Showing 12 of 32 research papers
Attention Is All You Need
Ashish Vaswani et al.
Introduced the Transformer architecture, which is the foundation for most modern large language models.
Generative Adversarial Nets
Ian J. Goodfellow et al.
Proposed the GAN framework, a novel way to train generative models, leading to breakthroughs in image generation.
Deep Residual Learning for Image Recognition
Kaiming He et al.
Introduced residual networks (ResNets), enabling the training of much deeper neural networks than previously possible.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin et al.
A powerful language representation model that considers the full context of a word by looking at the text before and after it.
Language Models are Few-Shot Learners
Tom B. Brown et al.
Introduced GPT-3 and demonstrated that large language models can perform a variety of tasks without fine-tuning.
Mastering the game of Go with deep neural networks and tree search
David Silver et al.
Detailed the AlphaGo system, which defeated a world champion Go player, a landmark achievement for AI.
Denoising Diffusion Probabilistic Models
Jonathan Ho et al.
A foundational paper on diffusion models which have become state-of-the-art for high-quality image generation.
Learning Transferable Visual Models From Natural Language Supervision
Alec Radford et al.
Introduced CLIP, a model that learns visual concepts from natural language, enabling powerful zero-shot image classification.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy et al.
Applied the Transformer architecture directly to images, challenging the dominance of CNNs in computer vision.
Human-level control through deep reinforcement learning
Volodymyr Mnih et al.
The Deep Q-Network (DQN) paper that demonstrated an AI learning to play Atari games from raw pixel data.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Jason Wei et al.
Showed that prompting LLMs to generate a series of intermediate reasoning steps improves their performance on complex tasks.
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky et al.
The AlexNet paper, which kickstarted the deep learning revolution by winning the ImageNet competition.