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    Eugenia Iofinova

    Eugenia Iofinova

    5th year Ph.D. student in machine learning at IST Austria.

    • Vienna, Austria
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    SPADE: Sparsity-Guided Debugging for Deep Neural Networks.

    Arshia Soltani Moakhar, Eugenia Iofinova*, Elias Frantar, Dan Alistarh

    ICML 2024, 2024-07-21

    We improve DNN interpretability by computing a sparse trace of an input through a model prior to running interpretability methods.

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    Panza: A Personalized Text Writing Assistant via Data Playback and Local Fine-Tuning

    Armand Nicolicioiu, Eugenia Iofinova*, Eldar Kurtic, Mahdi Nikdan, Andrei Panferov, Ilia Markov, Nir Shavit, Dan Alistarh

    ArXiv, 2024-06-24

    We demonstrate the feasibility of training an e-mail composition assistant entirely on a consumer-grade GPU.

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    Accurate Neural Network Pruning Requires Rethinking Sparse Optimization

    Denis Kuznedelev*, Eldar Kurtic*, Eugenia Iofinova*, Elias Frantar, Alexandra Peste*, Dan Alistarh

    TMLR, 2024-06-20

    We show that, generally speaking, dense training settings are not optimal for sparse training for the same dataset/architecture.

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    SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks

    Mahdi Nikdan*, Tommasso Pegolotti*, Eugenia Iofinova, Eldar Kurtic, Dan Alistarh

    ICML 2023, 2023-07-23

    My groupmates found a way to do faster backpropagation through unstructured-sparsity weights.

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    Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures

    Eugenia Iofinova, Alexandra Peste, Dan Alistarh

    CVPR 2023, 2023-06-18

    We demonstrate that ‘stereotyping’, i.e., amplifying feature correlations, increases with model sparsity, thus leading to increased bias.

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    How Well Do Sparse ImageNet Models Transfer?

    Eugenia Iofinova*, Alexandra Peste*, Mark Kurtz, Dan Alistarh

    CVPR 2022, 2022-06-20

    We investigate the effect of the pruning method on transfer learning.

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    FLEA: Provably Fair Multisource Learning from Unreliable Training Data

    Eugenia Iofinova*, Nikola Konstantinov, Christoph H. Lampert

    Preprint, 2021-06-22

    In which we propose a theoretically rigorous algorithm for detecting and eliminating malignant (perturbed) data in a multisource setup.

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    AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks

    Alexandra Peste, Eugenia Iofinova, Adrian Vladu, Dan Alistarh

    NeurIPS 2021, 2021-05-21

    In which we refine the principle of Iterative Hard Thresholding to propose a simple and effective protocol for unstructured sparse training.

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    The VRNetzer platform enables interactive network analysis in Virtual Reality

    Sebastian Pirch, Felix Müller, Eugenia Iofinova, Julia Pazmandi, Christiane V. R. Hütter, Martin Chiettini, Celine Sin, Kaan Boztug, Iana Podkosova, Hannes Kaufmann, Jörg Menche

    Nature Communications, 2021-01-19

    3D interactive platform to explore biological networks

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    EpiMath Austria SEIR: A COVID-19 Compartment Model for Austria

    Manu Eder, Joachim Hermisson, Michal Hledik, Christiane Hütter, Eugenia Iofinova, Rahul Pisupati, Jitka Polechova, Gemma Puixeu, Srdjan Sarikas, Benjamin Wölfl and Claudia Zimmermann

    Preprint, 2021-01-19

    Modeling the spread of COVID-19 in Austria

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