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| International Conference on Intelligent Systems Design and Applications (ISDA) |
2009-11-30 |
2009-12-02 |
2009-05-31 |
2009-07-25 |
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Pisa, Italy
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The International Conference on Intelligent Systems Design and Applications (ISDA) is a major annual international conference to bring together researchers, engineers, developers and practitioners from academia and industry working in all interdisciplinary areas of computational intelligence and system engineering to share their experience, and exchange and cross-fertilize their ideas.
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| Twenty-Third Annual Conference on Neural Information Processing Systems (NIPS) |
2009-01-07 |
2009-12-12 |
2009-06-05 |
2009-08-05 |
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Vancouver, Canada
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The Twenty-Third Annual Conference on Neural Information Processing Systems, is an interdisciplinary conference that brings together researchers in all aspects of neural and statistical information processing and computation. The conference is a highly selective, single track meeting that includes invited talks as well as oral and poster presentations of refereed papers. Submissions by authors who are new to NIPS are encouraged. Preceding the main conference will be one day of tutorials (December 7), and following will be two days of workshops at the Whistler/Blackcomb ski resort (December 11-12). |
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| The World Congress on Nature and Biologically Inspired Computing (NABIC'09) |
2009-12-09 |
2009-12-11 |
2009-06-26 |
2009-08-14 |
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Coimbatore, India
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The World Congress on Nature and Biologically Inspired Computing (NABIC'09) brings together international researchers, developers, practitioners, and users. The aim is to build a 3 day platform where the concerned researchers /academicians /engineers from diverge regions of the world would converge to share their excitement and paradoxically, frustration towards the pursuit of building up of machines that would not be strictly algorithmic in nature and are capable of handling ambiguity, uncertainty etc. by applying common sense.
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| The Eighth International Conference on Machine Learning and Applications (ICMLA) |
2009-12-13 |
2009-12-15 |
2009-07-06 |
2009-09-01 |
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Miami, USA
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The aim of the conference is to bring researchers working
in the areas of machine learning and applications together. The
conference will cover both theoretical and experimental research results. Submission of machine learning papers describing machine learning applications in fields like medicine, biology, industry, manufacturing, security, education, virtual environments, game playing and problem solving is strongly encouraged. |
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| The First International Workshop on Applications of Machine Learning Techniques in Medicine and Biology |
2010-02-10 |
2010-02-15 |
2009-09-10 |
2009-10-12 |
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St. Maarten, Netherlands Antilles
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Machine learning (ML) is an inherently interdisciplinary field, built on concepts from artificial intelligence, cognitive science, probability and statistics, information theory, philosophy, control theory, psychology, neurobiology and other fields. ML techniques have found widespread applications in biology and medicine. Medicine is largely an evidence-driven discipline where large quantities of relatively high-quality data are collected and stored in databases. The medical data are highly heterogeneous and are stored in numerical, text, image, sound and video formats. They include clinical data (symptoms, demographics, biochemical tests, diagnoses and various imaging, video, vital signals, etc), logistics data (charges and costs, policies, guidelines, clinical trials, etc), bibliographical data, and molecular data. Bioinformatics, which concerns the latter type of data, conceptualizes biology in terms of molecules and applies "informatics" techniques, derived from disciplines such as applied mathematics, computer science and statistics to understand and organize the information associated with these molecules on a large scale. In other words, bioinformatics encompasses analysis of molecular data expressed in the form of nucleotides, amino acids, DNA, RNA, pedtides and proteins. The sheer amount and breadth of data requires development of efficient methods for knowledge/information extraction that can cope with the size and complexity of the accumulated data. There are numerous examples of successful applications of machine learning in areas of diagnosis and prevention, prognosis and therapeutic decision making. |
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Papers Due |
Notification Date |
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| NIPS-Workshop on Probabilistic Approaches for Robotics and Control |
2009-12-11 |
2009-12-12 |
2009-10-17 |
2009-10-26 |
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Whistler, Canada
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During the last decade, many areas of Bayesian machine learning have reached a high level of maturity. This has resulted in a variety of theoretically sound and efficient algorithms for learning and inference in the presence of uncertainty. However, in the context of control, robotics, and reinforcement learning, uncertainty has not yet been treated with comparable rigor despite its central role in risk-sensitive control, sensori-motor control, robust control, and cautious control. A consistent treatment of uncertainty is also essential when dealing with stochastic policies, incomplete state information, and exploration strategies.
A typical situation where uncertainty comes into play is when the exact state transition dynamics are unknown and only limited or no expert knowledge is available and/or affordable. One option is to learn a model from data. However, if the model is too far off, this approach can result in arbitrarily bad solutions. This model bias can be sidestepped by the use of flexible model-free methods. The disadvantage of model-free methods is that they do not generalize and often make less efficient use of data. Therefore, they often need more trials than feasible to solve a problem on a real-world system. A probabilistic model could be used for efficient use of data while alleviating model bias by explicitly representing and incorporating uncertainty.
The use of probabilistic approaches requires (approximate) inference algorithms, where Bayesian machine learning can come into play. Although probabilistic modeling and inference conceptually fit into this context, they are not widespread in robotics, control, and reinforcement learning. Hence, this workshop aims to bring researchers together to discuss the need, the theoretical properties, and the practical implications of probabilistic methods in control, robotics, and reinforcement learning.
One particular focus will be on probabilistic reinforcement learning approaches that profit recent developments in optimal control which show that the problem can be substantially simplified if certain structure is imposed. The simplifications include linearity of the (Hamilton-Jacobi) Bellman equation. The duality with Bayesian estimation allow for analytical computation of the optimal control laws and closed form expressions of the optimal value functions. |
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| Thirteenth International Conference on Artificial Intelligence and Statistics |
2010-05-13 |
2010-05-15 |
2009-11-06 |
2010-02-13 |
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Sardinia, Italy
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This is the thirteenth conference on Artificial Intelligence and Statistics (AISTATS*2010), an interdisciplinary gathering of researchers at the intersection of computer science, artificial intelligence, statistics, and related areas. Since its inception the AISTATS conference has been held every two years in North America. At the 2009 conference, with the support of the EU funded PASCAL II Network of Excellence (www.pascal-network.org), the decision was made to bring the conference to Europe for the first time. Starting in 2010 AISTATS will be held every year, alternating the venue between Europe and North America. The Conference Programme will include invited talks, contributed talks, and posters. Contributed talks and posters are selected via a rigorous peer-review process based on 8 page papers. Accepted papers will be published as a special issue in the Journal of Machine Learning Research (JMLR) Workshop and Conference Proceedings Series. |
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| Twenty-Fourth AAAI Conference on Artificial Intelligence |
2010-07-11 |
2010-07-15 |
2009-12-01 |
2010-02-06 |
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Atlanta, USA
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AAAI is delighted to announce that the Twenty-Fourth AAAI Conference on Artificial Intelligence will be held at the Westin Peachtree Plaza in Atlanta, Georgia. The purpose of the AAAI-10 conference is to promote research in AI and scientific exchange among AI researchers, practitioners, scientists, and engineers in related disciplines. |
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| Seventh International Symposium on Neural Networks |
2010-06-06 |
2010-06-09 |
2009-12-01 |
2010-01-01 |
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Shanghai, China
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ISNN 2010 aims to provide a high-level international forum for scientists, engineers, and educators to present the state of the art of neural network research and applications in related fields. The symposium will feature plenary speeches given by world renowned scholars, regular sessions with broad coverage, and special sessions focusing on popular topics.
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| 8th International Symposium on Intelligent Automation and Control |
2010-09-19 |
2010-09-23 |
2009-12-15 |
2010-02-15 |
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Kobe, Japan
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The 2010 World Automation Congress International Symposium on Intelligent Automation and Control will bring together researchers from multiple disciplines to advance the use of artificial intelligence in the control and/or development of automated systems. |
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Start Date |
End Date |
Papers Due |
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| International Conference on Cognitive and Neural Systems Engineering |
2010-05-26 |
2010-05-28 |
2010-01-25 |
2010-02-22 |
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Tokyo, Japan
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The International Conference on Cognitive and Neural Systems Engineering (ICCNSE 2010) aims to bring together researchers, scientists, engineers, and scholar students to exchange and share their experiences, new ideas, and research results about all aspects of Cognitive and Neural Systems Engineering, and discuss the practical challenges encountered and the solutions adopted. |
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| International Conference on Machine Learning (ICML) |
2009-06-21 |
2010-06-24 |
2010-01-26 |
2010-04-06 |
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Haifa, Israel
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The 27th International Conference on Machine Learning (ICML 2010) will be held in Haifa, Israel on June 21-24, 2010. ICML is the leading international machine learning conference, attracting annually some 500 participants from all over the world. ICML is supported by the International Machine Learning Society (IMLS). |
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| 6th IFIP Conference on Artificial Intelligence Applications & Innovations |
2010-10-05 |
2010-10-07 |
2010-02-05 |
2010-04-21 |
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Ayia Napa, Cyprus
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The abundance of information and increase in computing power currently enables researchers to tackle highly complicated and challenging computational problems. Solutions to such problems are now feasible using advances and innovations from the area of Artificial Intelligence. The general focus of the AIAI 2010 conference is to provide insights on how Artificial Intelligence may be applied in real world situations and serve the study, analysis and modelling of theoretical and practical issues. Also, research papers describing advanced prototypes, innovative systems, tools and techniques are encouraged. General survey papers indicating future directions and professional work-in-progress reports are of equal interest. Acceptance will be based on quality, originality and practical merit of the work. |
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| 19th European Conference on Artificial Intelligence - ECAI 2010 |
2010-08-16 |
2010-08-20 |
2010-02-15 |
2010-04-30 |
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Lisbon, Portugal
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ECAI, is the leading Conference on Artificial Intelligence in Europe, and is a biennial organization of the European Coordinating Committee for Artificial Intelligence - ECCAI. |
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| Empirical Evaluations in Reinforcement Learning |
2010-12-26 |
2010-12-27 |
2010-02-26 |
2010-06-30 |
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Machine Learning Journal , (Journal Special Issue)
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The continuing development of a field requires a healthy exchange
between theoretical advances and experimental observations. The
purpose of this special issue is to assess progress in empirical
evaluations of reinforcement-learning algorithms and to encourage the
adoption of effective experimental methodologies. The last several
years have seen new trends in uniform software interfaces between
environments and learning algorithms, community comparisons and
competitions, and an increased interest in experimenting with
reinforcement learning in embedded systems.
The emphasis of the special issue is not on the development of novel
algorithms. Instead, papers will be assessed in terms of the insights
they provide about how best to assess performance in reinforcement
learning, i.e., the "meta" problem of evaluating the evaluation
methodologies themselves. In particular, papers presenting empirical
results should also discuss what those results reveal about the
strengths and weaknesses of the evaluation methodology. Similarly,
papers describing real-life applications should make clear what
limitations the application exposes in 'off-the-shelf' methods, how
the employed method had to be modified to address real-world
complications, and what the results show that could not be learned
from experiments in 'toy' domains. Papers proposing new evaluation
methodologies should include illustrative empirical results offering
insights that would be difficult to obtain with conventional
methodologies. Finally, papers proposing new evaluation methodologies
should also compare and contrast with methodologies in related areas,
e.g. supervised learning, explaining why such methodologies are not
adequate and what ideas, if any, can be borrowed from them. |
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Start Date |
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Papers Due |
Notification Date |
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| IEEE International Workshop on Machine Learning for Signal Processing |
2010-08-29 |
2010-09-01 |
2010-04-01 |
2010-05-28 |
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Kittila, Finland
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Machine learning in multi-dimensional and statistical signal
processing is concerned with tasks such as detection, estimation,
prediction, classification, and optimization. Typical approaches are
modern implementations of supervised, unsupervised, reinforcement
and semi-supervised learning, for instance using probabilistic
modeling and kernel methods.
Machine learning has a wide range of applications: adaptive filtering,
time-series analysis, pattern recognition, image processing,
computer vision, data mining and visualization, information retrieval,
robot control, data fusion, blind source separation, sparse and
structured representations, context modeling, multimodal interfaces,
neuroinformatics, bioinformatics, sensor networks, cognitive radio,
etc. Also, in many applications, hardware implementations are
important due to large amounts of data to be processed and real-time
processing requirements. |
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| 6th International Conference on Intelligent Information Processing |
2010-10-13 |
2010-10-16 |
2010-04-01 |
2010-05-01 |
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Manchester, UK
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The IIP conference series provides a forum for engineers and scientists in academia, university and industry to present their latest research findings in any aspects of intelligent information processing. This time, we especially encourage papers on knowledge discovery, intelligent Web, intelligent agents, machine learning, autonomic reasoning, intelligence science etc. We also welcome papers that highlight successful modern applications of IIP, such as biomedicine, e-Services, e-Learning, business intelligence. IIP2010 attempts to meet the needs of a large and diverse community. |
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| The 21st International Conference on Algorithmic Learning Theory |
2010-10-06 |
2010-10-08 |
2010-05-05 |
2010-06-14 |
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Canberra, Australia
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The 21st International Conference on Algorithmic Learning Theory (ALT 2010) will be held in Canberra, during 6-8 October 2010. The conference is on the theoretical foundations of machine learning. The conference will be co-located with the 13th International Conference on Discovery Science (DS 2010) and after the Machine Learning Summer School to be held at the Australian National University. |
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the university of tennessee
