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Antti Pihlajamäki
- ESANN 2021 - Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces [Details]
- ESANN 2006 - Classification of Boar Sperm Head Images using Learning Vector Quantization [Details]
- ESANN 2015 - Model Selection for Big Data: Algorithmic Stability and Bag of Little Bootstraps on GPUs [Details]
- ESANN 1994 - Variable binding in a neural network using a distributed representation [Details]
- ESANN 1999 - Mean-field equations reveal synchronization in a 2-populations neural network model [Details]
- ESANN 2023 - Large-scale dataset and benchmarking for hand and face detection focused on sign language [Details]
- ESANN 2024 - Generation of Simulated Dataset of Computed Tomography Images of Eggs and Extraction of Measurements Using Deep Learning [Details]
- ESANN 2023 - A model-based approach to meta-Reinforcement Learning: Transformers and tree search [Details]
- ESANN 2015 - An affinity matrix approach for structure selection of extreme learning machines [Details]
- ESANN 2024 - Leveraging Physics-Informed Neural Networks as Solar Wind Forecasting Models [Details]
- ESANN 2021 - Slope: A First-order Approach for Measuring Gradient Obfuscation [Details]
- ESANN 2023 - Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization [Details]
- ESANN 2023 - Towards Machine Learning Models that We Can Trust: Testing, Improving, and Explaining Robustness [Details]
- ESANN 2006 - The combination of STDP and intrinsic plasticity yields complex dynamics in recurrent spiking networks [Details]
- ESANN 2015 - Designing semantic feature spaces for brain-reading [Details]
- ESANN 1996 - Neural versus neurofuzzy systems for credit approval [Details]
- ESANN 2023 - Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization [Details]
- ESANN 2025 - Enhancing Image Classification in Quantum Computing: A Study on Preprocessing Techniques and Qubit Limitations [Details]
- ESANN 2020 - Variational MIxture of Normalizing Flows [Details]
- ESANN 2016 - Multi-task learning for speech recognition: an overview [Details]
- ESANN 2025 - Explainable ensemble learning for structural damage prediction under seismic events [Details]
- ESANN 2025 - A Model of Memristive Nanowire Neuron for Recurrent Neural Networks [Details]
- ESANN 2000 - A statistical model selection strategy applied to neural networks [Details]
- ESANN 2002 - A resampling and multiple testing-based procedure for determining the size of a neural network [Details]
- ESANN 2002 - Noise derived information criterion for model selection [Details]
- ESANN 2003 - A new Meta Machine Learning (MML) method based on combining non-significant different neural networks [Details]
- ESANN 2010 - Combining back-propagation and genetic algorithms to train neural networks for start-up time modeling in combined cycle power plants [Details]
- ESANN 2016 - Bag-of-Steps: predicting lower-limb fracture rehabilitation length [Details]
- ESANN 2017 - ELM Preference Learning for Physiological Data [Details]
- ESANN 2006 - Learning Visual Invariance [Details]
- ESANN 2025 - Quantum Tensor Network Learning with DMRG [Details]
- ESANN 2019 - Real-time Convolutional Neural Networks for emotion and gender classification [Details]
- ESANN 2014 - Enhanced NMF initialization using a physical model for pollution source apportionment [Details]