Discovering outliers or anomalies in data can be a powerful capability for a wide range of applications. The choice of algorithms depends on what type of data do we have and what kind of task w… in stat.ML | cs.CR | cs.CV | cs.LG, latest revision 8/12/2019 When you just don’t have enough labeled data, semi-supervised learning can come to the rescue. From picking up on fraudulent activity on your credit card to finding a networked computer sputtering about before it takes down the rest of the system, flagging unexpected rare events within a data set can significantly reduce the time required for humans to sift through mountains of logs or apparently unconnected data to get to the root cause of a problem. In recent years, researchers have developed and applied new machine learning technologies. in cs.LG | cs.AI | cs.CL | cs.CV | stat.ML, latest revision 9/30/2019 UPDATE: We’ve also summarized the top 2020 AI & machine learning research papers. As adversarial attacks that exploit these inconceivable patterns have gained significant attention over the past year, there may be opportunities for developers to harness these features instead, so they won’t lose control of their AI. View Machine Learning Research Papers on Academia.edu for free. 4 Awesome COVID Machine Learning Projects, Machine Learning for Humans, Part 4: Neural Networks & Deep Learning, 5 Awesome Projects to Hone Your Deep Learning Skills, Machine Learning in Agriculture: Applications and Techniques, Textfeatures: Library for extracting basic features from text data, The differences between Data Science, Artificial Intelligence, Machine Learning, and Deep Learning, Distinguishing between Narrow AI, General AI and Super AI. The goal of many research papers presented over the last year was to improve the system’s ability to understand complex relationships introduced during the conversation by better leveraging the conversation history and context. Tan, Mingxing and Le, Quoc in cs.LG, cs.CV and stat.ML, latest revision 11/23/2019 I am currently in my undergraduate final year. Machine learning has attracted increasing interest in medical image computing and computer-assisted intervention, and plays an important role in image-based computer-aided diagnosis in digital pathology. So, it should sound reasonable that predictions for the next important movements in AI and machine learning should be based on collectible data. Convolutional Neural Networks (CNNs or ConvNets) are used primarily to process visual data through multiple layers of learnable filters that collectively iterate through the entire field of an input image. Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world where groundbreaking work is published. Not only is data coming in faster and at higher volumes, but it is also coming in messier. Chalapathy, R. and Chawla, S. in cs.LG | stat.ML, latest revision 1/23/2019 Computer Vision. A great feature of transformers is that they do not have to process the sequential information in order, as would a Recurrent Neural Network (RNN). While incomprehensible to humans, these exist as natural features that are fundamentally used by supervised learning algorithms. Predictions tend to be based on the best guesses or gut reactions from practitioners and subject matter experts in the field. Neural Networks. 5. Comparison of a 2-D vs. Graph convolution network. Main 2020 Developments and Key 2021 Trends in AI, Data Science... AI registers: finally, a tool to increase transparency in AI/ML. Reward(R) — A type of feedback through which the success and failure of user’s actions are measured. This approach is a new novel neural architecture that expands transformers to handle longer text lengths (hence, the “XL” for “extra long”). Wu, Zonghan, et al. 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