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 Neural Abstract Machines & Program Induction (NAMPI), NIPS 2016
 When: December 10, 2016
 Where: CCIB, Barcelona, Spain
 Web: https://uclmr.github.io/nampi/
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~ DESCRIPTION

Machine intelligence capable of learning complex procedural behavior, inducing (latent) programs, and reasoning with these programs is a key to solving artificial intelligence. The problems of learning procedural behavior and program induction have been studied from different perspectives in many computer science fields such as program synthesis, probabilistic programming, inductive logic programming, reinforcement learning, and recently in deep learning. However, despite the common goal, there seems to be little communication and collaboration between the different fields focused on this problem.

Recently, there have been many success stories in the deep learning community related to learning neural networks capable of using trainable memory abstractions. This has led to the development of neural networks with differentiable data structures such as Neural Turing Machines, Memory Networks, Neural Stacks, and Hierarchical Attentive Memory, among others. Simultaneously, neural program induction models like Neural Program-Interpreters and the Neural Programmer have created much excitement in the field, promising induction of algorithmic behavior and enabling the inclusion of programming languages in the processes of execution and induction, while remaining trainable end-to-end. Trainable program induction models have the potential to make a substantial impact on many problems involving long-term memory, reasoning, and procedural execution, such as question answering, dialog, and robotics.

The aim of the NAMPI workshop is to bring together researchers and practitioners from both academia and industry, in the areas of deep learning, program synthesis, probabilistic programming, inductive programming and reinforcement learning, to exchange ideas on the future of program induction with a special focus on neural network models and abstract machines. Through this workshop, we look to identify common challenges, exchange ideas and lessons learned from the different fields, as well as establish a (set of) standard evaluation benchmark(s) for approaches that learn with abstraction and/or reason with induced programs.

~ CALL_FOR_PAPERS

We invite authors to submit papers on topics related to machine learning -enabled abstract machines, and learning approaches to program and code induction, including, but not limiting to:
- Applications of Machine Learning -Based Program Induction
- Compositionality in Representation Learning for Program Induction
- Differentiable Memory
- Differentiable Data Structures
- Function and (sub-)Program Compositionality
- Inductive Logic Programming
- Knowledge Representation in Neural Abstract Structures
- Large-scale Program Induction
- Machine Learning for End-user Development
- Meta-Learning and Self-improving
- Neural Abstract Machines
- Optimisation methods for Program Induction
- Program Induction: Datasets, Tasks, and Evaluation
- Program Synthesis
- Probabilistic Programming
- Reinforcement Learning for Program Induction
- Semantic Parsing for Program Induction

We encourage visionary and position papers, as well as work-in-progress submissions. We also accept previously published papers and cross-submissions, but will not include them in the workshop proceedings.

Standard Workshop Paper

The submissions for regular workshop papers should be substantially original and novel. They should not exceed more than 4 pages, excluding references. The submissions should be anonymised as we will strive to organise a double-blind review with open comments on beta.openreview.net. In case double-blind review will not be available upon time of submission, we will do a single-blind review. Authors will be expected to present a poster, and the camera-ready versions must be uploaded to arXiv, as it will be displayed on the workshop webpage. The papers should be submitted to http://openreview.net/group?id=NIPS.cc/2016/workshop/NAMPI

Work-in-progress & cross-submissions

Preliminary work and cross-submissions can be submitted as a 2 page extended abstract. The authors will be expected to present a poster, however these papers do not count as NAMPI workshop papers and will not be included in the workshop proceedings. Interested authors should submit their extended abstracts to nampi@googlegroups.com. Papers in this category do not need to be anonymised and their selection will be determined at the discretion of the organising committee.
All submissions should be typeset in NIPS format.

~ KEY_DATES

[EXTENDED] Paper submission deadline: October 30th
[EXTENDED] Notification of acceptance: November 22nd
Final Papers Due: December 1st
NAMPI workshop: December 10th
Deadlines are at 11:59pm PDT.

~ INTVITED_SPEAKERS

* Rob Fergus (Facebook AI Research and New York University)
* Alex Graves (Google DeepMind)
* Edward Grefenstette (Google DeepMind)
* Percy Liang (Stanford University)
* Stephen Muggleton (Imperial College London)
* Doina Precup (McGill University)
* Jürgen Schmidhuber (IDSIA)
* Charles Sutton (University of Edinburgh)
* Daniel Tarlow (Microsoft Research)
* Joshua Tenenbaum (Massachusetts Institute of Technology)
* Martin Vechev (ETH, Zurich)

~ ORGANISERS

* Matko Bošnjak (University College London)
* Nando de Freitas (University of Oxford and Google DeepMind)
* Tejas Kulkarni (Massachusetts Institute of Technology and Google DeepMind)
* Arvind Neelakantan (University of Massachusetts Amherst)
* Scott Reed (Google DeepMind)
* Sebastian Riedel (University College London)
* Tim Rocktäschel (University College London)

~ PROGRAM COMMITTEE MEMBERS


~ SPONSORS