63 citations · 312 across the 51 of their papers we have counts for
9 papers · 1 filter
Minimizing the Effect of Sleep Deprivation in the Forward-Forward Algorithm
Joy Datta, Puja Saha, Rawhatur Rabbi +4
This paper addresses the challenge posed by sleep deprivation in the Forward-Forward algorithm, where separating the two passes in this algorithm and imbalancing the data processin…
HingeRLC-GAN: Combating Mode Collapse with Hinge Loss and RLC Regularization
Osman Goni, Himadri Saha Arka, Mithun Halder +2
Recent advances in Generative Adversarial Networks (GANs) have demonstrated their capability for producing high-quality images. However, a significant challenge remains mode collap…
Adaptive Tabu Dropout for Regularization of Deep Neural Network
Md. Tarek Hasan, Arifa Akter, Mohammad Nazmush Shamael +4
Dropout is an effective strategy for the regularization of deep neural networks. Applying tabu to the units that have been dropped in the recent epoch and retaining them for traini…
Deep Transfer Learning Based Peer Review Aggregation and Meta-review Generation for Scientific Articles
Md. Tarek Hasan, Mohammad Nazmush Shamael, H. M. Mutasim Billah +5
Peer review is the quality assessment of a manuscript by one or more peer experts. Papers are submitted by the authors to scientific venues, and these papers must be reviewed by pe…
A Gray Box Interpretable Visual Debugging Approach for Deep Sequence Learning Model
Md Mofijul Islam, Amar Debnath, Tahsin Al Sayeed +6
Deep Learning algorithms are often used as black box type learning and they are too complex to understand. The widespread usability of Deep Learning algorithms to solve various mac…
FRnet-DTI: Deep Convolutional Neural Networks with Evolutionary and Structural Features for Drug-Target Interaction
Farshid Rayhan, Sajid Ahmed, Zaynab Mousavian +2
The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto encoder and a…
MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification
Farshid Rayhan, Sajid Ahmed, Asif Mahbub +4
Class imbalance problem has been a challenging research problem in the fields of machine learning and data mining as most real life datasets are imbalanced. Several existing machin…
CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification
Farshid Rayhan, Sajid Ahmed, Asif Mahbub +3
Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing lea…
LIUBoost : Locality Informed Underboosting for Imbalanced Data Classification
Sajid Ahmed, Farshid Rayhan, Asif Mahbub +4
The problem of class imbalance along with class-overlapping has become a major issue in the domain of supervised learning. Most supervised learning algorithms assume equal cardinal…