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16,653 stories from 2786 sources · 1280 in the last 24 hours · refreshed hourly. Every story links to the original publisher.

Today's AI news from trusted publishers and official AI lab blogs, refreshed about every 30 minutes.

News today: 1,935Last updated:
  1. Domain Randomization for Sim2Real TransferLil'Log · research · blogIn Robotics, one of the hardest problems is how to make your model transfer to the real world. Due to the sample inefficiency of deep RL algorithms and the cost of data collection on real robots, we often need to train models in a simulator which theoretically provides an infinit
  2. Are Deep Neural Networks Dramatically Overfitted?Lil'Log · research · blog[Updated on 2019-05-27: add the section on Lottery Ticket Hypothesis.] If you are like me, entering into the field of deep learning with experience in traditional machine learning, you may often ponder over this question: Since a typical deep neural network has so many parameters
  3. Generalized Language ModelsLil'Log · research · blog[Updated on 2019-02-14: add ULMFiT and GPT-2 .] [Updated on 2020-02-29: add ALBERT .] [Updated on 2020-10-25: add RoBERTa .] [Updated on 2020-12-13: add T5 .] [Updated on 2020-12-30: add GPT-3 .] [Updated on 2021-11-13: add XLNet , BART and ELECTRA ; Also updated the Summary sect
  4. Object Detection Part 4: Fast Detection ModelsLil'Log · research · blogIn Part 3 , we have reviewed models in the R-CNN family. All of them are region-based object detection algorithms. They can achieve high accuracy but could be too slow for certain applications such as autonomous driving. In Part 4, we only focus on fast object detection models, i
  5. Meta-Learning: Learning to Learn FastLil'Log · research · blog[Updated on 2019-10-01: thanks to Tianhao, we have this post translated in Chinese !]
  6. Flow-based Deep Generative ModelsLil'Log · research · blogSo far, I ve written about two types of generative models, GAN and VAE . Neither of them explicitly learns the probability density function of real data, $p(\mathbf{x})$ (where $\mathbf{x} \in \mathcal{D}$) because it is really hard! Taking the generative model with latent variab
  7. From Autoencoder to Beta-VAELil'Log · research · blog[Updated on 2019-07-18: add a section on VQ-VAE VQ-VAE-2 .] [Updated on 2019-07-26: add a section on TD-VAE .] Autocoder is invented to reconstruct high-dimensional data using a neural network model with a narrow bottleneck layer in the middle (oops, this is probably not true for
  8. Attention? Attention!Lil'Log · research · blog[Updated on 2018-10-28: Add Pointer Network and the link to my implementation of Transformer.] [Updated on 2018-11-06: Add a link to the implementation of Transformer model.] [Updated on 2018-11-18: Add Neural Turing Machines .] [Updated on 2019-07-18: Correct the mistake on usin
  9. Implementing Deep Reinforcement Learning Models with Tensorflow OpenAI GymLil'Log · research · blogThe full implementation is available in lilianweng/deep-reinforcement-learning-gym In the previous two posts, I have introduced the algorithms of many deep reinforcement learning models. Now it is the time to get our hands dirty and practice how to implement the models in the wil
  10. Policy Gradient AlgorithmsLil'Log · research · blog[Updated on 2018-06-30: add two new policy gradient methods, SAC and D4PG .] [Updated on 2018-09-30: add a new policy gradient method, TD3 .] [Updated on 2019-02-09: add SAC with automatically adjusted temperature ]. [Updated on 2019-06-26: Thanks to Chanseok, we have a version o
  11. A (Long) Peek into Reinforcement LearningLil'Log · research · blog[Updated on 2020-09-03: Updated the algorithm of SARSA and Q-learning so that the difference is more pronounced. [Updated on 2021-09-19: Thanks to 爱吃猫的鱼, we have this post in Chinese ].
  12. The Multi-Armed Bandit Problem and Its SolutionsLil'Log · research · blogThe algorithms are implemented for Bernoulli bandit in lilianweng/multi-armed-bandit . Exploitation vs Exploration The exploration vs exploitation dilemma exists in many aspects of our life. Say, your favorite restaurant is right around the corner. If you go there every day, you
  13. Object Detection for Dummies Part 3: R-CNN FamilyLil'Log · research · blog[Updated on 2018-12-20: Remove YOLO here. Part 4 will cover multiple fast object detection algorithms, including YOLO.] [Updated on 2018-12-27: Add bbox regression and tricks sections for R-CNN.] In the series of Object Detection for Dummies , we started with basic concepts in im
  14. Object Detection for Dummies Part 2: CNN, DPM and OverfeatLil'Log · research · blogPart 1 of the Object Detection for Dummies series introduced: (1) the concept of image gradient vector and how HOG algorithm summarizes the information across all the gradient vectors in one image; (2) how the image segmentation algorithm works to detect regions that potentially
  15. Object Detection for Dummies Part 1: Gradient Vector, HOG, and SSLil'Log · research · blogI ve never worked in the field of computer vision and has no idea how the magic could work when an autonomous car is configured to tell apart a stop sign from a pedestrian in a red hat. To motivate myself to look into the maths behind object recognition and detection algorithms,
  16. Learning Word EmbeddingLil'Log · research · blogHuman vocabulary comes in free text. In order to make a machine learning model understand and process the natural language, we need to transform the free-text words into numeric values. One of the simplest transformation approaches is to do a one-hot encoding in which each distin
  17. Anatomize Deep Learning with Information TheoryLil'Log · research · blogProfessor Naftali Tishby passed away in 2021. Hope the post can introduce his cool idea of information bottleneck to more people. Recently I watched the talk Information Theory in Deep Learning by Prof Naftali Tishby and found it very interesting. He presented how to apply the in
  18. From GAN to WGANLil'Log · research · blog[Updated on 2018-09-30: thanks to Yoonju, we have this post translated in Korean !] [Updated on 2019-04-18: this post is also available on arXiv .] Generative adversarial network (GAN) has shown great results in many generative tasks to replicate the real-world rich content such
  19. How to Explain the Prediction of a Machine Learning Model?Lil'Log · research · blogThe machine learning models have started penetrating into critical areas like health care, justice systems, and financial industry. Thus to figure out how the models make the decisions and make sure the decisioning process is aligned with the ethnic requirements or legal regulati
  20. Predict Stock Prices Using RNN: Part 2Lil'Log · research · blogIn the Part 2 tutorial, I would like to continue the topic on stock price prediction and to endow the recurrent neural network that I have built in Part 1 with the capability of responding to multiple stocks. In order to distinguish the patterns associated with different price se
  21. Predict Stock Prices Using RNN: Part 1Lil'Log · research · blogThis is a tutorial for how to build a recurrent neural network using Tensorflow to predict stock market prices. The full working code is available in github.com/lilianweng/stock-rnn . If you don t know what is recurrent neural network or LSTM cell, feel free to check my previous
  22. An Overview of Deep Learning for Curious PeopleLil'Log · research · blog(The post was originated from my talk for WiMLDS x Fintech meetup hosted by Affirm .) I believe many of you have watched or heard of the games between AlphaGo and professional Go player Lee Sedol in 2016. Lee has the highest rank of nine dan and many world championships. No doubt
  23. FAQLil'Log · research · blog
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