Research Papers

A collection of academic papers that have significantly influenced my understanding of machine learning, computer vision, and artificial intelligence. These papers represent key developments in their respective fields.

Attention Is All You Need

Vaswani et al.

2017

NeurIPS

Introduces the Transformer architecture, which has become the foundation for modern language models and revolutionized natural language processing.

Deep Learning

Language Models are Few-Shot Learners

Brown et al.

2020

NeurIPS

Introduces GPT-3 and demonstrates that large language models can perform tasks with few or no examples through in-context learning.

NLP

High-Resolution Image Synthesis with Latent Diffusion Models

Rombach et al.

2022

CVPR

Presents Stable Diffusion, a latent diffusion model for high-quality image synthesis that significantly reduces computational requirements.

Computer Vision

Constitutional AI: A Framework for Machine Ethics

Askell et al.

2023

arXiv

Introduces a framework for developing AI systems with built-in ethical principles and behavioral constraints, ensuring safer and more reliable AI development.

AI Ethics

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Lewis et al.

2020

NeurIPS

Presents RAG, combining neural seq2seq models with a retrieval component for knowledge-intensive tasks, improving factuality and transparency in language models.

NLP

Learning Transferable Visual Models From Natural Language Supervision

Radford et al.

2021

ICML

Introduces CLIP, demonstrating how to train highly versatile visual models using natural language supervision at scale.

Computer Vision