CALL FOR CONTRIBUTIONS
NIPS 2011 workshop on Challenges in Learning Hierarchical Models: Transfer Learning and Optimization Melia Sierra Nevada & Melia Sol y Nieve, Sierra Nevada, Spain https://sites.google.com/site/nips2011workshop/home Important Dates: ---------------- Deadline for submissions: October 21, 2011 Notification of acceptance: October 28, 2011 Overview: ---------------- The ability to learn abstract representations that support transfer to novel but related tasks lies at the core of solving many AI related tasks, including visual object recognition, information retrieval, speech perception, and language understanding. Hierarchical models that support inferences at multiple levels have been developed and argued as among the most promising candidates for achieving such goal. An important property of these models is that they can extract complex statistical dependencies from high-dimensional sensory input and efficiently learn latent variables by re-using and combining intermediate concepts, allowing these models to generalize well across a wide variety of tasks. In the past few years, researchers across many different communities, from applied statistics to engineering, computer science and neuroscience, have proposed several hierarchical models that are capable of extracting useful, high-level structured representations. The learned representations have been shown to give promising results for solving a multitude of novel learning tasks. A few notable examples of such models include Deep Belief Networks, Deep Boltzmann Machines, sparse coding-based methods, nonparametric and parametric hierarchical Bayesian models. Despite recent successes, many existing hierarchical models are still far from being able to represent, identify and learn the wide variety of possible patterns and structure in real-world data. Existing models can not cope with new tasks for which they have not been specifically trained. Even when applied to related tasks, trained systems often display unstable behavior. Furthermore, massive volumes of training data (e.g., data transferred between tasks) and high-dimensional input spaces pose challenging questions on how to effectively train the deep hierarchical models. The recent availability of large scale datasets (like ImageNet for visual object recognition or Wall Street Journal for large vocabulary speech recognition), the continuous advances in optimization methods, and the availability of cluster computing have drastically changed the working scenario, calling for a re-assessment of the strengths and weaknesses of many existing optimization strategies. The aim of this workshop is to bring together researchers working on such hierarchical models to discuss two important challenges: the ability to perform transfer learning and the best strategies to optimize these systems on large scale problems. These problems are "large" in terms of input dimensionality (in the order of millions), number of training samples (in the order of 100 millions or more) and number of categories (in the order of several tens of thousands). Submission Instructions: ------------------------ We solicit submissions of unpublished research papers. Papers must have at most 6 pages (even in the form of extended abstracts), and must satisfy the formatting instructions of the NIPS 2011 call for papers. Submissions should include the title, authors' names, institutions and email addresses and should be sent in PDF or PS file format by email to transflearn.optim.wnips2...@gmail.com. Submissions will be reviewed by the organizing committee on the basis of relevance, significance, technical quality, and clarity. Selected submissions may be accepted either as an oral presentation or as a poster presentation: there will be a limited number of oral presentations. We encourage submissions with a particular emphasis on: * transfer learning * one-shot learning * learning hierarchical models * scalability of hierarchical models at training and test time * deterministic and stochastic optimization for hierarchical models * parallel computing * theoretical foundations of transfer learning * applications of hierarchical models to large scale datasets Organizers ---------- Quoc V. Le, Computer Science Department, Stanford University Marc'Aurelio Ranzato, Google Inc Ruslan Salakhutdinov, Department of Statistics, University of Toronto Andrew Ng, Computer Science Department, Stanford University Josh Tenenbaum, Department of Brain and Cognitive Sciences, MIT https://sites.google.com/site/nips2011workshop/home _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
