
(6)
Learning Types Β· Machine Learning
(5)
Learning Types Β· Deep Learning
(5)
Reinforcement Learning
(4)
Bayesian & Probabilistic Β· Artificial Intelligence
(4)
Core Methods
(4)
Bayesian & Probabilistic Β· Machine Learning
(3)
Methods
(3)
Applications
(2)
Computer Vision
(1)
Keywords
variational inference
(8)
state space model
(4)
variational autoencoder
(4)
probabilistic inference
(2)
policy optimization
(2)
generative model
(2)
world model
(2)
differentiable rendering
(2)
constrained optimization
(2)
metric learning
(1)
multi-source learning
(1)
sample efficiency
(1)
pose estimation
(1)
bayesian inference
(1)
reinforcement learning
(1)
imitation learning
(1)
zero-shot learning
(1)
3d vision
(1)
video understanding
(1)
supervised learning
(1)
Papers
Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning
NeurIPS 2024
Exploring Under Constraints with Model-Based Actor-Critic and Safety Filters
CoRL 2024
Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models
NeurIPS 2023
CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces
L4DC 2023
Filter-Aware Model-Predictive Control
L4DC 2023
PRISM: Probabilistic Real-Time Inference in Spatial World Models
CoRL 2022
Tracking and Planning with Spatial World Models
L4DC 2022
Latent Matters: Learning Deep State-Space Models
NeurIPS 2021
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models
ICLR 2021
Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF
ICLR 2021
Continual Learning with Bayesian Neural Networks for Non-Stationary Data
ICLR 2020
Learning Flat Latent Manifolds with VAEs
ICML 2020
Approximate Bayesian Inference in Spatial Environments
RSS 2019
Learning Hierarchical Priors in VAEs
NeurIPS 2019
Multi-Source Neural Variational Inference
AAAI 2019
Switching Linear Dynamics for Variational Bayes Filtering
ICML 2019
FlowNet: Learning Optical Flow With Convolutional Networks
ICCV 2015