NIPS 2015 Call for Papers
Palais des Congrès de Montréal, Montréal CANADA
Mon Dec 7th through Sat the 12th
Deadline for Paper Submissions:
Fri Jun 05, 2015 23:00 PM UTC.
Submit at: https://cmt.research.microsoft.com/NIPS2015/
Submissions are solicited for the Annual Conference on Neural Information Processing Systems, an interdisciplinary conference that brings together researchers in all aspects of neural and statistical information processing and computation, and their applications. The conference is a highly selective, single track meeting that includes oral and poster presentations of refereed papers as well as invited talks. The 2015 conference will be held on December 7-10 at Montreal Convention Center, Montreal, Canada. One day of tutorials (December 7) will precede the main conference, and two days of workshops (December 11-12) will follow the conference at the same location.
Submission process: Electronic submissions will be accepted until Friday, June 5, 2015, 11 pm Universal Time (4 pm Pacific Daylight Time). As was the case last year, 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.
Reviewing: Reviewing will be double-blind: the reviewers will not know the identities of the authors. The anonymous reviews and meta-reviews of accepted papers will be made public after the end of the review process.
Evaluation Criteria: Submissions will be refereed on the basis of technical quality, novelty, potential impact, and
Dual Submissions Policy: Submissions that are identical (or substantially similar) to versions that have been previously published, or accepted for publication, or that have been submitted in parallel to other conferences are not appropriate for NIPS and violate our dual submission policy. Exceptions to this rule are the following:
- Submission is permitted of a short version of a paper that has been submitted to a journal, but has not yet been published in that journal. Authors must declare such dual-submissions either through the CMT submission form, or via email to the program chairs at firstname.lastname@example.org. It is the authors' responsibility to make sure that the journal in question allows dual concurrent submissions to conferences. Submission is permitted for papers presented or to be presented at conferences or workshops without proceedings, or with only abstracts published.
- Previously published papers with substantial overlap written by the authors must be cited so as to preserve author anonymity (e.g. "the authors of 1 prove that ..."). Differences relative to these earlier papers must be explained in the text of the submission.
- It is acceptable to submit to NIPS 2015 work that has been made available as a technical report (or similar, e.g. in arXiv) without citing it. While this could compromise the authors' anonymity, reviewers will be asked to refrain from actively searching for the authors' identity or disclose to the area chairs if their identity is known to them.
- The dual-submission rules apply during the NIPS review period which begins June 5 and ends September 4, 2015.
Submission Instructions: All submissions will be made electronically, 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. Please refer to the complete submission and formatting instructions and to the style files for further details.
Supplementary Material: Authors can submit up to 10 MB of material, containing proofs, audio, images, video, data or source code. Note that the reviewers and the program committee reserve the right to judge the paper solely on the basis of the 9 pages of the paper; looking at any extra material is up to the discretion of the reviewers and is not required.
Technical Areas: Papers are solicited in all areas of neural information processing and statistical learning, including, but not limited to:
- Algorithms and Architectures: statistical learning algorithms, kernel methods, graphical models, Gaussian processes, Bayesian methods, neural networks, deep learning, dimensionality reduction and manifold learning, model selection, combinatorial optimization, relational and structured learning.
- Applications: innovative applications that use machine learning, including systems for time series prediction, bioinformatics, systems biology, text/web analysis, multimedia processing, and robotics.
- Brain Imaging: neuroimaging, cognitive neuroscience, EEG (electroencephalogram), ERP (event related potentials), MEG (magnetoencephalogram), fMRI (functional magnetic resonance imaging), brain mapping, brain segmentation, brain computer interfaces.
- Cognitive Science and Artificial Intelligence: theoretical, computational, or experimental studies of perception, psychophysics, human or animal learning, memory, reasoning, problem solving, natural language processing, and neuropsychology.
- Control and Reinforcement Learning: decision and control, exploration, planning, navigation, Markov decision processes, game playing, multi-agent coordination, computational models of classical and operant conditioning.
- Hardware Technologies: analog and digital VLSI, neuromorphic engineering, computational sensors and actuators, microrobotics, bioMEMS, neural prostheses, photonics, molecular and quantum computing.
- Learning Theory: generalization, regularization and model selection, Bayesian learning, spaces of functions and kernels, statistical physics of learning, online learning and competitive analysis, hardness of learning and approximations, statistical theory, large deviations and asymptotic analysis, information theory.
- Neuroscience: theoretical and experimental studies of processing and transmission of information in biological neurons and networks, including spike train generation, synaptic modulation, plasticity and adaptation.
- Speech and Signal Processing: recognition, coding, synthesis, denoising, segmentation, source separation, auditory perception, psychoacoustics, dynamical systems, recurrent networks, language models, dynamic and temporal models.
- Visual Processing: biological and machine vision, image processing and coding, segmentation, object detection and recognition, motion detection and tracking, visual psychophysics, visual scene analysis and interpretation.
Demonstrations and Workshops: There is a separate Demonstration track at NIPS. Authors wishing to submit to the Demonstration track should consult the upcoming Call for Demonstrations.
The workshops will be held at the Montreal Convention Center December 11-12. The upcoming call for workshop proposals will provide details.