Summary of Combining Statistical Depth and Fermat Distance For Uncertainty Quantification, by Hai-vy Nguyen et al.
Combining Statistical Depth and Fermat Distance for Uncertainty Quantificationby Hai-Vy Nguyen, Fabrice Gamboa, Reda Chhaibi,…
Combining Statistical Depth and Fermat Distance for Uncertainty Quantificationby Hai-Vy Nguyen, Fabrice Gamboa, Reda Chhaibi,…
Going Forward-Forward in Distributed Deep Learningby Ege Aktemur, Ege Zorlutuna, Kaan Bilgili, Tacettin Emre Bok,…
A Complexity Map of Probabilistic Reasoning for Neurosymbolic Classification Techniquesby Arthur Ledaguenel, Céline Hudelot, Mostepha…
Data-Driven Preference Sampling for Pareto Front Learningby Rongguang Ye, Lei Chen, Weiduo Liao, Jinyuan Zhang,…
Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Netby Indu…
DisorderUnetLM: Validating ProteinUnet for efficient protein intrinsic disorder predictionby Krzysztof Kotowski, Irena Roterman, Katarzyna StaporFirst…
Frame Quantization of Neural Networksby Wojciech Czaja, Sanghoon NaFirst submitted to arxiv on: 11 Apr…
Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulationby Lujie Yang, Hongkai Dai,…
A Parsimonious Setup for Streamflow Forecasting using CNN-LSTMby Sudan Pokharel, Tirthankar RoyFirst submitted to arxiv…
Gradient Networksby Shreyas Chaudhari, Srinivasa Pranav, José M. F. MouraFirst submitted to arxiv on: 10…