This repository showcases my research and implementation of Sequential Generative Models. This project explores how deep learning architectures can capture long-term temporal dependencies in data, a fundamental challenge in both Computer Vision and Natural Language Understanding (NLU).
- Temporal Dependency Modeling: Advanced RNN/Transformer-based architectures for sequence prediction.
- Latent Space Exploration: Utilizing Variational Autoencoders (VAEs) and GANs to model sequential distributions.
- Siri-Inspired Logic: Refined algorithms for understanding context and sequence, mirroring the challenges found in Siri Understanding workflows.
- Scale-Ready Data Ingestion: Designed to handle streaming data inputs, utilizing principles learned from Spark Streaming and large-scale distributed systems.
- Modeling: PyTorch / TensorFlow.
- Processing: Python, Scala.
- Data Handling: Scalable patterns for high-dimensional sequential datasets.
Originally conceptualized during my time at UCSD, this framework serves as a playground for testing hypotheses in Sequential Generative Modeling, helping to bridge the gap between theoretical ML and production-grade conversational AI.
Developed by Kunal Jain — Machine Learning Engineer @ Apple