【阅读】Ilya的论文阅读清单

• 背景介绍
• 主要内容
  • 核心神经网络创新
  • 专业神经网络应用
  • 理论见解和原则性方法
  • 跨学科和概念研究
  • 效率和可扩展性技术
  • 教材和教程

背景介绍

Ilya Sutskever 的论文清单：30u30

Ilya Sutskever 是 Hinton 的大弟子，OpenAI的联合创始人兼首席科学家。

以下是他推荐的论文清单，他认为阅读完这些内容之后就可以了解AI领域90%的内容

主要内容

核心神经网络创新

1. Recurrent Neural Network Regularization - Enhancement to LSTM units for better overfitting prevention.
   递归神经网络正则化 - 增强了 LSTM 单元，以更好地防止过拟合。
2. Pointer Networks - Novel architecture for solving problems with discrete token outputs.
   Pointer Networks - 用于解决离散令牌输出问题的新颖架构.
3. Deep Residual Learning for Image Recognition - Improvements for training very deep networks through residual learning.
   用于图像识别的深度残差学习 - 改进了通过残差学习训练非常深度的网络。
4. Identity Mappings in Deep Residual Networks - Enhancements to deep residual networks through identity mappings.
   深度残差网络中的身份映射 - 通过身份映射增强深度残差网络。
5. Neural Turing Machines - Combining neural networks with external memory resources for enhanced algorithmic tasks.
   • 将神经网络与外部内存资源相结合，以增强算法任务。
6. Attention Is All You Need - Introducing the Transformer architecture solely based on attention mechanisms.
   Attention Is All You Need - 介绍完全基于注意力机制的 Transformer 架构。

专业神经网络应用

• Multi-Scale Context Aggregation by Dilated Convolutions - A convolutional network module for better semantic segmentation.
  Multi-Scale Context Aggregation by Dilated Convolutions - 一个卷积网络模块，用于更好的语义分割.
• Neural Machine Translation by Jointly Learning to Align and Translate - A model improving translation by learning to align and translate concurrently.
  Neural Machine Translation by Joint Learning to Align and Translation - 一种通过学习同时对齐和翻译来改进翻译的模型.
• Neural Message Passing for Quantum Chemistry - A framework for learning on molecular graphs for quantum chemistry.
  Neural Message Passing for Quantum Chemistry - 量子化学分子图学习框架.
• Relational RNNs - Enhancement to standard memory architectures integrating relational reasoning capabilities.Theoretical and Principled Approaches
  关系 RNN - 增强了集成关系推理功能的标准内存架构。理论和原则方法
• Deep Speech 2: End-to-End Speech Recognition in English and Mandarin - Deep learning system for speech recognition.
  Deep Speech 2： End-to-End Speech Recognition in English and Mandarin -用于语音识别的深度学习系统.
• ImageNet Classification with Deep CNNs - Convolutional neural network for classifying large-scale image data.
  ImageNet Classification with Deep CNNs - 用于对大规模图像数据进行分类的卷积神经网络.
• Variational Lossy Autoencoder - Combines VAEs and autoregressive models for improved image synthesis.
  变分有损自动编码器 - 结合 VAE 和自回归模型以改进图像合成。
• A Simple NN Module for Relational Reasoning - A neural module designed to improve relational reasoning in AI tasks.
  用于关系推理的简单神经网络模块 - 旨在改进 AI 任务中关系推理的神经模块.

理论见解和原则性方法

• Order Matters: Sequence to sequence for sets - Investigating the impact of data order on model performance.
  Order Matters： Sequence to sequence for sets - 调查数据顺序对模型性能的影响。
• Scaling Laws for Neural LMs - Empirical study on the scaling laws of language model performance.
  Scaling Laws for Neural LMs -语言模型性能的缩放规律的实证研究.
• A Tutorial Introduction to the Minimum Description Length Principle - Tutorial on the MDL principle in model selection and inference.
  最小描述长度原则教程简介 - 模型选择和推理中的MDL原理教程.
• Keeping Neural Networks Simple by Minimizing the Description Length of the Weights - Method to improve neural network generalization by minimizing weight description length.
  通过最小化权重的描述长度来保持神经网络的简单性 - 通过最小化权重描述长度来提高神经网络泛化的方法。
• Machine Super Intelligence DissertationMachine Super Intelligence Dissertation - Study on optimal behavior of agents in computable environments.
  机器 超级 智能 论文Machine Super Intelligence Dissertation - 研究智能体在可计算环境中的最优行为。
• PAGE 434 onwards: Komogrov Complexity - Comprehensive exploration of Kolmogorov complexity, discussing its mathematical foundations and implications for fields like information theory and computational complexity.
  第434页起：科莫格罗夫复杂性 - 全面探索柯尔莫戈罗夫复杂性，讨论其数学基础以及对信息论和计算复杂性等领域的影响。

跨学科和概念研究

• Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton - Study on complexity in closed systems using cellular automata.
  Quantifying the Rise and Fall of Complexity in Closed Systems： The Coffee Automaton - 使用元胞自动机研究封闭系统中的复杂性.

效率和可扩展性技术

• GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism - A method for efficient training of large-scale neural networks.
  GPipe： Efficient Training of Giant Neural Networks using Pipeline Parallelism - 一种高效训练大规模神经网络的方法.

教材和教程

• CS231n: Convolutional Neural Networks for Visual Recognition - Stanford University course on CNNs for visual recognition.
  CS231n：用于视觉识别的卷积神经网络 - 斯坦福大学视觉识别 CNN 课程。
• The Annotated Transformer - Annotated, line-by-line implementation of the Transformer paper. Code is available here.
  The Annotated Transformer - Transformer 论文的带注释的逐行实现.代码可在此处获得。
• The First Law of Complexodynamics - Blog post discussing the measure of system complexity in computational terms.
  The First Law of Complexodynamics - 讨论计算术语中系统复杂性度量的博客文章.
• The Unreasonable Effectiveness of RNNs - Blog post demonstrating the versatility of RNNs.
  RNN 的不合理有效性 - 展示 RNN 多功能性的博客文章.
• Understanding LSTM Networks - Blog post providing a detailed explanation of LSTM networks.
  了解 LSTM 网络 - 博客文章提供了 LSTM 网络的详细说明。