166 citations · 182 across the 6 of their papers we have counts for
7 papers
CacheFormer: High Attention-Based Segment Caching
Sushant Singh, Ausif Mahmood
Efficiently handling long contexts in transformer-based language models with low perplexity is an active area of research. Numerous recent approaches like Linformer, Longformer, Pe…
Neural Attention: A Novel Mechanism for Enhanced Expressive Power in Transformer Models
Andrew DiGiugno, Ausif Mahmood
Transformer models typically calculate attention matrices using dot products, which have limitations when capturing nonlinear relationships between embedding vectors. We propose Ne…
xLSTMTime : Long-term Time Series Forecasting With xLSTM
Musleh Alharthi, Ausif Mahmood
In recent years, transformer-based models have gained prominence in multivariate long-term time series forecasting (LTSF), demonstrating significant advancements despite facing cha…
The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architectures
Sushant Singh, Ausif Mahmood
In recent years, Natural Language Processing (NLP) models have achieved phenomenal success in linguistic and semantic tasks like text classification, machine translation, cognitive…
Neural Generative Models for 3D Faces with Application in 3D Texture Free Face Recognition
Ahmed ElSayed, Elif Kongar, Ausif Mahmood +2
Using heterogeneous depth cameras and 3D scanners in 3D face verification causes variations in the resolution of the 3D point clouds. To solve this issue, previous studies use 3D r…
Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild
Ahmed ElSayed, Ausif Mahmood, Tarek Sobh
Majority of the face recognition algorithms use query faces captured from uncontrolled, in the wild, environment. Often caused by the cameras limited capabilities, it is common for…
Highly Scalable, Parallel and Distributed AdaBoost Algorithm using Light Weight Threads and Web Services on a Network of Multi-Core Machines
Munther Abualkibash, Ahmed ElSayed, Ausif Mahmood
AdaBoost is an important algorithm in machine learning and is being widely used in object detection. AdaBoost works by iteratively selecting the best amongst weak classifiers, and…