Ethan Gordon | Searcharxiv

arXiv2025

Understanding Textual Emotion Through Emoji Prediction

This project explores emoji prediction from short text sequences using four deep learning architectures: a feed-forward network, CNN, transformer, and BERT. Using the TweetEval dataset, we address class imbalance through focal loss and regularization techniques. Results show BERT achieves the highest overall performance due to its pre-training advantage, while CNN demonstrates superior efficacy on rare emoji classes. This research shows the importance of architecture selection and hyperparameter tuning for sentiment-aware emoji prediction, contributing to improved human-computer interaction.

cs.CL↗

arXiv2018

Mode Division Multiplexing (MDM) Weight Bank Design for Use in Photonic Neural Networks

Neural networks provide a powerful tool for applications from classification and regression to general purpose alternative computing. Photonics have the potential to provide enormous speed benefits over electronic and software networks, allowing such networks to be used in real-time applications at radio frequencies. Mode division multiplexing (MDM) is one method to increase the total information capacity of a single on-chip waveguide and, by extension, the information density of the photonic neural network (PNN). This Independent Work consists of three experimental designs ready for fabrication, each of which investigates the process of expanding current PNN technology to include MDM. Experiment 1 determines the optimal waveguide geometry to couple optical power into different spacial modes within a single waveguide. Experiment 2 combines MDM and previous wavelength division multiplexing (WDM) technology into a single weight bank for use as the dendrite of a photonic neuron. Finally, Experiment 3 puts two full neurons in a folded bus, or "hairpin," network topology to provide a platform for training calibration schemes that can be applied to larger networks.

eess.SP↗