$ ./nampi_selector.sh

  > nampi_2018 (v2) <
    nampi_2016 (v1)

$ ./faim_workshop.sh


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Neural Abstract Machines & Program Induction v2 {
 : A Federated Artificial Intelligence Meeting (FAIM) workshop (ICML, IJCAI/ECAI, AAMAS)
 : Stockholm, Sweden
 : July 15th }

> import nampi as np
>
> print(np.abstract)

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 [1], probabilistic programming [2], inductive logic programming [3], reinforcement learning [4], 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 a lot of 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 Differentiable Neural Computers [5], Memory Networks [6], Neural Stacks [7, 8], and Hierarchical Attentive Memory [9], as well as complex differentiable interpreters [10, 11] able to combine differentiable structures with program induction and execution. Simultaneously, neural program induction models like Neural Program Interpreters [12] and Neural Programmer [13] and DeepCoder [14] have created a lot of excitement in the field, promising induction of algorithmic behavior, programs, and enabling inclusion of programming languages in the processes of execution and induction, while staying end-to-end trainable. Trainable program induction models have the potential to make a substantial impact in 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 researchers and practitioners from both academia and industry, in the areas of deep learning, program synthesis, probabilistic programming, programming languages, inductive programming and reinforcement learning, together 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 among 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.

> print(np.call_for_participation)

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 organise a double-blind review with open comments on openreview.net. 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 https://openreview.net/group?id=ICML.cc/2018/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, though they will be posted on the workshop webpage. 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 ICML format.

Full CFP is available here

> print(np.key_dates)

Paper submission deadline: June 8th (extended)
Notification of acceptance: June 23rd
Final Papers Due: June 27th
NAMPI workshop: July 15th
Deadlines are at 11:59pm PDT.

> print(np.area_header)
> for area_of_interest in sorted(np.areas): \
> print("- %s" % area_of_interest)

Areas of interest for discussion and submissions include, but are not limited to:

- Applications
- Compositionality in Representation Learning
- 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 -guided programming
- 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 speaker in np.speakers: \
> print("∘ %s (%s)" % (speaker.name, speaker.affiliation))

> print(np.recording_notification)
> print(np.schedule)

Please note we will upload the recordings* at a later point.

08:50-09:00 Opening Remarks

# 1st talk set
09:00-09:30 Dawn Song: Deep Learning for Program synthesis: Lessons & Open Challenges [slides]
09:30-10:00 Armando Solar-Lezama: Program synthesis and ML join forces [slides]

10:00-10:30 Coffee Break

# 2nd talk set
10:30-11:00 Sumit Gulwani: Programming by Examples: Logical Reasoning meets Machine Learning [slides]
11:00-11:30 Brenden Lake: Program induction for building more human-like machine learning algorithms
11:30-12:00 Satinder Singh: Program Induction and Language: Two Vignettes
12:00-12:30 Oriol Vinyals: Generating Visual Programs with Agents

12:30-14:00 Lunch Break

# 3rd talk set
14:00-14:30 Rishabh Singh: Neural Meta Program Synthesis [slides]
14:30-15:00 Veselin Raychev: Interpretable Probabilistic Models for Code
15:00-15:30 Richard Evans: Differentiable Inductive Logic Programming. [slides]

15:30-15:35 Best Paper Award

15:35-16:50 Poster Session and Coffe Break with mingling refreshments

16:50-18:00 Panel with Sumit Gulwani, Brenden Lake, Percy Liang, Rishabh Singh, Armando Solar-Lezama and Joshua Tenenbaum

> for paper in np.accepted_papers: \
> print("∘ %s (%s)" % (("[BEST PAPER AWARD] " if paper.best else '') + paper.authors, paper.title))


> for paper in np.accepted_extended_abstracts: \
> print("∘ %s (%s)" % (paper.authors, paper.title))


> for organizer in np.organizers: \
> print("∘ %s (%s)" % (organizer.name, organizer.affiliation))

  • Matko Bošnjak (University College London)
  • Pushmeet Kohli (DeepMind)
  • Tejas Kulkarni (DeepMind)
  • Sebastian Riedel (University College London)
  • Tim Rocktäschel (University of Oxford)
  • Dawn Song (UC Berkeley)
  • Rob Zinkov (University of Oxford)

  • > for pc_member in np.pc_members: \
    > print("∘ %s (%s)" % (pc_member.name, pc_member.affiliation))

  • Marc Brockschmidt (Microsoft Research)
  • Ivo Danihelka (DeepMind)
  • Richard Evans (DeepMind)
  • Alexander Gaunt (Microsoft Research)
  • Edward Grefenstette (DeepMind)
  • Caglar Gulcehre (University of Montreal)
  • Lukasz Kaiser (Google Brain)
  • Kristian Kersting (TU Darmstadt)
  • Pasquale Minervini (University College London)
  • Sameer Singh (UC Irvine)
  • Luke Zettlemoyer (University of Washington)

  • > for sponsor in np.sponsors: \
    > Image.open(sponsor.logo).show()






    > raise np.TravelBursaryException('Travel Bursaries available!')

    Thanks to our generous sponsors§, we will be able to support a few travel bursaries. Preference will be given to (student) authors of admitted papers. For more details and the application drop us a line at nampi@googlegroups.com.

    > for i, reference in enumerate(np.references): \
    > print("[%d] %s" % (i + 1, reference))

    [1] Manna, Zohar, and Richard Waldinger. "A deductive approach to program synthesis." ACM Transactions on Programming Languages and Systems (TOPLAS) 2.1 (1980): 90-121.
    [2] McCallum, Andrew, Karl Schultz, and Sameer Singh. "Factorie: Probabilistic programming via imperatively defined factor graphs." Advances in Neural Information Processing Systems. (2009)
    [3] Muggleton, Stephen, and Luc De Raedt. "Inductive logic programming: Theory and methods." The Journal of Logic Programming 19 (1994): 629-679.
    [4] Sutton, Richard S., and Andrew G. Barto. "Reinforcement learning: An introduction." Vol. 1. No. 1. Cambridge: MIT press, (1998)
    [5] Graves, Alex, Greg Wayne, and Ivo Danihelka. "Neural turing machines." arXiv preprint arXiv:1410.5401 (2014).
    [6] Weston, Jason, Sumit Chopra, and Antoine Bordes. "Memory networks." International Conference on Learning Representations (2014).
    [7] Grefenstette, Edward, et al. "Learning to transduce with unbounded memory." Advances in Neural Information Processing Systems. (2015)
    [8] Joulin, Armand, and Tomas Mikolov. "Inferring algorithmic patterns with stack-augmented recurrent nets." Advances in Neural Information Processing Systems. (2015)
    [9] Andrychowicz, Marcin, and Karol Kurach. "Learning Efficient Algorithms with Hierarchical Attentive Memory." arXiv preprint arXiv:1602.03218 (2016).
    [10] Bošnjak, Matko, et al. "Programming With a Differentiable Forth Interpreter." International Conference on Machine Learning, (2016)
    [11] Gaunt, Alexander L., et al. "Terpret: A probabilistic programming language for program induction." arXiv preprint arXiv:1608.04428 (2016).
    [12] Reed, Scott, and Nando de Freitas. "Neural programmer-interpreters." International Conference on Learning Representations (2016).
    [13] Neelakantan, Arvind, Quoc V. Le, and Ilya Sutskever. "Neural programmer: Inducing latent programs with gradient descent." International Conference on Learning Representations (2016).
    [13] Balog, Matej, et al. "Deepcoder: Learning to write programs." arXiv preprint arXiv:1611.01989 (2016).


    * recording sponsored by DeepMind and Bloomsbury AI
    † mingling session sponsored by UCL Computer Science
    ‡ best paper award sponsored by DeepCode
    § travel bursaries sponsored by NEAR and Bloomsbury AI