**Exploring AI Tasks and Research Papers**

The field of Artificial Intelligence is vast and diverse, with various tasks and techniques being developed and researched. In this article, we will delve into the different sections of the AI tasks website and explore some of the most interesting categories.

**Computer Vision**

One of the main sections of the website is Computer Vision, which contains 782 tasks. This section can be further broken down into subcategories such as Segmentation, Semantic Segmentation, Image Classification, Object Detection, Image Generation, and Domain Adaptation. These tasks are all related to image and video processing, with applications in areas such as self-driving cars, medical imaging, and surveillance.

**Medical Image Segmentation**

Within the Computer Vision section, there is a specific category for Medical Image Segmentation. This task involves segmenting medical images to extract specific features or information. The website provides examples of how this task can be applied to different medical applications, such as brain tumor segmentation and lesion segmentation. These tasks are crucial in medical imaging, where accurate segmentation can lead to improved diagnosis and treatment outcomes.

**Drug Discovery**

Another category under Computer Vision is Drug Discovery. This section contains a leaderboard that tracks the performance of different methods on various datasets. The leaderboard provides valuable insights into the effectiveness of different approaches, allowing researchers and developers to compare their methods with existing ones. The website also provides links to the original research papers and code for each method, making it easier for users to explore and learn from these techniques.

**Natural Language Processing**

In addition to Computer Vision, the AI tasks website also covers Natural Language Processing (NLP). This section contains 21 tasks related to text analysis, including Machine Translation, Language Modeling, Question Answering, Sentiment Analysis, Text Classification, and others. These tasks are crucial in areas such as language translation, sentiment analysis, and chatbots.

**Machine Translation**

Within the NLP section, there is a specific category for Machine Translation. This task involves translating text from one language to another, with applications in areas such as international business, communication, and education. The website provides examples of how this task can be applied to different languages and domains, including machine learning approaches that improve translation accuracy.

**Sentiment Analysis**

Another NLP task is Sentiment Analysis, which involves determining the emotional tone or sentiment of text. This task has applications in areas such as customer service, market research, and social media monitoring. The website provides examples of how this task can be applied to different domains, including text classification approaches that improve sentiment analysis accuracy.

**Representation Learning**

The AI tasks website also covers Representation Learning, which involves learning features or representations from data that can be used for various tasks. This section contains 124 additional tasks related to representation learning, including Word Embeddings, Domain Adaptation, and Data Augmentation. These tasks are crucial in areas such as language modeling, image recognition, and natural language processing.

**Methodology**

In addition to task categories, the website also provides a Methodology section that covers various approaches and techniques used in AI research. This section includes topics such as Representation Learning, Transfer Learning, Word Embeddings, Domain Adaptation, Data Augmentation, and others. These methods are crucial in areas such as image recognition, natural language processing, and machine learning.

**Miscellaneous**

The website also covers Miscellaneous tasks, including Graphs, Games, Realtime Strategy Games, Speech and Audio, Time Series Analysis, Computer Code, Text-to-Sequence, Program Synthesis, Code Generation, Features, Election Dimensionality Reduction, and Robotics. These tasks are diverse and cover a wide range of applications in areas such as computer vision, natural language processing, machine learning, and more.

**Conclusion**

The AI tasks website provides an extensive collection of resources for researchers and developers interested in artificial intelligence. From Computer Vision to Natural Language Processing, the website covers various tasks and techniques that can be used to develop and improve AI systems. Whether you're a newcomer or an experienced researcher, this website is a valuable resource for learning about AI research and staying up-to-date with the latest developments in the field.

**References**

If you're interested in learning more about these topics, be sure to check out the original research papers and code provided on the website. Some of the notable papers include those on Machine Translation, Sentiment Analysis, Representation Learning, and Transfer Learning. These resources provide valuable insights into the latest techniques and approaches being used in AI research.

**Best Practices**

If you're developing your own AI system or method, be sure to explore the leaderboard and compare your approach with existing ones. This can help identify areas for improvement and provide valuable feedback for future development. Additionally, don't forget to check out the full task list and miscellaneous section for additional resources and inspiration.

**Final Thoughts**

The AI tasks website is a treasure trove of resources for researchers and developers interested in artificial intelligence. From Computer Vision to Natural Language Processing, this website covers various tasks and techniques that can be used to develop and improve AI systems. Whether you're a newcomer or an experienced researcher, this website is a valuable resource for learning about AI research and staying up-to-date with the latest developments in the field.

**Stay Inspired**

The AI tasks website provides endless inspiration and resources for researchers and developers interested in artificial intelligence. From machine translation to representation learning, there's always something new to explore and learn from these techniques and approaches. Stay inspired and keep pushing the boundaries of what's possible with AI research!

"WEBVTTKind: captionsLanguage: enwhere can I find research articles with the corresponding code that is the question that I get asked quite often and so I figured that it would probably be a great idea if I create a video about it and so in this video I'm going to tell you how you can have access to the research papers along with the corresponding code and so without further ado let's get started and the website that I'm talking about today is called papers with code and I'll provide you the link in the description down below go ahead and type in papers with code comm and this is the website that we're going to talk about today so as you can see from the main website you have a search box where you can search for the papers or the codes or the tasks that you would like to find and what it essentially will provide you with is the research paper along with the corresponding code and so on the front page here you see that there is the trending research and as the name implies they are the research that are trending and what you will see here are the screen shot of the papers or the graphical abstract the name of the research article parts of the abstract which will essentially tell you what this paper is about and on the right you will see the number of stars that it has received on github and it provides you the link to the original paper as well as the code on github and you will also see the tag that this research article is referring to and so let's start out with having a general look at this website so let's click on the belt and so what we see here is the mission of this website which is to create a free and open resource with machine learning papers code and evaluation tables and so what this essentially mean is that the website will provide you with the papers the corresponding code and the evaluation table and so the evaluation table is the prediction results that each of the paper are referring to so I'm gonna show you that in just a moment and so as you can see in the last sentence of the mission they have automated the linking of code to papers and they are also working on automating the extraction of evaluation matrix from the papers and so the evaluation metric as ivory we mention is essentially the prediction results from the models that are mentioned in the original papers so let's say that there are a hundred papers in this website and each of the paper let's say that they are using the same benchmark data set and let's say that paper one reported a accuracy of 0.8 and paper 2 reported an accuracy of 0.8 one and both of them are using the same benchmark data set and so what we can essentially see is the comparison of the prediction accuracy from each of these two papers and so if there are a hundred papers reporting prediction accuracy from the same benchmark data set that we're gonna see all of the hundred evaluation metrics and so the cool part about this is that you can see the general performance increase or a decrease that are occurring over time and so I'm gonna show you that in just a moment and so if you would like to contribute to this open source project you can feel free to have a look and do so and if you would like to download the entire data set that constitute this website then feel free to click on the downloading data so they will provide you all of the papers with the abstract and the links between the papers and code along with the evaluation tables all of them in JSON formats and so here they mentioned that the contents have been parsed from 60000 papers and they have manually annotated tasks and data set in 1600 archived abstract from the last three months of 2018 and hundreds of papers on popular machine learning tasks with evaluation metrics were derived from the following sources and so if you're interested in having a look at the code that were used for scraping and importing the data you can click on this link okay and so let's have a look at the trends and so in the trench section you're gonna see the computational framework that were used in the papers contained within this website so here you can see that there are pi towards tensorflow jabs m x net cafe - and other languages and framework and so you can see the general popularity of these over time and the market share so here in the trends we can see that in March 2016 other languages and framework accounted for 65% whereas the number two would be tensorflow and pi torch was number three at 3% and it should be noted that the percentage here are based on the github repository contained within this website and the time at which it was published and so here we see that in June 2016 other language accounted for 77% number two is tensorflow and 19% number three cafe 2 at 2% number 4 hi torch at 1% and as we progress over time we can see that other languages is starting to decline and tensorflow is gaining momentum along with PI torch we see that by March 2017 other languages accounted for only 56% tensorflow increase to 34 hi towards increased to 8 and over time the PI torch market chair started to increase as well right and we see that other languages started to decline 40% 39% and we can see here that by 2018 in June PI torch is rivaling tensorflow at 27% versus 32 percent and other language at 40 percent and unless fast forward to December 2019 we could see that tensorflow became number one at 44% number two is other languages number three is tensorflow at 23% and by March 2020 no one is also pi torch at 49% number two other language number three tensorflow at 20% and so we can see here that over time pi torch gained momentum and became number one and so in summary we could see here that the initial technology that were used were other languages which could be traditional machine learning algorithms and we started to see that the trend started to increase to using tensorflow and pi towards slowly emerged and so over time pi torch gained momentum and by 2020 it became number one okay and so let's have a look at the code availability so the code availability is here the percentage of published paper that at least one code implementation and so we could see that in March of 2016 it had 13% meaning that 13% of papers came along with the code and we're starting to see a general increase over time and by December of 2019 the percentage increase to 23% for the code availability okay and here they summarize how did they collect the data from the github repository okay and so let's have a look back at the front page so let's click on the first entry here and so we could see the name of the research article the date at which it was published the name of the co-authors the abstract of the paper and we could click on read more to have a look at the entire abstract and the corresponding PDF of the research article is provided here which you could click on ok and so this is the research article PDF let's head on back and the abstracts from the original website which is the same thing here and the code is right here you can click on this link and the number of stars and github will be the metric that or used for ranking the papers in this website and each entry are assigned the keyword tasks here so if you would like to find more papers about this tag you can just click on it for example common-sense reasoning or sentence completion or language modeling and so here is the results from the paper and remember I told you about the evaluation table and so this is the evaluation table and so the evaluation table will tell you the task at hand and the dataset name the model the metric name such as the accuracy f1 okay and the metric value is the corresponding value of the accuracy or the f1 and the global rank is the rank that this particular paper has attained in comparison with other papers reporting the same prediction tasks using the same benchmark data set as this one okay and I think that's all for this paper entry here and so let's click on back and as mentioned before this is the star that has been assigned by the number of people bookmarking the particular github repository and so the number of star here indicate the popularity of this particular paper and so this is the trending research and we can click on the latest and they will be the ones that are recently published and so here in May 28 2008 are the recent papers so if we scroll down 27 May 20 20 26 May 20 20 so this is sorted by the dates okay from the latest date to the order dates and let's click on the greatest here and so the greatest here is the top papers of all time meaning that they have the most stars on github and so the original paper describing tensorflow had the highest rank at 144 thousand stars here and the second is also the paper associated with tensorflow and the third is scikit-learn okay with forty thousand eight hundred pi torch had fourth and fifth six also is part towards and so this particular section is very good for beginners and those who are enthusiastic about machine learning and deep learning and if you would like to get more serious into the details then have a look at these important papers and look at the code and get a general idea of what they are about okay so now let's have a look at the brow state of the Arts and so the good thing about this section is that the papers will be categorized into several sections and so here we see computer vision and then there are sub section as well and if you would like to have a look at all of them you can click on see all 782 tasks so this is under computer vision and there's natural language processing and under natural language processing you will see machine translation language modeling question answering sentiment analysis text classification and so this is very nice in that if you would like to have a look further into learning about sentiment analysis and so here's your chance to have a look at the original research papers along with the corresponding code so that you could download the code and play along with the code get a glimpse to get an idea of what the code has to offer and learn from the code while you're at it so for those of you who are into medical area then this is particularly interesting for you you could have a look at medical image segmentation drug discovery lesion segmentation brain tumor segmentation brain segmentation and there are 186 more tasks and papers that are describing about methodology improvements are here in the methodology section and so they are divided into representation learning transfer learning word embedding domain adaptation data augmentation and there are 124 additional tasks so you can click on here to have a look at the full task list and miscellaneous right so they are topics that are not categorized into any particular section graphs related topics playing games Atari game Starcraft part 1 and part 2 realtime strategy games so if you are into eSports then have a look at this particular section okay and so if you're interested in speech and audio then this would be for you if you're into time series analysis then this section is for you okay and so audio is right here computer code right here text to sequel program synthesis code generation features election dimensionality reduction okay and so if you're into robotics then this section is for you if you're into music right here knowledgebase adversarial reasoning okay so let's have a look at the first category here computer vision see all 782 tasks okay and so they could be broken down further into sub sub categories okay segmentation semantic segmentation image classification object detection image generation domain adaptation right and there's so many here so okay so they're also related to computer vision so medical image segmentation is contained here and let's have a look back and so we can see that okay if their medical then we can see that medical image segmentation belongs to both the medical category and the computer vision because they're related to both and so there's also drug discovery so let's have a look at drug discovery okay so in the drug discovery here there are this leaderboard and the leaderboard here tells you what is the general trend of the performance metrics and the name of the data sets and the name of the best method in this comparison and the paper title the paper and the code and so we can see here that no code is provided here and the greatest paper with code is right here so this is the paper by the deep ten project have a look at the tops 21 dataset so let's click on this one and so here we see that the trend of the performance AUC increases over time so this how we'll look here we see here that in 2015 the method called graph convey had a you see of zero point eight four six and over time it increased slightly point eight five four point eight six two and point eight seven five and it should be noted that over time starting from 2015 the performance deteriorated zero point eight four five seven eight one seven four eight and so I'm not sure about the reason for the lower a you see probably they're trying out new things but they're not as good as the graph convey method okay and so this is particularly good in the sense that you could compare the different methods that are applied on the same data set and so if you're developing your own methodology so you could have a look at whether you could improve upon the performance when comparing to the previously published methods let's have a look back let's click on another data set HIV data set okay and here there are four papers and the performance 2015 gave point eight two to two thousand seventeen point eight five one which increased and then this context spread and RNN led to lowered prediction okay so I think that you get a general idea of what this papers with code website is all about and I think that is particularly a good resource for newcomers to the field along with those who are more experienced as it allows you to learn directly from the code and read the paper at the same time and so if you're finding value in this video please give it a thumbs up and if you haven't yet subscribed please subscribe to the channel and as always the best way to learn data science is to do data science and please enjoy the journey thank you for watching please like subscribe and share and I'll see you in the next one but in the meantime please check out these videoswhere can I find research articles with the corresponding code that is the question that I get asked quite often and so I figured that it would probably be a great idea if I create a video about it and so in this video I'm going to tell you how you can have access to the research papers along with the corresponding code and so without further ado let's get started and the website that I'm talking about today is called papers with code and I'll provide you the link in the description down below go ahead and type in papers with code comm and this is the website that we're going to talk about today so as you can see from the main website you have a search box where you can search for the papers or the codes or the tasks that you would like to find and what it essentially will provide you with is the research paper along with the corresponding code and so on the front page here you see that there is the trending research and as the name implies they are the research that are trending and what you will see here are the screen shot of the papers or the graphical abstract the name of the research article parts of the abstract which will essentially tell you what this paper is about and on the right you will see the number of stars that it has received on github and it provides you the link to the original paper as well as the code on github and you will also see the tag that this research article is referring to and so let's start out with having a general look at this website so let's click on the belt and so what we see here is the mission of this website which is to create a free and open resource with machine learning papers code and evaluation tables and so what this essentially mean is that the website will provide you with the papers the corresponding code and the evaluation table and so the evaluation table is the prediction results that each of the paper are referring to so I'm gonna show you that in just a moment and so as you can see in the last sentence of the mission they have automated the linking of code to papers and they are also working on automating the extraction of evaluation matrix from the papers and so the evaluation metric as ivory we mention is essentially the prediction results from the models that are mentioned in the original papers so let's say that there are a hundred papers in this website and each of the paper let's say that they are using the same benchmark data set and let's say that paper one reported a accuracy of 0.8 and paper 2 reported an accuracy of 0.8 one and both of them are using the same benchmark data set and so what we can essentially see is the comparison of the prediction accuracy from each of these two papers and so if there are a hundred papers reporting prediction accuracy from the same benchmark data set that we're gonna see all of the hundred evaluation metrics and so the cool part about this is that you can see the general performance increase or a decrease that are occurring over time and so I'm gonna show you that in just a moment and so if you would like to contribute to this open source project you can feel free to have a look and do so and if you would like to download the entire data set that constitute this website then feel free to click on the downloading data so they will provide you all of the papers with the abstract and the links between the papers and code along with the evaluation tables all of them in JSON formats and so here they mentioned that the contents have been parsed from 60000 papers and they have manually annotated tasks and data set in 1600 archived abstract from the last three months of 2018 and hundreds of papers on popular machine learning tasks with evaluation metrics were derived from the following sources and so if you're interested in having a look at the code that were used for scraping and importing the data you can click on this link okay and so let's have a look at the trends and so in the trench section you're gonna see the computational framework that were used in the papers contained within this website so here you can see that there are pi towards tensorflow jabs m x net cafe - and other languages and framework and so you can see the general popularity of these over time and the market share so here in the trends we can see that in March 2016 other languages and framework accounted for 65% whereas the number two would be tensorflow and pi torch was number three at 3% and it should be noted that the percentage here are based on the github repository contained within this website and the time at which it was published and so here we see that in June 2016 other language accounted for 77% number two is tensorflow and 19% number three cafe 2 at 2% number 4 hi torch at 1% and as we progress over time we can see that other languages is starting to decline and tensorflow is gaining momentum along with PI torch we see that by March 2017 other languages accounted for only 56% tensorflow increase to 34 hi towards increased to 8 and over time the PI torch market chair started to increase as well right and we see that other languages started to decline 40% 39% and we can see here that by 2018 in June PI torch is rivaling tensorflow at 27% versus 32 percent and other language at 40 percent and unless fast forward to December 2019 we could see that tensorflow became number one at 44% number two is other languages number three is tensorflow at 23% and by March 2020 no one is also pi torch at 49% number two other language number three tensorflow at 20% and so we can see here that over time pi torch gained momentum and became number one and so in summary we could see here that the initial technology that were used were other languages which could be traditional machine learning algorithms and we started to see that the trend started to increase to using tensorflow and pi towards slowly emerged and so over time pi torch gained momentum and by 2020 it became number one okay and so let's have a look at the code availability so the code availability is here the percentage of published paper that at least one code implementation and so we could see that in March of 2016 it had 13% meaning that 13% of papers came along with the code and we're starting to see a general increase over time and by December of 2019 the percentage increase to 23% for the code availability okay and here they summarize how did they collect the data from the github repository okay and so let's have a look back at the front page so let's click on the first entry here and so we could see the name of the research article the date at which it was published the name of the co-authors the abstract of the paper and we could click on read more to have a look at the entire abstract and the corresponding PDF of the research article is provided here which you could click on ok and so this is the research article PDF let's head on back and the abstracts from the original website which is the same thing here and the code is right here you can click on this link and the number of stars and github will be the metric that or used for ranking the papers in this website and each entry are assigned the keyword tasks here so if you would like to find more papers about this tag you can just click on it for example common-sense reasoning or sentence completion or language modeling and so here is the results from the paper and remember I told you about the evaluation table and so this is the evaluation table and so the evaluation table will tell you the task at hand and the dataset name the model the metric name such as the accuracy f1 okay and the metric value is the corresponding value of the accuracy or the f1 and the global rank is the rank that this particular paper has attained in comparison with other papers reporting the same prediction tasks using the same benchmark data set as this one okay and I think that's all for this paper entry here and so let's click on back and as mentioned before this is the star that has been assigned by the number of people bookmarking the particular github repository and so the number of star here indicate the popularity of this particular paper and so this is the trending research and we can click on the latest and they will be the ones that are recently published and so here in May 28 2008 are the recent papers so if we scroll down 27 May 20 20 26 May 20 20 so this is sorted by the dates okay from the latest date to the order dates and let's click on the greatest here and so the greatest here is the top papers of all time meaning that they have the most stars on github and so the original paper describing tensorflow had the highest rank at 144 thousand stars here and the second is also the paper associated with tensorflow and the third is scikit-learn okay with forty thousand eight hundred pi torch had fourth and fifth six also is part towards and so this particular section is very good for beginners and those who are enthusiastic about machine learning and deep learning and if you would like to get more serious into the details then have a look at these important papers and look at the code and get a general idea of what they are about okay so now let's have a look at the brow state of the Arts and so the good thing about this section is that the papers will be categorized into several sections and so here we see computer vision and then there are sub section as well and if you would like to have a look at all of them you can click on see all 782 tasks so this is under computer vision and there's natural language processing and under natural language processing you will see machine translation language modeling question answering sentiment analysis text classification and so this is very nice in that if you would like to have a look further into learning about sentiment analysis and so here's your chance to have a look at the original research papers along with the corresponding code so that you could download the code and play along with the code get a glimpse to get an idea of what the code has to offer and learn from the code while you're at it so for those of you who are into medical area then this is particularly interesting for you you could have a look at medical image segmentation drug discovery lesion segmentation brain tumor segmentation brain segmentation and there are 186 more tasks and papers that are describing about methodology improvements are here in the methodology section and so they are divided into representation learning transfer learning word embedding domain adaptation data augmentation and there are 124 additional tasks so you can click on here to have a look at the full task list and miscellaneous right so they are topics that are not categorized into any particular section graphs related topics playing games Atari game Starcraft part 1 and part 2 realtime strategy games so if you are into eSports then have a look at this particular section okay and so if you're interested in speech and audio then this would be for you if you're into time series analysis then this section is for you okay and so audio is right here computer code right here text to sequel program synthesis code generation features election dimensionality reduction okay and so if you're into robotics then this section is for you if you're into music right here knowledgebase adversarial reasoning okay so let's have a look at the first category here computer vision see all 782 tasks okay and so they could be broken down further into sub sub categories okay segmentation semantic segmentation image classification object detection image generation domain adaptation right and there's so many here so okay so they're also related to computer vision so medical image segmentation is contained here and let's have a look back and so we can see that okay if their medical then we can see that medical image segmentation belongs to both the medical category and the computer vision because they're related to both and so there's also drug discovery so let's have a look at drug discovery okay so in the drug discovery here there are this leaderboard and the leaderboard here tells you what is the general trend of the performance metrics and the name of the data sets and the name of the best method in this comparison and the paper title the paper and the code and so we can see here that no code is provided here and the greatest paper with code is right here so this is the paper by the deep ten project have a look at the tops 21 dataset so let's click on this one and so here we see that the trend of the performance AUC increases over time so this how we'll look here we see here that in 2015 the method called graph convey had a you see of zero point eight four six and over time it increased slightly point eight five four point eight six two and point eight seven five and it should be noted that over time starting from 2015 the performance deteriorated zero point eight four five seven eight one seven four eight and so I'm not sure about the reason for the lower a you see probably they're trying out new things but they're not as good as the graph convey method okay and so this is particularly good in the sense that you could compare the different methods that are applied on the same data set and so if you're developing your own methodology so you could have a look at whether you could improve upon the performance when comparing to the previously published methods let's have a look back let's click on another data set HIV data set okay and here there are four papers and the performance 2015 gave point eight two to two thousand seventeen point eight five one which increased and then this context spread and RNN led to lowered prediction okay so I think that you get a general idea of what this papers with code website is all about and I think that is particularly a good resource for newcomers to the field along with those who are more experienced as it allows you to learn directly from the code and read the paper at the same time and so if you're finding value in this video please give it a thumbs up and if you haven't yet subscribed please subscribe to the channel and as always the best way to learn data science is to do data science and please enjoy the journey thank you for watching please like subscribe and share and I'll see you in the next one but in the meantime please check out these videos\n"