Research paper on artificial intelligence

Impact factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. Sjr uses a similar algorithm as the google page rank; it provides a quantitative and a qualitative measure of the journal’s more on journal example article on lides are short, 5-minute presentations in which the author explains their paper in their own in brief authors co-submit and publish a data article in data in brief, it appears on sciencedirect linked to the original research article in this ctive plot example article on application lets readers explore data and other quantitative results submitted with the article, providing insights into and access to data that is otherwise buried in sx authors co-submit and publish a method article in methodsx, it appears on sciencedirect linked to the original research article in this hing your article with us has many benefits, such as having access to a personal dashboard: citation and usage data on your publications in one place. This free service is available to anyone who has published and whose publication is in downloaded artificial intelligence most downloaded articles from artificial intelligence in the last 90 cial cognition for social human–robot interaction: an implementation. Ng metric-topological maps for indoor mobile robot semi-markov ethics: mapping the issues for a mechanized k lin | keith abney | george ion of artificial s a science of integrated ai and rajan | alessandro m computation, quantum theory and c manipulation of multiple objects as a pajarinen | ville open-source toolkit for mining milne | ian h. Suchanek | klaus berberich | gerhard dropout learning baldi | peter chical conceptual spaces for concept lewis | jonathan 7, 2017 @ 10:15 s kristen stewart's research paper on artificial intelligence: a critical ns expressed by forbes contributors are their own. Do people who work in machine learning and ai think of actress kristen stewart's research paper on ai?

Artificial intelligence research papers

Originally appeared on quora: the place to gain and share knowledge, empowering people to learn from others and better understand the by xavier amatriain, vp of engineering at quora, on quora:There are perhaps two different questions to answer here: (1) what do we think of the paper? As most things surrounding ai these days there is of course some hype effect and i understand how general publications would fall for a paper that manages to put together ai and a hollywood actress. They also later explain that releasing a paper on arxiv does not mean it has been peer reviewed or accepted by any research community. People should keep in mind that although arxiv moderates submissions, they are not accountable for the research quality of any of the papers there. For now, and before it is accepted anywhere else, the paper should be interpreted in that , as others have pointed out, this is not a research paper on ai as claimed in the headline. It does not mean in itself this is a bad research paper, but it needs to be evaluated on different grounds since it clearly does not introduce any novelty to the ai/ml field.

While i have done research on ml, most of my publications are actually application papers on areas that go from recommender systems to multimedia systems. As a matter of fact, i have co-authored papers with artists, some of which have been published in international conferences and , with all this in mind, let me address the second question: what do i think of the paper? In particular, being this an application paper, it is not enough with referencing recent ai/ml papers. The authors should have referenced other approaches to artistic creation with said, i think the paper might be a worthwhile submission for a “poster” to a workshop, for example. Given the format and the length i am imagining that is what the authors might have in y, let me address the question of whether kristen stewart deserves to co-author the paper since this has been discussed in other answers. The role of the artist/creator is as important or more as the role of the ai researcher.

In fact, here is a little secret for you, i am pretty sure kristen contributed more to what is described in this paper than many famous professors or researchers who are added to phd student question originally appeared on quora. More questions:Artificial intelligence: will it ever be possible for artificial intelligence to write entertainment? Issn 1076 - 9757) covers all areas of artificial intelligence (ai), publishing refereed research articles, survey articles, and technical notes. Jair reviews papers within approximately three months of submission and publishes accepted articles on the internet immediately upon receiving the final versions. Most impressive research papers around artificial cial intelligence research advances are transforming technology as we know it. The ai research community is solving some of the most technology problems related to software and hardware infrastructure, theory and algorithms.

Interestingly, the field of ai ai research has drawn acolytes from the non-tech field as well. Case in point — prolific hollywood actor kristen stewart’s highly publicized paper on artificial intelligence, originally published at cornell university library’s open access site. Stewart co-authored the paper, titled “bringing impressionism to life with neural style transfer in come swim” with the american poet and literary critic david shapiro and adobe research engineer bhautik ially, the ai-based paper talks about the style transfer techniques used in her short film come swim. Analytics india magazine lists down the most cited scientific papers around ai, machine intelligence, and computer vision, that will give a perspective on the technology and its of these papers have been chosen on the basis of citation value for each. Some of these papers take into account a highly influential citation count (hic) and citation velocity (cv). Citation velocity is the weighted average number of citations per year over the last 3 ai dips into her extensive research knowledge.

Computational approach to edge detection: originally published in 1986 and authored by john canny this paper, on the computational approach to edge detection, has approximately 9724 citations. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the s, the paper also presents a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. This helps in establishing the fact that edge detector performance improves considerably as the operator point spread function is extended along the read  experts speak: recruitment in analyticsa proposal for the dartmouth summer research project on artificial intelligence: this research paper was co-written by john mccarthy, marvin l. This summer research proposal defined the field, and has another first to its name — it is the first paper to use the term artificial intelligence. The proposal invited researchers to the dartmouth conference, which is widely considered the “birth of ai”. Threshold selection method from gray-level histograms: the paper was authored by nobuyuki otsu and published in 1979.

Through this paper, otsu discusses a nonparametric and unsupervised method of automatic threshold selection for picture paper delves into how an optimal threshold is selected by the discriminant criterion to maximize the separability of the resultant classes in gray levels. The paper validates the method by presenting several experimental normalization: accelerating deep network training by reducing internal covariate shift: this 2015 article was co-written by sergey ioffe and christian szegedy. The paper received 946 citations and reflects on a hic score of paper talks about how training deep neural networks is complicated by the fact that the distribution of each layer’s inputs changes during training. In other words, batch normalization beats the original model by a significant read  self-driving not the only frontier of innovation in automotive, check out other use cases of aideep residual learning for image recognition: the 2016 paper was co-authored by kaiming he, xiangyu zhang, and shaoqing ren. The paper has been cited 1436 times, reflecting on a hic value of 137 and a cv of 582. The authors have delved into residual learning framework to ease the training of deep neural networks that are substantially deeper than those used s, the research paper explicitly reformulates the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions.

The research also delves into how comprehensive empirical evidence show that these residual networks are easier to optimize, and can gain accuracy from considerably increased ctive image features from scale-invariant keypoints: this article was authored by david g. The paper received 21528 citations  and explores the method for extracting distinctive invariant features from images. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3d viewpoint, addition of noise, and change in paper additionally delves into an approach which leverages these features for image recognition. This approach can help identify objects among clutter and occlusion while achieving near real-time t: a simple way to prevent neural networks from overfitting: the 2014 paper was co-authored by nitish srivastava, geoffrey hinton, alex krizhevsky, ilya sutskever, and ruslan salakhutdinov. The paper has been cited around 2084 times, with a hic and cv value of 142 and 536 respectively. However, overfitting is a serious problem in such central premise of the paper is to drop units (along with their connections) from the neural network during training, thus preventing units from co-adapting too much.

Quinlan, this scientific paper was originally published in 1986 and summarizes an approach to synthesizing decision trees that has been used in a variety of systems. Additionally, the paper discusses a reported shortcoming of the basic algorithm, besides comparing the two methods of overcoming it. To conclude the paper, the author presents illustrations of current research published its first artificial intelligence research -scale video classification with convolutional neural networks : this 2014 paper was co-written by 6 authors, andrej karpathy, george toderici, sanketh shetty, thomas leung, rahul sukthankar, and li fei-fei. The paper has been cited over 865 times, and reflects on a hic score of 24, and a cv of utional neural networks (cnns) are proven to stand as a powerful class of models for image recognition problems. This was accomplished using a new dataset of 1 million youtube videos belonging to 487 ilistic reasoning in intelligent systems: networks of plausible inference: the paper was published in 1988. The paper presents a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under furnishes a provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other ai approaches to uncertainty, such as the dempster-shafer formalism, truth maintenance systems, and nonmonotonic e your comments below a background in engineering, amit has assumed the mantle of content analyst at analytics india magazine.

In other life, he would invest his time into comics, football, and us articlechina opens up its ‘big data valley’ at guizhou articlegoing beyond books: barnes and noble takes a dive into predictive n stewart has ventured into the world of artificial intelligence. Less well-known is her interest in ai, laid out in a new paper on the use of the technology to create art in her screenwriting debut, come swim. The paper was released yesterday on arxiv, an online research repository run by cornell which publishes papers before they’ve been peer t’s starting inspiration point for come swim was one of her own paintings. The paper describes the filmmaker’s experiments with style transfer, a popular use of machine learning that transforms one image into the artistic technique and color profile of another. Stewart’s co-authors on the paper are a producer at starlight studios (which produced the film) and an adobe employee, whose involvement in the film is paper’s most interesting aspect is its ambition: the team originally tried to tune the algorithm to transfer the sense of emotion in the painting. To direct the algorithm into producing an artistically satisfying image proved more difficult than expected, according to the paper.