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Important Papers in AI

A curated collection of seminal papers that have shaped the field of artificial intelligence, machine learning, and deep learning.

ImageNet Classification with Deep Convolutional Neural Networks

2012
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
A landmark paper that introduced AlexNet, demonstrating the power of deep convolutional neural networks on the ImageNet dataset and sparking the deep learning revolution.
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Attention is All You Need

2017
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, et al.
Introduced the Transformer architecture, which relies entirely on attention mechanisms, revolutionizing NLP and enabling models like BERT and GPT.
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Playing Atari with Deep Reinforcement Learning

2013
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, et al.
Demonstrated the use of deep Q-networks (DQN) to play Atari games directly from pixels, a milestone in deep reinforcement learning.
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Generative Adversarial Nets

2014
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, et al.
Introduced GANs, a framework for training generative models via adversarial processes, leading to breakthroughs in image synthesis.
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

2018
Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
Presented BERT, a transformer-based model pre-trained on large corpora, achieving state-of-the-art results on a wide range of NLP tasks.
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Mastering the Game of Go with Deep Neural Networks and Tree Search

2016
David Silver, Aja Huang, Chris J Maddison, et al.
Describes AlphaGo, the first computer program to defeat a world champion in Go, combining deep neural networks and Monte Carlo tree search.
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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

2019
Victor Sanh, Lysandre Debut, Julien Chaumond, Thomas Wolf
Introduced DistilBERT, a smaller and faster version of BERT, making transformer models more accessible for practical applications.
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YOLOv1: You Only Look Once: Unified, Real-Time Object Detection

2016
Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi
Proposed YOLO, a real-time object detection system that reframes detection as a single regression problem, enabling fast and accurate detection.
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Tips for Reading Research Papers

Read this insightful guide: How to Read a Paper (Stanford)

This article is highly recommended for students new to reading research papers. It provides a practical, step-by-step approach to understanding and analyzing academic work.

  • Start by reading the abstract and conclusion to get the main idea.
  • Skim the introduction and figures to understand the motivation and results.
  • Focus on the methods and experiments sections for technical details.
  • Don't get stuck on every equation—try to grasp the intuition first.
  • Take notes and summarize each section in your own words.
  • Look up unfamiliar terms or references as you go.
  • Discuss the paper with peers or mentors to deepen your understanding.