Martin Trapp
16 papers · 2019–2025 · 8 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π Interdisciplinary Bridge π§ Keyword Pioneer π Academic Marathon (6) π Cross-Pollinator (13)
πΊοΈ
Taxonomy Completionist
(18)
π
Conference Polyglot
(8)
π
Century Club
(16)
π₯
Unstoppable
(7)
π
Conference Pioneer
Conferences
UAI (4)
NIPS (3)
ICLR (2)
PGM (2)
WACV (2)
AISTATS (1)
ECCV (1)
ICML (1)
Top co-authors
Keywords
probabilistic circuit
(4)
sum-product network
(3)
gaussian process
(3)
uncertainty quantification
(3)
density estimation
(2)
probabilistic modeling
(2)
posterior inference
(2)
structure learning
(2)
generative model
(2)
energy efficiency
(1)
automatic differentiation
(1)
deep learning
(1)
bayesian inference
(1)
epistemic uncertainty
(1)
characteristic function
(1)
anomaly detection
(1)
bayesian regression
(1)
multi-output regression
(1)
probabilistic inference
(1)
exact inference
(1)
Papers
Flatness Improves Backbone Generalisation in Few-Shot Classification
WACV 2025
Streamlining Prediction in Bayesian Deep Learning
ICLR 2025
Approximate Bayesian Inference via Bitstring Representations
UAI 2025
Fixing Overconfidence in Dynamic Neural Networks
WACV 2024
On Hardware-efficient Inference in Probabilistic Circuits
UAI 2024
Subtractive Mixture Models via Squaring: Representation and Learning
ICLR 2024
Characteristic Circuits
NIPS 2023
Uncertainty-Guided Source-Free Domain Adaptation
ECCV 2022
A Hardware Perspective to Evaluating Probabilistic Circuits
PGM 2022
Periodic Activation Functions Induce Stationarity
NIPS 2021
Leveraging probabilistic circuits for nonparametric multi-output regression
UAI 2021
Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations
PGM 2020
Deep Structured Mixtures of Gaussian Processes
AISTATS 2020
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
ICML 2020
Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning
UAI 2019
Bayesian Learning of Sum-Product Networks
NIPS 2019