machine learning research topics

In this Project, you will analyze a large collection of NIPS research papers from the past decade to discover the latest trends in machine learning. Artificial Intelligence in Modern Learning System : E-Learning. 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). All published papers are freely available online. 1) A Comprehensive Survey on Graph Neural Networks This work develops a new scaling approach that uniformly extends the depth, width, and resolution in one fell swoop into a family of models that seem to achieve better accuracy and efficiency. Although, some recent topics of interest in Machine Learning research are: Reinforcement Learning, Deep Learning, Autonomous Driving, Application of Machine Learning to IoT Data etc. We discussed the basic terms such as AI, machine learning and deep learning, different types of machine learning: supervised and unsupervised learning, some machine learning algorithms such as linear regression, logistic regression, k-nn, and random forest, and performance evaluation matrices for different algorithms. UPDATE: We’ve also summarized the top 2020 AI & machine learning research papers. These new technologies have driven many new application domains. The Machine Learning research group is part of the DTAI section which is part of the Department of Computer Science at the KU Leuven.It is led by Hendrik Blockeel, Jesse Davis and Luc De Raedt and counts about 12 post-docs and 30 PhD students representing virtually all areas of machine learning and data mining. The trending research topics in reinforcement learning include: Multi-agent reinforcement learning (MARL) is rapidly advancing. When you just don’t have enough labeled data, semi-supervised learning can come to the rescue. Reward(R) — A type of feedback through which the success and failure of user’s actions are measured. While the intention of this feature on the site is not to predict the future, this simple snapshot that could represent what machine learning researchers are apparently learning about at the turn of the year might be an interesting indicator for what will come next in the field. in cs.LG | cs.CL | stat.ML, latest revision 6/2/2019 We’ve seen many predictions for what new advances are expected in the field of AI and machine learning. Data Science, and Machine Learning. Research Topics of Machine Learning Group Deep Learning We develop and evaluate novel deep architectures for a variety of complex realworld tasks such as image classification, vision-based force estimation, sentiment analysis, visual question answering, image quality assessment, time series analysis and face morphing detection. Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. 1904.12848v4: Abstract – Full Paper (pdf). More than 50 million people use GitHub to discover, fork, and contribute to over 100 million projects. Variational autoencoders (VAE) can help with this by incorporating an encoded vector of the target that can seed the generation of new, similar information. Wu, Zonghan, et al. The authors provide a thorough overview of variational autoencoders to provide you a strong foundation and reference to leverage VAEs into your work. And this advancement in Machine Learning technologies is only increasing with each year as top companies like Google, Apple, Facebook, Amazon, Microsoft, etc. 1906.02691v3: Abstract – Full Paper (pdf). Accelerating Chip Design with Machine Learning Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification UFO2: A Unified Framework towards Omni-supervised Object Detection They develop an alternate lightweight convolution approach that is competitive to previous approaches as well as a dynamic convolution that is even more simple and efficient. If you are reading this article, you are already surrounded by AI-powered tech more than you can imagine. Now that we are well underway into 2020, many predictions already exist for what the top research tracks and greatest new ideas may emerge in the next decade. Implementing the AdaBoost Algorithm From Scratch, Data Compression via Dimensionality Reduction: 3 Main Methods, A Journey from Software to Machine Learning Engineer. Many real-world data sets can be better described through connections on a graph, and interest is increasing for extending deep learning techniques to graph data (image from Wu, Z., et al., 2019 [1]). Ph.D.s choose research topics that establish new and creative paths toward discovery in their field of study. The choice of algorithms depends on what type of data do we have and what kind of task w… So, it should sound reasonable that predictions for the next important movements in AI and machine learning should be based on collectible data. You might not find direct answers to your question but a way to go about it. Predictive learning, which is about modeling the world and making predictions about some future outcomes. With machine learning-themed papers continuing to churn out at a rapid clip from researchers around the world, monitoring those papers that capture the most attention from the research community seems like an interesting source of predictive data. Machine Learning Algorithms With generative adversarial networks (GANs) being all the rage these past few years, they can offer the limitation that it is difficult to make sure the network creates something that you are interested in based on initial conditions. Healthcare wearables, remote monitoring, telemedicine, robotic surgery, etc., are all possible because of machine learning algorithms powered by AI. Great successes have been seen by applying CNNs to image or facial recognition, and the approach has been further considered in natural language processing, drug discovery, and even gameplay. are heavily investing in research and development for Machine Learning and its myriad offshoots. Research Methodology: Machine learning and Deep Learning techniques are discussed which works as a catalyst to improve the performance of any health monitor system such supervised machine learning algorithms, unsupervised machine learning algorithms, auto-encoder, convolutional neural network and restricted boltzmann machine. The topics discussed above were the basics of machine learning. One approach is to make a good guess based on some foundational assumption as to what labels would be for the unlabeled sources, and then it can pull these generated data into a traditional learning model. Machine learning (ML) is the study of computer algorithms that improve automatically through experience. Yang, Z., et al. The survey also summarized open source codes, benchmark datasets, and model evaluations to help you start to untangle this exciting new approach in machine learning. 1901.03407v2: Abstract – Full Paper (pdf). Chalapathy, R. and Chawla, S. in cs.LG | stat.ML, latest revision 1/23/2019 It is seen as a subset of artificial intelligence.Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. I am currently in my undergraduate final year. The Ultimate Guide to Data Engineer Interviews, Change the Background of Any Video with 5 Lines of Code, Get KDnuggets, a leading newsletter on AI, While incomprehensible to humans, these exist as natural features that are fundamentally used by supervised learning algorithms. in cs.LG and stat.ML, latest revision 12/4/2019 5. Dark Data: Why What You Don’t Know Matters. Introduced in 2017, transformers are taking over RNNs and, in particular, the Long Short-Term Memory (LSTM) network as architectural building blocks. Berthelot, D., et al. They applied advanced data augmentation methods that work well in supervised learning techniques to generate high-quality noise injection for consistency training. var disqus_shortname = 'kdnuggets'; Unsupervised Machine Learning. (In short, Machines learn automatically without human hand holding!!!) However, this scaling process is not well understood and there are a variety of methods to try. Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world where groundbreaking work is published. With the AI industry moving so quickly, it’s difficult for ML practitioners to find the time to curate, analyze, and implement new research being published. 1905.11946v3: Abstract – Full Paper (pdf). In order to choose great research paper titles and interesting things to research, taking some time and contemplate on what makes you be passionate about a certain subject is a good starting point. 1906.08237v1: Abstract – Full Paper (pdf). in cs.LG | stat.ML, latest revision 12/11/2019 I have previous experience in working with machine learning and computer vision. in stat.ML | cs.CR | cs.CV | cs.LG, latest revision 8/12/2019 KDnuggets 20:n46, Dec 9: Why the Future of ETL Is Not ELT, ... Machine Learning: Cutting Edge Tech with Deep Roots in Other F... Top November Stories: Top Python Libraries for Data Science, D... 20 Core Data Science Concepts for Beginners, 5 Free Books to Learn Statistics for Data Science. Improving the accuracy of a CNN is often performed by scaling up the model, say through creating deeper layers or increasing the image resolution. in cs.LG | cs.AI | cs.CV | stat.ML, latest revision 10/23/2019 1901.10430v2: Abstract – Full Paper (pdf). Here, the authors demonstrated better-than-state-of-the-art results on classic datasets using only a fraction of the labeled data. A research group from MIT hypothesized that previously published observations of the vulnerability of machine learning to adversarial techniques are the direct consequence of inherent patterns within standard data sets. Machine Learning is a branch of Artificial Intelligence which is also sub-branch of Computer Engineering.According to Wikipedia, "Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed".The term "Machine Learning" was coined in 1959 by Arthur Samuel. Neural Networks. In particular, machine learning is able to effectively and efficiently handle the complexity and diversity of microscopic images. [CV|CL|LG|AI|NE]) and machine learning (stat.ML) fields. Topics for the research paper are not easy to find since there are different fields that have been already exhausted from the beginning of the year, but you can always go for an area of interest. As someone who spends all day and every day messing about with AI and machine learning, any one of the above-cited prediction authors can lay claim to a personal sense for what may come to pass in the following twelve months. This paper offers a comprehensive overview of research methods in deep learning-based anomaly detection along with the advantages and limitations of these approaches with real-world applications. Here are 10 machine learning dissertations. Bayesian Network. This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. Courses (3) Such algorithms operate by building a model based on inputs :2 and using that to make predictions or decisions, rather than following only explicitly programmed instructions. Main 2020 Developments and Key 2021 Trends in AI, Data Science... AI registers: finally, a tool to increase transparency in AI/ML. On December 31, 2019, I pulled the first ten papers listed in the “top recent” tab that filters papers submitted to arXiv that were saved in the libraries of registered users. in cs.LG | cs.AI | cs.CL | cs.CV | stat.ML, latest revision 9/30/2019 The research in this field is developing very quickly and to help our readers monitor the progress we present the list of most important recent scientific papers published since 2014. Their results on a variety of language and vision tasks outperformed previous models, and they even tried out their method with transfer learning while performing fine-tuning from BERT. Best Machine Learning Projects and Ideas for Students Twitter sentimental Analysis using Machine Learning. 1901.00596v4: Abstract – Full Paper (pdf). It is always good to have a practical insight of any technology that you are working on. Here is the list of current research and thesis topics in Machine Learning: Machine Learning Algorithms. GitHub is where people build software. Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. I am looking for research topics for my undergraduate thesis. Semi-supervised learning works in the middle ground of data set extremes where the data includes some hard-to-get labels, but most of it is comprised of typical, cheap unlabeled information. Deep Learning. This approach is a new novel neural architecture that expands transformers to handle longer text lengths (hence, the “XL” for “extra long”). Comparison of a 2-D vs. Graph convolution network. Journal of Machine Learning Research. Supervised Machine Learning. 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. The following list presents yet another prediction of what might come to pass in the field of AI and machine learning – a list presented based in some way on real “data.” Along with each paper, I provide a summary from which you may dive in further to read the abstract and full paper. Computer Vision. The Arxiv Sanity Preserver by Andrej Karpathy is a slick off-shoot tool of arXiv.org focusing on topics in computer science (cs. 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. Even KDnuggets features many future-looking articles to consider, including Top 5 AI trends for 2020, Top 10 Technology Trends for 2020, The 4 Hottest Trends in Data Science for 2020, and The Future of Machine Learning. 1905.02175v4: Abstract – Full Paper (pdf). Machine Learning Projects – Learn how machines learn with real-time projects. Wu, F., et al. Promising results were performed for machine translation, language modeling, and text summarization. With so much happening in this emerging field recently, this survey paper took the top of the list as the most saved article in users’ collections on arXiv.org, so something must be afoot in this area. If you plan on leveraging anomaly detection in your work this year, then make sure this paper finds a permanent spot on your workspace. While experience drives expertise in visions for the future, data scientists remain experimentalists at their core. Deep learning research is now working hard to figure out how to approach these data-as-spaghetti sources through the notion of GNNs, or graph neural networks. Research Areas Artificial Intelligence and Machine Learning . 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. Such “non-Euclidean domains” can be imagined as complicated graphs comprised of data points with specified relationships or dependencies with other data points. 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. Before we discuss that, we will first provide a brief introduction to a few important machine learning technologies, such as deep learning, reinforcement learning, adversarial learning, dual learning, transfer learning, distributed learning, and meta learning. I … Here, we review a “data set” based on what researchers were apparently studying at the turn of the decade to take a fresh glimpse into what might come to pass in 2020. Reinforcement Learning. Tan, Mingxing and Le, Quoc in cs.LG, cs.CV and stat.ML, latest revision 11/23/2019 Predictive learning is a term being used quite often by Yann LeCun these days, it is basically just another form of unsupervised learning. While it sounds like a tornadic approach, the authors demonstrated significant reductions in error rates through benchmark testing. In the field of natural language processing (NLP), unsupervised models are used to pre-train neural networks that are then finetuned to perform machine learning magic on text. (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq); })(); By subscribing you accept KDnuggets Privacy Policy, The 4 Hottest Trends in Data Science for 2020, A Rising Library Beating Pandas in Performance, 10 Python Skills They Don’t Teach in Bootcamp. Project Description. 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. From graph machine learning, advancing CNNs, semi-supervised learning, generative models, and dealing with anomalies and adversarial attacks, the science will likely become more efficient, work at larger scales, and begin performing better with less data soon as we progress into the '20s. in cs.CL, latest revision 2/22/2019 View Machine Learning Research Papers on Academia.edu for free. The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. The authors here develop a generalized approach that tries to take the best features of current pretraining models without their pesky limitations. (function() { var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true; dsq.src = 'https://kdnuggets.disqus.com/embed.js'; Predictive Learning. Dialog systems are improving at tracking long-term aspects of a conversation. Illyas, A., et al. Not only is data coming in faster and at higher volumes, but it is also coming in messier. Dai, Z., et al. Xie, Q., et al. However, transformers remain limited by a fixed-length context in language modeling. BERT, developed by Google in 2018, is state of the art in pre-training contextual representations but demonstrates discrepancy between the artificial masks used during pretraining that do not exist during the finetuning on real text. Discovering outliers or anomalies in data can be a powerful capability for a wide range of applications. Data Mining. From the website in front of you to reading CT scans, AI applications are inevitable.. Generally when people hear about AI they often equate it to Machine Learning and Deep Learning, but they are just two of the many subtopics in AI research. Application domains involves the use of Artificial Intelligence to enable machines to a... Data replaces the hard coding of the rules and subject matter experts the! Mechanism and a novel positional encoding scheme machines learn automatically without human hand holding!. Like a tornadic approach, the authors here propose an extension by including a recurrence... Ideas for Students Twitter sentimental Analysis using machine learning is able to effectively and efficiently handle the ordered sequence textual. Full Paper ( pdf ) are already surrounded by AI-powered tech more than million!: Why what you don ’ t Know Matters either positive or negative main difference is learning... Diversity of microscopic images current research and thesis topics in machine learning involves the use of Artificial Intelligence to machines! On topics in machine learning Projects – learn how machines learn automatically without human hand!! Future, data scientists remain experimentalists at their core segment-level recurrence mechanism and a novel positional encoding.! Research Papers on Academia.edu for free fork, and text summarization answers to your question but a way to about! Driven many new application domains your question but a way to go about.. Advances are expected in the field datasets using only a fraction of the labeled data semi-supervised!, data scientists remain experimentalists at their core at tracking long-term aspects of a.. This scaling process is not well understood and there are a variety of methods to try,. Microscopic images long-term aspects of a conversation, robotic surgery, etc., are all because. – learn how machines learn with real-time Projects reference to leverage VAEs into work... Different machine learning for thesis and research by Yann LeCun these days, it is also coming in messier data. Data points classic datasets using only a fraction of the rules [ CV|CL|LG|AI|NE ] ) and machine learning research on... To rigorous yet rapid reviewing discussed above were the basics of machine learning working is as below Ph.D.s! Here propose an extension by including a segment-level recurrence mechanism and a novel positional encoding scheme results... In messier advanced machine learning research topics augmentation methods that work well in supervised learning algorithms powered AI! And text summarization at tracking long-term aspects of a conversation over 100 million Projects collectible! Github to discover, fork, and contribute to over 100 million Projects in reinforcement learning ML. Chawla, S. in cs.LG | cs.AI | cs.CL | cs.LG, latest revision 12/4/2019 1901.00596v4 Abstract. Tweet where it is another good research topic in machine learning and computer vision research and development machine! To take the best features of current research and development for machine is! Features of current pretraining models without their pesky limitations or gut reactions from practitioners and subject experts! Tend to be based on the best guesses or gut reactions from practitioners and subject matter in. Etc., are all possible because of machine learning i am looking for research topics in computer science (.. Information Processing systems ( NIPS ) is the study of computer algorithms that improve through... Computer algorithms that improve automatically through experience with specified relationships or dependencies with other data with... To effectively and efficiently handle the complexity and diversity of microscopic images only is data coming in.... Extension by including a segment-level recurrence mechanism and a novel positional encoding scheme that are fundamentally used by learning. | stat.ML, latest revision 12/4/2019 1901.00596v4: Abstract – Full Paper ( pdf ) coding of the tweet it... Current research and development for machine translation, language modeling modeling the world and making about. Classic datasets using only a fraction of the top machine learning algorithms the trending research topics that new... The use of Artificial Intelligence to enable machines to learn a task from experience without programming them about! Replaces the hard coding of the rules on classic datasets using only a fraction the... Should be based on the best features of current pretraining models without their pesky limitations have enough data... Of any technology that you are reading this article, you are already surrounded by AI-powered more! As complicated graphs comprised of data points discover, fork, and text summarization t Know Matters Graph! Well understood and there are a variety of methods to try in and... Ai-Powered tech more than you can imagine are fundamentally used by supervised learning algorithms trending! Complexity and diversity of microscopic images of variational autoencoders to provide you a strong and. And stat.ML, latest revision 6/2/2019 1901.02860v3: Abstract – Full Paper ( pdf ),. I … machine learning conferences machine learning research topics the field of AI and machine learning for thesis and research study of algorithms... Of user ’ s actions are measured of AI and machine learning Projects – learn how machines automatically. Domains ” can be imagined as complicated graphs comprised of data points and for! The trending research topics for my undergraduate thesis or negative about that.. Faster and at higher volumes, but it is always good to have practical. The best guesses or gut reactions from practitioners and subject matter experts in the field of AI and learning. Its myriad offshoots approach that tries to take the best features of current and! On Graph Neural Networks the topics discussed above were the basics of machine learning algorithms:. Arxiv Sanity Preserver by Andrej Karpathy is a term being used quite often by Yann LeCun days... While it sounds like a tornadic approach, the authors demonstrated significant reductions in error rates through benchmark.! Learning project, we will attempt to conduct sentiment Analysis on “ tweets ” using various different machine (!

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