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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0"/>
<title>AI Papers Reading List • Diego Rodríguez Atencia</title>
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</head>
<body>
<!-- Navigation Bar -->
<nav>
<div class="nav-container">
<div class="nav-links">
<a href="/">Home</a>
<a href="/blog/blog.html">Blog</a>
<a href="https://regllm.xyz" target="_blank" rel="noopener">regllm.xyz</a>
</div>
</div>
</nav>
<div class="container">
<!-- Hero Section -->
<header>
<h1>AI Papers Reading List</h1>
<p class="subtitle">
A comprehensive collection of foundational AI and LLM papers
</p>
<p class="subtitle">
Based on the <a href="https://github.com/henrythe9th/AI-Crash-Course" target="_blank" rel="noopener" style="color: var(--accent); text-decoration: none;">AI Crash Course by Henry Shi</a>
</p>
<a href="https://github.com/henrythe9th/AI-Crash-Course" target="_blank" rel="noopener" class="repo-link">
View Original Repository →
</a>
</header>
<!-- View Toggle -->
<div class="view-toggle">
<button class="toggle-button active" onclick="switchView('list')">📋 List View</button>
<button class="toggle-button" onclick="switchView('graph')">🕸️ Graph View</button>
</div>
<!-- Graph Container -->
<div id="network-container"></div>
<div class="graph-info" id="graph-info" style="display: none;">
Click and drag to explore • Zoom with scroll • Click nodes to view details
</div>
<!-- List View -->
<div id="list-view">
<!-- Start Here -->
<section>
<h2>🎯 Start Here</h2>
<div class="resource-list">
<div class="resource-item">
<a href="https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi" target="_blank" rel="noopener">
Neural Network → LLM Series (3Blue1Brown)
</a>
</div>
</div>
</section>
<!-- Survey Papers -->
<section>
<h2>📚 Survey Papers</h2>
<div class="paper-list">
<div class="paper-card">
<div class="paper-checkbox" role="checkbox" aria-checked="false" aria-label="Mark as read"></div>
<div class="paper-content">
<div class="paper-title">
<a href="https://arxiv.org/pdf/2402.06196v2" target="_blank" rel="noopener">LLM Survey</a>
</div>
<div class="paper-description">Comprehensive overview of large language models</div>
<div class="paper-year">2024</div>
</div>
</div>
<div class="paper-card">
<div class="paper-checkbox" role="checkbox" aria-checked="false" aria-label="Mark as read"></div>
<div class="paper-content">
<div class="paper-title">
<a href="https://arxiv.org/pdf/2308.11432" target="_blank" rel="noopener">Agent Survey</a>
</div>
<div class="paper-description">Survey of LLM-based agents</div>
<div class="paper-year">2023</div>
</div>
</div>
<div class="paper-card">
<div class="paper-checkbox" role="checkbox" aria-checked="false" aria-label="Mark as read"></div>
<div class="paper-content">
<div class="paper-title">
<a href="https://arxiv.org/pdf/2406.06608" target="_blank" rel="noopener">Prompt Engineering Survey</a>
</div>
<div class="paper-description">Comprehensive guide to prompt engineering techniques</div>
<div class="paper-year">2024</div>
</div>
</div>
</div>
</section>
<!-- Foundational Modelling -->
<section>
<h2>🏗️ Foundational Modelling</h2>
<div class="paper-list">
<div class="paper-card">
<div class="paper-checkbox" role="checkbox" aria-checked="false" aria-label="Mark as read"></div>
<div class="paper-content">
<div class="paper-title">
<a href="https://arxiv.org/pdf/1706.03762" target="_blank" rel="noopener">Attention Is All You Need <span class="priority">*</span></a>
</div>
<div class="paper-description">The original transformer architecture paper</div>
<div class="paper-year">2017</div>
</div>
</div>
<div class="paper-card">
<div class="paper-checkbox" role="checkbox" aria-checked="false" aria-label="Mark as read"></div>
<div class="paper-content">
<div class="paper-title">
<a href="https://arxiv.org/pdf/2001.08361" target="_blank" rel="noopener">Scaling Laws for Neural Language Models</a>
</div>
<div class="paper-description">Understanding how model performance scales with size</div>
<div class="paper-year">2020</div>
</div>
</div>
<div class="paper-card">
<div class="paper-checkbox" role="checkbox" aria-checked="false" aria-label="Mark as read"></div>
<div class="paper-content">
<div class="paper-title">
<a href="https://arxiv.org/pdf/2005.14165" target="_blank" rel="noopener">GPT-3 <span class="priority">*</span></a>
</div>
<div class="paper-description">Language models are few-shot learners</div>
<div class="paper-year">2020</div>
</div>
</div>
<div class="paper-card">
<div class="paper-checkbox" role="checkbox" aria-checked="false" aria-label="Mark as read"></div>
<div class="paper-content">
<div class="paper-title">
<a href="https://arxiv.org/abs/2106.09685" target="_blank" rel="noopener">LoRA: Low-Rank Adaptation of Large Language Models</a>
</div>
<div class="paper-description">Efficient fine-tuning technique using low-rank decomposition</div>
<div class="paper-year">2021</div>
</div>
</div>
<div class="paper-card">
<div class="paper-checkbox" role="checkbox" aria-checked="false" aria-label="Mark as read"></div>
<div class="paper-content">
<div class="paper-title">
<a href="https://arxiv.org/pdf/2203.15556" target="_blank" rel="noopener">Training Compute-Optimal Large Language Models</a>
</div>
<div class="paper-description">Chinchilla paper on optimal model and data scaling</div>
<div class="paper-year">2022</div>
</div>
</div>
<div class="paper-card">
<div class="paper-checkbox" role="checkbox" aria-checked="false" aria-label="Mark as read"></div>
<div class="paper-content">
<div class="paper-title">
<a href="https://arxiv.org/pdf/2203.02155" target="_blank" rel="noopener">Training Language Models to Follow Instructions with Human Feedback <span class="priority">*</span></a>
</div>
<div class="paper-description">InstructGPT: RLHF for instruction following</div>
<div class="paper-year">2022</div>
</div>
</div>
<div class="paper-card">
<div class="paper-checkbox" role="checkbox" aria-checked="false" aria-label="Mark as read"></div>
<div class="paper-content">
<div class="paper-title">
<a href="https://arxiv.org/pdf/2305.18290" target="_blank" rel="noopener">Direct Preference Optimization</a>
</div>
<div class="paper-description">Simpler alternative to RLHF for alignment</div>
<div class="paper-year">2023</div>
</div>
</div>
<div class="paper-card">
<div class="paper-checkbox" role="checkbox" aria-checked="false" aria-label="Mark as read"></div>
<div class="paper-content">
<div class="paper-title">
<a href="https://arxiv.org/pdf/2306.05685" target="_blank" rel="noopener">Judging LLM-as-a-Judge</a>
</div>
<div class="paper-description">Using LLMs to evaluate other LLMs</div>
<div class="paper-year">2023</div>
</div>
</div>
<div class="paper-card">
<div class="paper-checkbox" role="checkbox" aria-checked="false" aria-label="Mark as read"></div>
<div class="paper-content">
<div class="paper-title">
<a href="https://arxiv.org/pdf/2401.04088" target="_blank" rel="noopener">Mixtral of Experts</a>
</div>
<div class="paper-description">Sparse mixture of experts architecture</div>
<div class="paper-year">2024</div>
</div>
</div>
</div>
</section>
<!-- Planning/Reasoning -->
<section>
<h2>🧠 Planning & Reasoning</h2>
<div class="paper-list">
<div class="paper-card">
<div class="paper-checkbox" role="checkbox" aria-checked="false" aria-label="Mark as read"></div>
<div class="paper-content">
<div class="paper-title">
<a href="https://arxiv.org/pdf/1712.01815" target="_blank" rel="noopener">Mastering Chess and Shogi by Self-Play (AlphaZero)</a>
</div>
<div class="paper-description">General game-playing through self-play reinforcement learning</div>
<div class="paper-year">2017</div>
</div>
</div>
<div class="paper-card">
<div class="paper-checkbox" role="checkbox" aria-checked="false" aria-label="Mark as read"></div>
<div class="paper-content">
<div class="paper-title">
<a href="https://arxiv.org/pdf/1911.08265" target="_blank" rel="noopener">Mastering Atari, Go, Chess and Shogi (MuZero) <span class="priority">*</span></a>
</div>
<div class="paper-description">Learning without knowing the rules</div>
<div class="paper-year">2019</div>
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<a href="https://arxiv.org/pdf/2201.11903" target="_blank" rel="noopener">Chain-of-Thought Prompting <span class="priority">*</span></a>
</div>
<div class="paper-description">Eliciting reasoning in large language models</div>
<div class="paper-year">2022</div>
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<a href="https://arxiv.org/pdf/2210.03629" target="_blank" rel="noopener">ReAct: Synergizing Reasoning and Acting</a>
</div>
<div class="paper-description">Combining reasoning traces with task-specific actions</div>
<div class="paper-year">2022</div>
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<a href="https://arxiv.org/pdf/2305.10601" target="_blank" rel="noopener">Tree of Thoughts</a>
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<div class="paper-description">Deliberate problem solving with language models</div>
<div class="paper-year">2023</div>
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<a href="https://arxiv.org/pdf/2308.09687" target="_blank" rel="noopener">Graph of Thoughts</a>
</div>
<div class="paper-description">Advanced reasoning with graph structures</div>
<div class="paper-year">2023</div>
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<a href="https://arxiv.org/pdf/2305.20050" target="_blank" rel="noopener">Let's Verify Step by Step</a>
</div>
<div class="paper-description">Outcome vs. process supervision for reasoning</div>
<div class="paper-year">2023</div>
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<a href="https://arxiv.org/pdf/2405.04388" target="_blank" rel="noopener">Meta Chain-of-Thought</a>
</div>
<div class="paper-description">Learning to improve reasoning chains</div>
<div class="paper-year">2024</div>
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<a href="https://arxiv.org/pdf/2412.04604" target="_blank" rel="noopener">ARC Prize <span class="priority">*</span></a>
</div>
<div class="paper-description">Progress towards general intelligence through reasoning</div>
<div class="paper-year">2024</div>
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<a href="https://arxiv.org/pdf/2501.12948v1" target="_blank" rel="noopener">DeepSeek-R1 <span class="priority">*</span></a>
</div>
<div class="paper-description">Incentivizing reasoning capability in LLMs</div>
<div class="paper-year">2025</div>
</div>
</div>
</div>
</section>
<!-- Applications -->
<section>
<h2>🚀 Applications</h2>
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<a href="https://arxiv.org/pdf/2302.04761" target="_blank" rel="noopener">Toolformer</a>
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<div class="paper-description">Language models can teach themselves to use tools</div>
<div class="paper-year">2023</div>
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<a href="https://arxiv.org/pdf/2303.08774" target="_blank" rel="noopener">GPT-4 Technical Report</a>
</div>
<div class="paper-description">Multimodal large-scale language model</div>
<div class="paper-year">2023</div>
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<a href="https://arxiv.org/pdf/2407.21783" target="_blank" rel="noopener">The Llama 3 Herd of Models <span class="priority">*</span></a>
</div>
<div class="paper-description">Open source multilingual language models</div>
<div class="paper-year">2024</div>
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<a href="https://arxiv.org/pdf/2403.05530" target="_blank" rel="noopener">Gemini 1.5</a>
</div>
<div class="paper-description">Long-context understanding and reasoning</div>
<div class="paper-year">2024</div>
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<a href="https://github.com/deepseek-ai/DeepSeek-V3/blob/main/DeepSeek_V3.pdf" target="_blank" rel="noopener">DeepSeek-V3</a>
</div>
<div class="paper-description">Cost-efficient mixture of experts model</div>
<div class="paper-year">2024</div>
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<a href="https://arxiv.org/pdf/2405.15793" target="_blank" rel="noopener">SWE-Agent</a>
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<div class="paper-description">Agent-computer interface for automated software engineering</div>
<div class="paper-year">2024</div>
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<a href="https://arxiv.org/pdf/2407.16741" target="_blank" rel="noopener">OpenHands</a>
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<div class="paper-description">Open platform for software development agents</div>
<div class="paper-year">2024</div>
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</div>
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</section>
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<section>
<h2>📊 Benchmarks</h2>
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<a href="https://arxiv.org/pdf/2206.04615" target="_blank" rel="noopener">Beyond the Imitation Game (BIG-Bench)</a>
</div>
<div class="paper-description">Diverse evaluation tasks for language models</div>
<div class="paper-year">2022</div>
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<a href="https://arxiv.org/pdf/2310.06770" target="_blank" rel="noopener">SWE-bench</a>
</div>
<div class="paper-description">Evaluating language models on real-world software issues</div>
<div class="paper-year">2023</div>
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<a href="https://arxiv.org/pdf/2403.04132" target="_blank" rel="noopener">Chatbot Arena</a>
</div>
<div class="paper-description">Crowdsourced benchmarking platform for LLMs</div>
<div class="paper-year">2024</div>
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</div>
</section>
<!-- Videos/Lectures -->
<section>
<h2>🎥 Videos & Lectures</h2>
<div class="resource-list">
<div class="resource-item">
<a href="https://www.youtube.com/@3blue1brown" target="_blank" rel="noopener">3Blue1Brown</a> - Visual mathematics and deep learning explanations
</div>
<div class="resource-item">
<a href="https://www.amazon.com/Build-Large-Language-Model-Scratch/dp/1633437167" target="_blank" rel="noopener">Build a Large Language Model From Scratch</a> - Comprehensive book guide
</div>
<div class="resource-item">
<a href="https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ" target="_blank" rel="noopener">Andrej Karpathy: Neural Networks: Zero to Hero</a> - Hands-on tutorial series
</div>
<div class="resource-item">
<a href="https://www.youtube.com/@YannicKilcher" target="_blank" rel="noopener">Yannic Kilcher</a> - Paper reviews and ML news
</div>
<div class="resource-item">
<a href="https://www.youtube.com/watch?v=eaAonE58sLU" target="_blank" rel="noopener">Noam Brown on Planning</a> - Expert insights on AI planning
</div>
<div class="resource-item">
<a href="https://www.youtube.com/watch?v=9vM4p9NN0Ts" target="_blank" rel="noopener">Stanford CS324: Advances in Foundation Models</a> - Building LLMs course
</div>
<div class="resource-item">
<a href="https://arxiv.org/pdf/2501.09223" target="_blank" rel="noopener">Foundations of LLMs</a> - Comprehensive tutorial paper
</div>
<div class="resource-item">
<a href="https://www.latent.space/p/not-old" target="_blank" rel="noopener">Why You're Not Too Old to Get Into AI</a> - Motivational perspective
</div>
</div>
</section>
<!-- Helpful Websites -->
<section>
<h2>🌐 Helpful Websites</h2>
<div class="resource-list">
<div class="resource-item">
<a href="https://github.com/adam-maj/deep-learning?tab=readme-ov-file" target="_blank" rel="noopener">History of Deep Learning</a> - Timeline and key developments
</div>
<div class="resource-item">
<a href="https://fullstackdeeplearning.com/" target="_blank" rel="noopener">Full Stack Deep Learning</a> - Production ML resources
</div>
<div class="resource-item">
<a href="https://www.promptingguide.ai/" target="_blank" rel="noopener">Prompting Guide</a> - Comprehensive prompt engineering guide
</div>
<div class="resource-item">
<a href="https://a16z.com/ai-canon/" target="_blank" rel="noopener">a16z AI Cannon</a> - Curated list of AI resources
</div>
<div class="resource-item">
<a href="https://www.latent.space/p/2025-papers" target="_blank" rel="noopener">2025 AI Engineer Reading List</a> - Latest papers and trends
</div>
<div class="resource-item">
<a href="https://nrehiew.github.io/blog/2024/" target="_blank" rel="noopener">State of Generative Models 2024</a> - Year in review
</div>
</div>
</section>
<!-- Others (non LLMs) -->
<section>
<h2>🎨 Beyond LLMs</h2>
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<a href="https://arxiv.org/pdf/2010.11929" target="_blank" rel="noopener">Vision Transformer (ViT)</a>
</div>
<div class="paper-description">An image is worth 16x16 words - Transformers for image classification</div>
<div class="paper-year">2021</div>
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<a href="https://arxiv.org/pdf/2112.10752" target="_blank" rel="noopener">High-Resolution Image Synthesis with Latent Diffusion</a>
</div>
<div class="paper-description">Stable Diffusion - Efficient image generation in latent space</div>
<div class="paper-year">2021</div>
</div>
</div>
</div>
</section>
<!-- Ilya Sutskever's Top 30 Papers -->
<section>
<h2>🎯 Ilya Sutskever's Top 30 Papers</h2>
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<a href="https://scottaaronson.blog/?p=762" target="_blank" rel="noopener">The First Law of Complexodynamics</a>
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<div class="paper-description">Scott Aaronson's insights on complexity theory</div>
<div class="paper-year">2011</div>
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<a href="https://karpathy.github.io/2015/05/21/rnn-effectiveness/" target="_blank" rel="noopener">The Unreasonable Effectiveness of Recurrent Neural Networks</a>
</div>
<div class="paper-description">Andrej Karpathy's exploration of RNN capabilities</div>
<div class="paper-year">2015</div>
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<a href="https://colah.github.io/posts/2015-08-Understanding-LSTMs/" target="_blank" rel="noopener">Understanding LSTM Networks</a>
</div>
<div class="paper-description">Christopher Olah's visual guide to LSTMs</div>
<div class="paper-year">2015</div>
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<div class="paper-title">
<a href="https://arxiv.org/pdf/1409.2329" target="_blank" rel="noopener">Recurrent Neural Network Regularization</a>
</div>
<div class="paper-description">Wojciech Zaremba et al. on improving RNN training</div>
<div class="paper-year">2014</div>
</div>
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<a href="https://www.cs.toronto.edu/~hinton/absps/colt93.pdf" target="_blank" rel="noopener">Keeping Neural Networks Simple by Minimizing the Description Length of the Weights</a>
</div>
<div class="paper-description">Geoffrey Hinton & Drew van Camp on MDL principle</div>
<div class="paper-year">1993</div>
</div>
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<a href="https://arxiv.org/pdf/1506.03134" target="_blank" rel="noopener">Pointer Networks</a>
</div>
<div class="paper-description">Oriol Vinyals et al. on attention-based architectures</div>
<div class="paper-year">2015</div>
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<a href="https://papers.nips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf" target="_blank" rel="noopener">ImageNet Classification with Deep Convolutional Neural Networks <span class="priority">*</span></a>
</div>
<div class="paper-description">AlexNet: The breakthrough in deep learning (Alex Krizhevsky et al.)</div>
<div class="paper-year">2012</div>
</div>
</div>
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<a href="https://arxiv.org/pdf/1511.06391" target="_blank" rel="noopener">Order Matters: Sequence to Sequence for Sets</a>
</div>
<div class="paper-description">Oriol Vinyals et al. on set-to-sequence problems</div>
<div class="paper-year">2015</div>
</div>
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<a href="https://arxiv.org/pdf/1811.06965" target="_blank" rel="noopener">GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism</a>
</div>
<div class="paper-description">Yanping Huang et al. on efficient training of large models</div>
<div class="paper-year">2018</div>
</div>
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<div class="paper-title">
<a href="https://arxiv.org/pdf/1512.03385" target="_blank" rel="noopener">Deep Residual Learning for Image Recognition <span class="priority">*</span></a>
</div>
<div class="paper-description">ResNet: Revolutionary skip connections (Kaiming He et al.)</div>
<div class="paper-year">2015</div>
</div>
</div>
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<a href="https://arxiv.org/pdf/1511.07122" target="_blank" rel="noopener">Multi-Scale Context Aggregation by Dilated Convolutions</a>
</div>
<div class="paper-description">Fisher Yu & Vladlen Koltun on dilated convolutions</div>
<div class="paper-year">2015</div>
</div>
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<a href="https://arxiv.org/pdf/1704.01212" target="_blank" rel="noopener">Neural Message Passing for Quantum Chemistry</a>
</div>
<div class="paper-description">Justin Gilmer et al. on graph neural networks</div>
<div class="paper-year">2017</div>
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<div class="paper-title">
<a href="https://arxiv.org/pdf/1706.03762" target="_blank" rel="noopener">Attention Is All You Need <span class="priority">*</span></a>
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<div class="paper-description">The foundational transformer architecture</div>
<div class="paper-year">2017</div>
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</div>
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<a href="https://arxiv.org/pdf/1409.0473" target="_blank" rel="noopener">Neural Machine Translation by Jointly Learning to Align and Translate</a>
</div>
<div class="paper-description">Bahdanau et al. introducing attention mechanism</div>
<div class="paper-year">2014</div>
</div>
</div>
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<div class="paper-title">
<a href="https://arxiv.org/pdf/1603.05027" target="_blank" rel="noopener">Identity Mappings in Deep Residual Networks</a>
</div>
<div class="paper-description">Kaiming He et al. improving ResNet design</div>
<div class="paper-year">2016</div>
</div>
</div>
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<a href="https://arxiv.org/pdf/1706.01427" target="_blank" rel="noopener">A Simple Neural Network Module for Relational Reasoning</a>
</div>
<div class="paper-description">Adam Santoro et al. on relation networks</div>
<div class="paper-year">2017</div>
</div>
</div>
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<a href="https://arxiv.org/pdf/1611.02731" target="_blank" rel="noopener">Variational Lossy Autoencoder</a>
</div>
<div class="paper-description">Xi Chen et al. on VAEs with powerful decoders</div>
<div class="paper-year">2016</div>
</div>
</div>
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<a href="https://arxiv.org/pdf/1806.01822" target="_blank" rel="noopener">Relational Recurrent Neural Networks</a>
</div>
<div class="paper-description">Adam Santoro et al. on memory and relational reasoning</div>
<div class="paper-year">2018</div>
</div>
</div>
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<a href="https://arxiv.org/pdf/1405.6903" target="_blank" rel="noopener">Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton</a>
</div>
<div class="paper-description">Scott Aaronson et al. on entropy and complexity</div>
<div class="paper-year">2014</div>
</div>
</div>
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<div class="paper-title">
<a href="https://arxiv.org/pdf/1410.5401" target="_blank" rel="noopener">Neural Turing Machines <span class="priority">*</span></a>
</div>