Search Results for author: Daniel Fink

Found 6 papers, 1 papers with code

Training robust and generalizable quantum models

1 code implementation20 Nov 2023 Julian Berberich, Daniel Fink, Daniel Pranjić, Christian Tutschku, Christian Holm

We derive tailored, parameter-dependent Lipschitz bounds for quantum models with trainable encoding, showing that the norm of the data encoding has a crucial impact on the robustness against perturbations in the input data.

Adversarial Robustness Quantum Machine Learning

Quantum computing through the lens of control: A tutorial introduction

no code implementations19 Oct 2023 Julian Berberich, Daniel Fink

In particular, beyond the tutorial introduction, we provide a list of research challenges in the field of quantum computing and discuss their connections to control.

A Double Machine Learning Trend Model for Citizen Science Data

no code implementations27 Oct 2022 Daniel Fink, Alison Johnston, Matt Strimas-Mackey, Tom Auer, Wesley M. Hochachka, Shawn Ligocki, Lauren Oldham Jaromczyk, Orin Robinson, Chris Wood, Steve Kelling, Amanda D. Rodewald

We used a simulation study to assess the ability of the method to estimate spatially varying trends in the face of real-world confounding.

HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders

no code implementations9 Mar 2021 Wenting Zhao, Shufeng Kong, Junwen Bai, Daniel Fink, Carla Gomes

This in turn leads to a challenging and long-standing problem in the field of computer science - how to perform ac-curate multi-label classification with hundreds of labels?

Multi-Label Classification

Enhanced Optimization with Composite Objectives and Novelty Selection

no code implementations10 Mar 2018 Hormoz Shahrzad, Daniel Fink, Risto Miikkulainen

An important benefit of multi-objective search is that it maintains a diverse population of candidates, which helps in deceptive problems in particular.

Deep Multi-Species Embedding

no code implementations28 Sep 2016 Di Chen, Yexiang Xue, Shuo Chen, Daniel Fink, Carla Gomes

Additionally, we demonstrate the benefit of using a deep neural network to extract features within the embedding and show how they improve the predictive performance of species distribution modelling.

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