NIPS 2015 Call for Papers

• 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 program-chairs@nips.cc. 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.

• 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.