Alejandro Molina

Orcid: 0000-0003-4509-9174

Affiliations:
  • TU Dortmund, Department of Computer Science, Germany


According to our database1, Alejandro Molina authored at least 47 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Article 
PhD thesis 
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Links

Online presence:

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Bibliography

2024
Adaptive Rational Activations to Boost Deep Reinforcement Learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2022
Next2You: Robust Copresence Detection Based on Channel State Information.
ACM Trans. Internet Things, 2022

Conditional sum-product networks: Modular probabilistic circuits via gate functions.
Int. J. Approx. Reason., 2022

2021
Evaluation results from "Next2You: Robust Copresence Detection Based on Channel State Information".
Dataset, October, 2021

Raw data from "Next2You: Robust Copresence Detection Based on Channel State Information".
Dataset, October, 2021

Index of Supplementary Files from "Next2You: Robust Copresence Detection Based on Channel State Information".
Dataset, July, 2021

Recurrent Rational Networks.
CoRR, 2021

2020
DeepDB: Learn from Data, not from Queries!
Proc. VLDB Endow., 2020

Residual Sum-Product Networks.
Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures.
Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits.
Proceedings of the 37th International Conference on Machine Learning, 2020

Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks.
Proceedings of the 8th International Conference on Learning Representations, 2020

CryptoSPN: Privacy-Preserving Sum-Product Network Inference.
Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020

CryptoSPN: Expanding PPML beyond Neural Networks.
Proceedings of the PPMLP'20: Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice, 2020

2019
Index of supplementary files from "Perils of Zero-Interaction Security in the Internet of Things".
Dataset, January, 2019

Raw data from Mobile scenario from "Perils of Zero-Interaction Security in the Internet of Things".
Dataset, January, 2019

Raw data from Office scenario from "Perils of Zero-Interaction Security in the Internet of Things".
Dataset, January, 2019

Raw data from Car scenario from "Perils of Zero-Interaction Security in the Internet of Things".
Dataset, January, 2019

Processed data from Office scenario from "Perils of Zero-Interaction Security in the Internet of Things".
Dataset, January, 2019

Processed data from Car scenario from "Perils of Zero-Interaction Security in the Internet of Things".
Dataset, January, 2019

Processed data from Mobile scenario from "Perils of Zero-Interaction Security in the Internet of Things".
Dataset, January, 2019

Results for scheme by Karapanos et al. from "Perils of Zero-Interaction Security in the Internet of Things".
Dataset, January, 2019

Results for scheme by Schürmann and Sigg from "Perils of Zero-Interaction Security in the Internet of Things".
Dataset, January, 2019

Results for scheme by Miettinen et al. from "Perils of Zero-Interaction Security in the Internet of Things".
Dataset, January, 2019

Results for scheme by Truong et al. from "Perils of Zero-Interaction Security in the Internet of Things".
Dataset, January, 2019

Results for scheme by Shrestha et al. from "Perils of Zero-Interaction Security in the Internet of Things".
Dataset, January, 2019

Perils of Zero-Interaction Security in the Internet of Things.
Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2019

Random Sum-Product Forests with Residual Links.
CoRR, 2019

SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks.
CoRR, 2019

Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Gaussian Lifted Marginal Filtering.
Proceedings of the KI 2019: Advances in Artificial Intelligence, 2019

Resource-Efficient Logarithmic Number Scale Arithmetic for SPN Inference on FPGAs.
Proceedings of the International Conference on Field-Programmable Technology, 2019

Automatic Bayesian Density Analysis.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Model-based Approximate Query Processing.
CoRR, 2018

Probabilistic Deep Learning using Random Sum-Product Networks.
CoRR, 2018

Gaussian Lifted Marginal Filtering.
Proceedings of the 5th international Workshop on Sensor-based Activity Recognition and Interaction, 2018

Automatic Mapping of the Sum-Product Network Inference Problem to FPGA-Based Accelerators.
Proceedings of the 36th IEEE International Conference on Computer Design, 2018

Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Core Dependency Networks.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Sum-Product Networks for Hybrid Domains.
CoRR, 2017

Coresets for Dependency Networks.
CoRR, 2017

Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage.
CoRR, 2016

2015
Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data.
Mach. Learn., 2015

LTE Connectivity and Vehicular Traffic Prediction Based on Machine Learning Approaches.
Proceedings of the IEEE 82nd Vehicular Technology Conference, 2015


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