Google AI 推出了测试时扩散深度研究员 (TTD-DR)，这是一个模仿人类研究过程的新颖框架，通过迭代起草和修改报告来利用检索到的信息。该方法将报告撰写建模为一个扩散过程，通过搜索驱动的去噪机制来完善初稿。OpenAI 还发表了几篇论文，详细介绍了训练大型神经网络的技术，包括数据、流水线和张量并行，以及探索由于浮点运算导致的深度线性网络的非线性计算特性。此外，OpenAI 还讨论了深度学习的基础设施考虑因素以及一种称为权重归一化的重新参数化技术，以加速训练。 AI
排序理由 此集群包含详细介绍新人工智能技术和基础设施的研究论文和博客文章，而不是前沿模型发布或重大行业新闻。
AI 生成摘要 · Google Gemini · 来自 20 个来源。 我们如何撰写摘要 →
Machine Intelligence
Large neural networks are at the core of many recent advances in AI, but training them is a difficult engineering and research challenge which requires orchestrating a cluster of GPUs to perform a single synchronized calculation.
Deep learning is an empirical science, and the quality of a group’s infrastructure is a multiplier on progress. Fortunately, today’s open-source ecosystem makes it possible for anyone to build great deep learning infrastructure.
<!-- This post is a summary of Prof Naftali Tishby's recent talk on "Information Theory in Deep Learning". It presented how to apply the information theory to study the growth and transformation of deep neural networks during training. --> <p><span class="update">Professor Naftal…
<!-- Starting earlier this year, I grew a strong curiosity of deep learning and spent some time reading about this field. To document what I’ve learned and to provide some interesting pointers to people with similar interests, I wrote this overview of deep learning models and the…
Stanford Winter Quarter 2016 class: CS231n: Convolutional Neural Networks for Visual Recognition. Lecture 12. Get in touch on Twitter @cs231n, or on Reddit /r/cs231n. Our course website is http://cs231n.stanford.edu/
Stanford Winter Quarter 2016 class: CS231n: Convolutional Neural Networks for Visual Recognition. Lecture 7. Get in touch on Twitter @cs231n, or on Reddit /r/cs231n.
In this paper, we make the case that a scientific theory of deep learning is emerging. By this we mean a theory which characterizes important properties and statistics of the training process, hidden representations, final weights, and performance of neural networks. We pull toge…
In this paper, we make the case that a scientific theory of deep learning is emerging. By this we mean a theory which characterizes important properties and statistics of the training process, hidden representations, final weights, and performance of neural networks. We pull toge…
Deep learning (DL) has become a cornerstone of modern machine learning (ML) praxis. We introduce the R package mlr3torch, which is an extensible DL framework for the mlr3 ecosystem. It is built upon the torch package, and simplifies the definition, training, and evaluation of neu…
We often think of Large Language Models (LLMs) as all-knowing, but as the team reveals, they still struggle with the logic of a second-grader. Why can’t ChatGPT reliably add large numbers? Why does it "hallucinate" the laws of physics? The answer lies in the architecture. This ep…
<p>Chris and Daniel sit down to chat about some exciting new AI developments including wav2vec-u (an unsupervised speech recognition model) and meta-learning (a new book about “How To Learn Deep Learning And Thrive In The Digital World”). Along the way they discuss engineering sk…
<p>In anticipation of the upcoming NVIDIA GPU Technology Conference (GTC), Will Ramey joins Daniel and Chris to talk about education for artificial intelligence practitioners, and specifically the role that the NVIDIA Deep Learning Institute plays in the industry. Will’s insights…
<p><span style="font-weight: 400;">Yann LeCun is one of the fathers of deep learning, the recent revolution in AI that has captivated the world with the possibility of what machines can learn from data. He is a professor at New York University, a Vice President & Chief AI Sci…
<p><span style="font-weight: 400;">Jeremy Howard is the founder of fast.ai, a research institute dedicated to make deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, a former president of Kaggle as well a top-ranking c…
<p>Yoshua Bengio, along with Geoffrey Hinton and Yann Lecun, is considered one of the three people most responsible for the advancement of deep learning during the 1990s, 2000s, and now. Cited 139,000 times, he has been integral to some of the biggest breakthroughs in AI over the…
<!-- SC_OFF --><div class="md"><p>Hi, all! I'm the lead author on this ambitious (14-author!) perspective paper on deep learning theory. We've all been working seriously, and more or less exclusively, on deep learning for many years now. We believe that a theory is emerging, and …