Topic
- Computing and Processing
- Components, Circuits, Devices and Systems
- Communication, Networking and Broadcast Technologies
- Power, Energy and Industry Applications
- Signal Processing and Analysis
- Robotics and Control Systems
- General Topics for Engineers
- Fields, Waves and Electromagnetics
- Engineered Materials, Dielectrics and Plasmas
- Bioengineering
- Transportation
- Photonics and Electrooptics
- Engineering Profession
- Aerospace
- Geoscience
- Nuclear Engineering
- Career Development
- Emerging Technologies
- Telecommunications
- English for Technical Professionals
Jugal Kalita
Also published under:Jugal K. Kalita, J. Kalita, J. K. Kalita, Jugal Kumar Kalita, Jk Kalita
Affiliation
Department of Computer Science, University of Colorado, Colorado Springs, CO, USA
Topic
Transformer Model,Neural Network,Self-attention Mechanism,Image Captioning,Long Short-term Memory,Recurrent Neural Network,Visual Features,Word Embedding,WordNet,Artificial Neural Network,Attention Map,Attention Mechanism,Attention Values,Authentication Scheme,Average Accuracy,Biological Neural Networks,Brown Bears,CNN-based Models,Catastrophic Forgetting,Categories In Order,Cellular Technology,Certificate Authority,Classification Performance,Closed And Open,Cloud Computing,College Students,Comparative Analysis Of Models,Compression Ratio,Computer Vision,Confusion Matrix,Contralateral,Convolutional Layers,Convolutional Neural Network,Correct Spelling,Data Fusion,Data Privacy,Decoding,Deep Architecture,Deep Learning,Deep Neural Network,Deep Neural Network Architecture,Descriptive Sentences,Digital Signature,Dropout Rate,Electromyography,Emotion Categories,Emotion Recognition,Emotional States,Encoder Layer,Encoder Output,
Biography
Jugal Kalita is a Professor of computer science with the University of Colorado Colorado Springs, USA. His research interests include natural language processing, computational linguistics, machine learning, deep learning. He, his students, and collaborators have published over 250 papers with more than 12,000 citations. He has written several books, including the Machine Learning: Theory and Practice (CRC Press, 2023).