MA1.L2.2
PAON: A NEW NEURON MODEL USING PADE APPROXIMANTS
Onur Keleş, A. Murat Tekalp, Koç University, Türkiye
Session:
MA1.L2: Advances in Neural Models Lecture
Track:
Multidimensional Signal Processing
Location:
Lecture Room 2
Presentation Time:
Mon, 28 Oct, 08:48 - 09:06 Gulf Standard Time (UTC +4)
Session Chair:
Eduardo Silva, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
Session MA1.L2
MA1.L2.1: ENN: A NEURAL NETWORK WITH DCT ADAPTIVE ACTIVATION FUNCTIONS
Marc Martinez-Gost, CTTC - UPC, Spain; Ana Isabel Pérez Neira, CTTC, Space and Resilient Communications, Spain; Miguel Ángel Lagunas, UPC, Spain
MA1.L2.2: PAON: A NEW NEURON MODEL USING PADE APPROXIMANTS
Onur Keleş, A. Murat Tekalp, Koç University, Türkiye
MA1.L2.3: Non-Separable Wavelet Transform using Learnable Convolutional Lifting Steps
João Parracho, Federal University of Rio de Janeiro, Brazil; Eduardo Silva, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil, Brazil; Lucas Thomaz, Luis Távora, Sergio Faria, Instituto de Telecomunicações / Polytechnic of Leiria, Brazil
MA1.L2.4: Trainable Fractional Fourier Transform
Emirhan Koc, Tuna Alikasifoglu, Arda Can Aras, Aykut Koc, Bilkent University, Türkiye
MA1.L2.5: Robustness of tensor decomposition-based neural network compression
Théo Rudkiewicz, Ecole Normale Supérieure Paris-Saclay, France; Mohamed Ouerfelli, Zakariya Chaouai, Riccardo Finotello, Mohamed Tamaazousti, Université Paris-Saclay, CEA, List, France
Contacts