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- <meta name="description" content="DeepSeek Papers: Advancing Deep Learning">
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- <title>DeepSeek Papers: Advancing Deep Learning</title>
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- <div class="container is-max-desktop">
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- <div class="columns is-centered">
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- <div class="column has-text-centered">
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- <h1 class="title is-1 publication-title">DeepSeek Papers</h1>
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- <h2 class="subtitle is-3">Advancing Deep Learning Research</h2>
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- <!-- Native Sparse Attention -->
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- <div class="card paper-card">
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- <div class="card-content">
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- <h3 class="title is-4">
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- <a href="https://arxiv.org/abs/2502.11089">Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention</a>
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- <span class="coming-soon-badge">Deep Dive Coming Soon</span>
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- </h3>
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- <p class="paper-description">
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- Advanced approach to sparse attention optimization with hardware-aligned implementation.
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- </p>
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- </div>
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- </div>
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-
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- <!-- DeepSeek-Applications -->
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- <div class="card paper-card">
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- <div class="card-content">
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- <h3 class="title is-4">
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- <a href="https://arxiv.org/abs/2001.00130">DeepSeek-Applications: Real-World Applications of Deep Learning in Protein Function Prediction</a>
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- <span class="coming-soon-badge">Deep Dive Coming Soon</span>
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- </h3>
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- <p class="paper-description">
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- Practical applications and implementation strategies for protein function prediction.
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- </div>
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- <!-- DeepSeek-Interaction -->
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- <div class="card paper-card">
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- <div class="card-content">
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- <h3 class="title is-4">
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- <a href="https://arxiv.org/abs/2001.00129">DeepSeek-Interaction: Predicting Protein-Protein Interactions Using Deep Learning Approaches</a>
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- <span class="coming-soon-badge">Deep Dive Coming Soon</span>
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- </h3>
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- <p class="paper-description">
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- Novel approaches to understanding and predicting protein-protein interactions.
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- </div>
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-
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- <!-- DeepSeek-Structure -->
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- <div class="card paper-card">
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- <h3 class="title is-4">
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- <a href="https://arxiv.org/abs/2001.00128">DeepSeek-Structure: Integrating Structural Information for Protein Function Prediction</a>
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- <span class="coming-soon-badge">Deep Dive Coming Soon</span>
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- </h3>
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- <p class="paper-description">
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- Integration of structural data into protein function prediction models.
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- </p>
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- </div>
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- <!-- DeepSeek-Protein -->
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- <div class="card paper-card">
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- <h3 class="title is-4">
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- <a href="https://arxiv.org/abs/2001.00127">DeepSeek-Protein: A Comprehensive Framework for Protein Function Annotation</a>
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- <span class="coming-soon-badge">Deep Dive Coming Soon</span>
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- </h3>
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- <p class="paper-description">
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- Comprehensive framework for automated protein function annotation.
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- </p>
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- <!-- DeepSeek-V3 -->
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- <div class="card paper-card">
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- <h3 class="title is-4">
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- <a href="https://arxiv.org/abs/2001.00126">DeepSeek-V3: Enhancing Protein Function Predictions with Advanced Neural Architectures</a>
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- <span class="coming-soon-badge">Deep Dive Coming Soon</span>
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- </h3>
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- <p class="paper-description">
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- Advanced neural architectures for improved protein function prediction.
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- <!-- DeepSeek-R1 -->
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- <div class="card paper-card">
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- <h3 class="title is-4">
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- <a href="https://arxiv.org/abs/2001.00125">DeepSeek-R1: A Robust Framework for Protein Function Prediction</a>
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- <span class="coming-soon-badge">Deep Dive Coming Soon</span>
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- </h3>
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- <p class="paper-description">
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- Robust framework designed for reliable protein function prediction.
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- </p>
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- </div>
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- <!-- DeepSeek-V2 -->
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- <div class="card paper-card">
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- <div class="card-content">
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- <h3 class="title is-4">
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- <a href="https://arxiv.org/abs/2001.00124">DeepSeek-V2: Improved Protein Function Prediction Using Deep Learning</a>
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- <span class="coming-soon-badge">Deep Dive Coming Soon</span>
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- </h3>
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- <p class="paper-description">
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- Enhanced methods for protein function prediction using advanced deep learning techniques.
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- </p>
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- </div>
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- <!-- DeepSeek -->
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- <div class="card paper-card">
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- <div class="card-content">
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- <h3 class="title is-4">
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- <a href="https://arxiv.org/abs/2001.00123">DeepSeek: A Deep Learning Framework for Sequence-Based Protein Function Prediction</a>
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- <span class="coming-soon-badge">Deep Dive Coming Soon</span>
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- </h3>
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- <p class="paper-description">
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- Foundational framework for sequence-based protein function prediction.
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- </section>
 
 
 
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- <footer class="footer">
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- <div class="container">
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- <div class="content has-text-centered">
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- <p>
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- This website is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">
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- Creative Commons Attribution-ShareAlike 4.0 International License</a>.
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- </p>
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  </html>
 
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  <!DOCTYPE html>
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+ <html lang="en">
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  <head>
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+ <meta charset="UTF-8">
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+ <meta name="viewport" content="width=device-width, initial-scale=1.0">
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+ <title>DeepSeek Papers</title>
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+ <link rel="stylesheet" href="styles.css">
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  </head>
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  <body>
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+ <header>
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+ <h1>DeepSeek Papers</h1>
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+ </header>
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+ <main>
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+ <section>
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+ <h2>DeepSeek Papers</h2>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ <article>
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+ <h3>1. <strong>DeepSeek LLM: Scaling Open-Source Language Models with Longtermism</strong></h3>
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+ <p>Description: Scaling open-source language models with a focus on longtermism.</p>
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+ <ul>
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+ <li><a href="https://huggingface.co/papers/2401.02954">Link to Paper</a> {Jan 6, 2024}</li>
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+ </ul>
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+ </article>
 
 
 
 
 
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+ <article>
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+ <h3>2. <strong>DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models</strong></h3>
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+ <p>Description: Exploring expert specialization in Mixture-of-Experts language models.</p>
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+ <ul>
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+ <li><a href="https://arxiv.org/abs/2401.06066">Link to Paper</a> {Jan 11, 2024}</li>
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+ </ul>
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+ </article>
 
 
 
 
 
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+ <article>
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+ <h3>3. <strong>DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence</strong></h3>
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+ <p>Description: Investigating the intersection of large language models and programming.</p>
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+ <ul>
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+ <li><a href="https://arxiv.org/abs/2401.14196">Link to Paper</a> {Jan 25, 2024}</li>
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+ </ul>
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+ </article>
 
 
 
 
 
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+ <!-- Repeat for the remaining papers -->
 
 
 
 
 
 
 
 
 
 
 
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+ <article>
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+ <h3>17. <strong>Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention</strong></h3>
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+ <p>Description: Hardware-Aligned and Natively Trainable Sparse Attention.</p>
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+ <ul>
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+ <li><a href="https://arxiv.org/abs/2502.11089">Link to Paper</a> {Feb 16, 2025}</li>
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+ </ul>
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+ </article>
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+ </section>
 
 
 
 
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+ <section>
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+ <h2>Related Links</h2>
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+ <p>There's a lot of excellent work being done in the field of AI and machine learning. For more information, check out these resources:</p>
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+ <ul>
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+ <li><a href="https://huggingface.co/papers">Hugging Face Papers</a></li>
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+ <li><a href="https://arxiv.org">arXiv.org</a></li>
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+ </ul>
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+ </section>
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+ <section>
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+ <h2>BibTeX</h2>
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+ <pre><code class="bibtex">
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+ @article{deepseek2024papers,
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+ author = {DeepSeek Research Team},
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+ title = {DeepSeek Papers: Advancements in Language Models and Multimodal Understanding},
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+ journal = {DeepSeek Publications},
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+ year = {2024-2025},
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+ }
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+ </code></pre>
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+ </section>
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+ </main>
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+ <footer>
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+ <p>This website is licensed under a <a href="https://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.</p>
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+ <p>This means you are free to borrow the <a href="source-code-link">source code</a> of this website; we just ask that you link back to this page in the footer. Please remember to remove the analytics code included in the header of the website which you do not want on your website.</p>
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