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Mohit Kumar
- ESANN 2023 - Secure Federated Learning with Kernel Affine Hull Machines [Details]
- No papers found
- ESANN 2022 - Feature Compression Using Dynamic Switches in Multi-split CNNs [Details]
- ESANN 2019 - Fast and reliable architecture selection for convolutional neural networks [Details]
- ESANN 2007 - Data reduction using classifier ensembles [Details]
- ESANN 2021 - Data-Efficient Training of High-Resolution Images in Medical Domain [Details]
- ESANN 2020 - Towards Adversarial Attack Resistant Deep Neural Networks [Details]
- ESANN 2019 - Memory Efficient Weightless Neural Network using Bloom Filter [Details]
- ESANN 2005 - A Stability Condition for Neural Network Control of Uncertain Systems [Details]
- ESANN 2017 - Random projection initialization for deep neural networks [Details]
- ESANN 1998 - Self-organization in mixture densities of HMM based speech recognition [Details]
- ESANN 1994 - Approximation of continuous functions by RBF and KBF networks [Details]
- ESANN 1995 - Approximation of functions by Gaussian RBF networks with bouded number of hidden units [Details]
- ESANN 1996 - Rates of approximation of real-valued boolean functions by neural networks [Details]
- ESANN 2006 - Non-linear gating network for the large scale classification model CombNET-II [Details]
- ESANN 2011 - Comparison of the Complex Valued and Real Valued Neural Networks Trained with Gradient Descent and Random Search Algorithms [Details]
- ESANN 2006 - Selection of more than one gene at a time for cancer prediction from gene expression data [Details]
- ESANN 2010 - Validation of unsupervised clustering methods for leaf phenotype screening [Details]
- ESANN 1999 - Integrating the evidence framework and the support vector machine [Details]
- ESANN 1997 - Exact asymptotic estimates of the storage capacities of the committee machines with overlapping and non-overlapping receptive fields [Details]
- ESANN 2015 - Rank-constrained optimization: a Riemannian manifold approach [Details]
- ESANN 2023 - Introducing Convolutional Channel-wise Goodness in Forward-Forward Learning [Details]