第三十届神经信息处理系统年会 2016 The Thirtieth Annual Conference on Neural Information Processing Systems ICICIP

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Note that the submission deadline is earlier than last year, it is already on May 20. All submissions will be made in PDF format. Papers are limited to eight pages, including figures and tables, in the NIPS style. An additional ninth page containing only cited references is allowed. Final papers will be due in advance of the conference. However, minor changes such as typos and additional references will still be allowed for a certain period after the conference.

Supplementary Material: Authors can submit up to 10 MB of material, containing proofs, audio, images, video, data or source code. Looking at any supplementary material is up to the discretion of the reviewers.

Reviewing: Reviewing will be double-blind: the reviewers will not know the identities of the authors. It will be up to the authors to ensure the proper anonymization of their paper. Prior submissions on arXiv.org are permitted. The reviewers will be asked not to actively look for such submissions, but if they are aware of them, this will not constitute a conflict of interest. The anonymous reviews and meta-reviews of accepted papers will be made public after the end of the review process.

Reviewing by authors: To better distribute the reviewing load and to make the reviewing process more transparent, we request that for each submission at least one of the authors volunteers as a reviewer. The authors can choose during the online submission process who among them takes on that duty. More details on this procedure will be posted on the NIPS 2016 webpage.

Evaluation Criteria: Submissions which are not within the scope of NIPS (see Technical Areas) or are already published elsewhere (see Dual Submission Policy) may be rejected by the Area Chairs without further review. Submission which have a fatal flaw(s) revealed by the reviewers, which may include (without limitation) wrong proofs or flawed or insufficient wet-lab, hardware or software experiments, may be rejected on that basis, without taking into consideration other criteria. Submissions passing the previous steps will be judged on the basis of technical quality, novelty, potential impact, and clarity.

Typical NIPS papers often but not always consist of a mix of algorithmic, theoretical and experimental results, in varying proportions. However, while theoretically grounded arguments are certainly welcome, it is counterproductive to add "decorative maths" whose only purpose is to make the paper look more substantial or even intimidating, without adding relevant insights. Algorithmic contributions should have at least an illustration of how the algorithm can eventually materialize into a machine learning application.

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2016-05-20

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征稿范围

Technical Areas: Papers are solicited in all areas of neural information processing and statistical learning, including, but not limited to:

Neuroscience, cognitive science, and brain imaging: Theoretical and experimental studies of processing and transmission of information in biological neurons and networks, including spike train generation, synaptic modulation, plasticity and adaptation. Neuroimaging, cognitive neuroscience, connectomics, brain mapping, brain segmentation, brain computer interfaces, theoretical, computational, or experimental studies of perception, psychophysics, human or animal learning, memory, reasoning, problem solving, and neuropsychology.
Algorithms and Architectures: Statistical learning algorithms, kernel methods, graphical models, Gaussian processes, Bayesian methods, neural networks, deep learning, dimensionality reduction and manifold learning, hyper-parameter and model selection, combinatorial optimization, relational and structured learning, Markov decision processes, reinforcement Learning, dynamical systems, recurrent networks.
Learning Theory: Models of learning and generalization, regularization and model selection, large deviations and asymptotic analysis, Bayesian learning, spaces of functions and kernels, statistical physics of learning, online learning and competitive analysis, computational complexity, hardness of learning and approximations, statistical theory, control theory, information theory.
Applications: Innovative applications that use machine learning, including systems for time series prediction, bioinformatics, systems biology, text/web analysis, multimedia processing, robotics, natural language processing, decision and control, exploration, planning, navigation, game playing, multi-agent coordination, speech, image, and signal processing, coding, synthesis, denoising, segmentation, source separation, auditory perception, psychoacoustics, other aspects of computer vision, object detection and recognition, motion detection and tracking, visual psychophysics, visual scene analysis and interpretation.
Data, competitions, implementations and software tools: Datasets or data repositories, benchmarks, competitions or challenges and software toolkits.