2 code implementations • 5 Apr 2024 • Selim Furkan Tekin, Fatih Ilhan, Tiansheng Huang, Sihao Hu, Ka-Ho Chow, Margaret L. Loper, Ling Liu
This paper presents FusionShot, a focal diversity optimized few-shot ensemble learning approach for boosting the robustness and generalization performance of pre-trained few-shot models.
1 code implementation • 2 Apr 2024 • Sihao Hu, Tiansheng Huang, Fatih Ilhan, Selim Tekin, Gaowen Liu, Ramana Kompella, Ling Liu
The development of game agents holds a critical role in advancing towards Artificial General Intelligence (AGI).
1 code implementation • CVPR 2023 • Ka-Ho Chow, Ling Liu, Wenqi Wei, Fatih Ilhan, Yanzhao Wu
Based on the insights, we introduce a three-tier forensic framework to identify and expel Trojaned gradients and reclaim the performance over the course of FL.
1 code implementation • 15 Jan 2023 • Fatih Ilhan, Ka-Ho Chow, Sihao Hu, Tiansheng Huang, Selim Tekin, Wenqi Wei, Yanzhao Wu, Myungjin Lee, Ramana Kompella, Hugo Latapie, Gaowen Liu, Ling Liu
Instead of having every sample go through all DNN layers during prediction, EENet learns an early exit scheduler, which can intelligently terminate the inference earlier for certain predictions, which the model has high confidence of early exit.
1 code implementation • CVPR 2023 • Fatih Ilhan, Gong Su, Ling Liu
In most FL approaches, all edge clients are assumed to have sufficient computation capabilities to participate in the learning of a deep neural network (DNN) model.
no code implementations • 17 Jun 2020 • Fatih Ilhan, Oguzhan Karaahmetoglu, Ismail Balaban, Suleyman Serdar Kozat
We investigate nonlinear regression for nonstationary sequential data.
no code implementations • 25 May 2020 • Oguzhan Karaahmetoglu, Fatih Ilhan, Ismail Balaban, Suleyman Serdar Kozat
We study anomaly detection and introduce an algorithm that processes variable length, irregularly sampled sequences or sequences with missing values.
no code implementations • 16 May 2020 • N. Mert Vural, Fatih Ilhan, Selim F. Yilmaz, Salih Ergüt, Suleyman S. Kozat
Recurrent Neural Networks (RNNs) are widely used for online regression due to their ability to generalize nonlinear temporal dependencies.
no code implementations • 7 Mar 2020 • Fatih Ilhan, Suleyman Serdar Kozat
We introduce a novel inference framework based on randomized transformations and gradient descent to learn the process.
no code implementations • 7 Mar 2020 • N. Mert Vural, Selim F. Yilmaz, Fatih Ilhan, Suleyman S. Kozat
We investigate online nonlinear regression with continually running recurrent neural network networks (RNNs), i. e., RNN-based online learning.
no code implementations • 25 Nov 2019 • Nuri Mert Vural, Fatih Ilhan, Suleyman S. Kozat
We investigate the convergence and stability properties of the decoupled extended Kalman filter learning algorithm (DEKF) within the long-short term memory network (LSTM) based online learning framework.