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Granger-causal Attentive Mixtures of Experts: Learning Important
  Features with Neural Networks

Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks

6 February 2018
Patrick Schwab
Djordje Miladinovic
W. Karlen
    CML
ArXivPDFHTML

Papers citing "Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks"

13 / 13 papers shown
Title
TPK: Trustworthy Trajectory Prediction Integrating Prior Knowledge For Interpretability and Kinematic Feasibility
TPK: Trustworthy Trajectory Prediction Integrating Prior Knowledge For Interpretability and Kinematic Feasibility
Marius Baden
Ahmed Abouelazm
Christian Hubschneider
Yin Wu
Daniel Slieter
J. M. Zöllner
31
0
0
10 May 2025
Neural Markov Jump Processes
Neural Markov Jump Processes
Patrick Seifner
Ramses J. Sanchez
BDL
35
7
0
31 May 2023
Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement
  Learning
Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning
Wenjie Shi
Gao Huang
Shiji Song
Cheng Wu
31
9
0
06 Dec 2021
Inductive Granger Causal Modeling for Multivariate Time Series
Inductive Granger Causal Modeling for Multivariate Time Series
Yunfei Chu
Xiaowei Wang
Jianxin Ma
Kunyang Jia
Jingren Zhou
Hongxia Yang
CML
AI4TS
11
11
0
10 Feb 2021
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
Ricards Marcinkevics
Julia E. Vogt
XAI
28
119
0
03 Dec 2020
Clinical Predictive Models for COVID-19: Systematic Study
Clinical Predictive Models for COVID-19: Systematic Study
Patrick Schwab
August DuMont Schütte
Benedikt Dietz
Stefan Bauer
OOD
ELM
42
35
0
17 May 2020
A Deep Learning Approach to Diagnosing Multiple Sclerosis from
  Smartphone Data
A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data
Patrick Schwab
W. Karlen
33
24
0
02 Jan 2020
Variable-lag Granger Causality for Time Series Analysis
Variable-lag Granger Causality for Time Series Analysis
Chainarong Amornbunchornvej
Elena Zheleva
T. Berger-Wolf
CML
AI4TS
22
21
0
18 Dec 2019
CXPlain: Causal Explanations for Model Interpretation under Uncertainty
CXPlain: Causal Explanations for Model Interpretation under Uncertainty
Patrick Schwab
W. Karlen
FAtt
CML
40
205
0
27 Oct 2019
Learning Counterfactual Representations for Estimating Individual
  Dose-Response Curves
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Patrick Schwab
Lorenz Linhardt
Stefan Bauer
J. M. Buhmann
W. Karlen
CML
OOD
29
131
0
03 Feb 2019
Perfect Match: A Simple Method for Learning Representations For
  Counterfactual Inference With Neural Networks
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
Patrick Schwab
Lorenz Linhardt
W. Karlen
CML
BDL
13
111
0
01 Oct 2018
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical
  Care
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
Patrick Schwab
E. Keller
C. Muroi
David J. Mack
C. Strässle
W. Karlen
23
23
0
14 Feb 2018
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis
Rushil Anirudh
Jayaraman J. Thiagarajan
R. Sridhar
T. Bremer
FAtt
AAML
23
12
0
15 Nov 2017
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