IDSIA

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Dalle Molle Institute of Artificial Intelligence Studies is a non-profit oriented research institute for artificial intelligenc.

Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control

The Differentiable Neural Computer (DNC) can learn algorithmic and quest...
04/23/2019 ∙ by Róbert Csordás, et al. ∙ 18 ∙ share

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Learning to Reason with Third-Order Tensor Products

We combine Recurrent Neural Networks with Tensor Product Representations...
11/29/2018 ∙ by Imanol Schlag, et al. ∙ 12 ∙ share

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R-SQAIR: Relational Sequential Attend, Infer, Repeat

Traditional sequential multi-object attention models rely on a recurrent...
10/11/2019 ∙ by Aleksandar Stanić, et al. ∙ 5 ∙ share

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Are Disentangled Representations Helpful for Abstract Visual Reasoning?

A disentangled representation encodes information about the salient fact...
05/29/2019 ∙ by Sjoerd van Steenkiste, et al. ∙ 3 ∙ share

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Discovering Boolean Gates in Slime Mould

Slime mould of Physarum polycephalum is a large cell exhibiting rich spa...
07/07/2016 ∙ by Simon Harding, et al. ∙ 0 ∙ share

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Highway and Residual Networks learn Unrolled Iterative Estimation

The past year saw the introduction of new architectures such as Highway ...
12/22/2016 ∙ by Klaus Greff, et al. ∙ 0 ∙ share

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A Clockwork RNN

Sequence prediction and classification are ubiquitous and challenging pr...
02/14/2014 ∙ by Jan Koutnik, et al. ∙ 0 ∙ share

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Binding via Reconstruction Clustering

Disentangled distributed representations of data are desirable for machi...
11/19/2015 ∙ by Klaus Greff, et al. ∙ 0 ∙ share

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Descriptor learning for omnidirectional image matching

Feature matching in omnidirectional vision systems is a challenging prob...
12/29/2011 ∙ by Jonathan Masci, et al. ∙ 0 ∙ share

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Object Recognition with Multi-Scale Pyramidal Pooling Networks

We present a Multi-Scale Pyramidal Pooling Network, featuring a novel py...
07/07/2012 ∙ by Jonathan Masci, et al. ∙ 0 ∙ share

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Multimodal similarity-preserving hashing

We introduce an efficient computational framework for hashing data belon...
07/06/2012 ∙ by Jonathan Masci, et al. ∙ 0 ∙ share

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Tagger: Deep Unsupervised Perceptual Grouping

We present a framework for efficient perceptual inference that explicitl...
06/21/2016 ∙ by Klaus Greff, et al. ∙ 0 ∙ share

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LSTM: A Search Space Odyssey

Several variants of the Long Short-Term Memory (LSTM) architecture for r...
03/13/2015 ∙ by Klaus Greff, et al. ∙ 0 ∙ share

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Neural Expectation Maximization

Many real world tasks such as reasoning and physical interaction require...
08/11/2017 ∙ by Klaus Greff, et al. ∙ 0 ∙ share

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Measuring Intelligence through Games

Artificial general intelligence (AGI) refers to research aimed at tackli...
09/06/2011 ∙ by Tom Schaul, et al. ∙ 0 ∙ share

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A Learning Framework for Morphological Operators using Counter-Harmonic Mean

We present a novel framework for learning morphological operators using ...
12/11/2012 ∙ by Jonathan Masci, et al. ∙ 0 ∙ share

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Natural Evolution Strategies

This paper presents Natural Evolution Strategies (NES), a recent family ...
06/22/2011 ∙ by Daan Wierstra, et al. ∙ 0 ∙ share

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Multi-Dimensional Recurrent Neural Networks

Recurrent neural networks (RNNs) have proved effective at one dimensiona...
05/14/2007 ∙ by Alex Graves, et al. ∙ 0 ∙ share

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Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity & Creativity

I postulate that human or other intelligent agents function or should fu...
09/05/2007 ∙ by Juergen Schmidhuber, et al. ∙ 0 ∙ share

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Market-Based Reinforcement Learning in Partially Observable Worlds

Unlike traditional reinforcement learning (RL), market-based RL is in pr...
05/15/2001 ∙ by Ivo Kwee, et al. ∙ 0 ∙ share

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Optimal Ordered Problem Solver

We present a novel, general, optimally fast, incremental way of searchin...
07/31/2002 ∙ by Juergen Schmidhuber, et al. ∙ 0 ∙ share

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Hindsight policy gradients

Goal-conditional policies allow reinforcement learning agents to pursue ...
11/16/2017 ∙ by Paulo Rauber, et al. ∙ 0 ∙ share

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Deep Learning in Neural Networks: An Overview

In recent years, deep artificial neural networks (including recurrent on...
04/30/2014 ∙ by Juergen Schmidhuber, et al. ∙ 0 ∙ share

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Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements

We present the first class of mathematically rigorous, general, fully se...
09/25/2003 ∙ by Juergen Schmidhuber, et al. ∙ 0 ∙ share

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A Computer Scientist's View of Life, the Universe, and Everything

Is the universe computable? If so, it may be much cheaper in terms of in...
04/13/1999 ∙ by Juergen Schmidhuber, et al. ∙ 0 ∙ share

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Algorithmic Theories of Everything

The probability distribution P from which the history of our universe is...
11/30/2000 ∙ by Juergen Schmidhuber, et al. ∙ 0 ∙ share

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New Millennium AI and the Convergence of History

Artificial Intelligence (AI) has recently become a real formal science: ...
06/19/2006 ∙ by Juergen Schmidhuber, et al. ∙ 0 ∙ share

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2006: Celebrating 75 years of AI - History and Outlook: the Next 25 Years

When Kurt Goedel layed the foundations of theoretical computer science i...
08/31/2007 ∙ by Juergen Schmidhuber, et al. ∙ 0 ∙ share

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The New AI: General & Sound & Relevant for Physics

Most traditional artificial intelligence (AI) systems of the past 50 yea...
02/10/2003 ∙ by Juergen Schmidhuber, et al. ∙ 0 ∙ share

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Deep Networks with Internal Selective Attention through Feedback Connections

Traditional convolutional neural networks (CNN) are stationary and feedf...
07/11/2014 ∙ by Marijn Stollenga, et al. ∙ 0 ∙ share

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Improving Speaker-Independent Lipreading with Domain-Adversarial Training

We present a Lipreading system, i.e. a speech recognition system using o...
08/04/2017 ∙ by Michael Wand, et al. ∙ 0 ∙ share

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Evolino for recurrent support vector machines

Traditional Support Vector Machines (SVMs) need pre-wired finite time wi...
12/15/2005 ∙ by Juergen Schmidhuber, et al. ∙ 0 ∙ share

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Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot

We address the problem of autonomously learning controllers for vision-c...
03/07/2006 ∙ by Viktor Zhumatiy, et al. ∙ 0 ∙ share

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Using Data Compressors to Construct Rank Tests

Nonparametric rank tests for homogeneity and component independence are ...
09/05/2007 ∙ by Daniil Ryabko, et al. ∙ 0 ∙ share

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Gradient-based Reinforcement Planning in Policy-Search Methods

We introduce a learning method called "gradient-based reinforcement plan...
11/28/2001 ∙ by Ivo Kwee, et al. ∙ 0 ∙ share

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A Linear Time Natural Evolution Strategy for Non-Separable Functions

We present a novel Natural Evolution Strategy (NES) variant, the Rank-On...
06/10/2011 ∙ by Yi Sun, et al. ∙ 0 ∙ share

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Efficient Natural Evolution Strategies

Efficient Natural Evolution Strategies (eNES) is a novel alternative to ...
09/26/2012 ∙ by Yi Sun, et al. ∙ 0 ∙ share

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Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, S

I argue that data becomes temporarily interesting by itself to some self...
12/23/2008 ∙ by Juergen Schmidhuber, et al. ∙ 0 ∙ share

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Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables

We present a novel approach for estimating conditional probability table...
08/23/2017 ∙ by L. Azzimonti, et al. ∙ 0 ∙ share

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Fitness Uniform Optimization

In evolutionary algorithms, the fitness of a population increases with t...
10/20/2006 ∙ by Marcus Hutter, et al. ∙ 0 ∙ share

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Fitness Uniform Deletion: A Simple Way to Preserve Diversity

A commonly experienced problem with population based optimisation method...
04/11/2005 ∙ by Shane Legg, et al. ∙ 0 ∙ share

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Statistical comparison of classifiers through Bayesian hierarchical modelling

Usually one compares the accuracy of two competing classifiers via null ...
09/28/2016 ∙ by Giorgio Corani, et al. ∙ 0 ∙ share

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Desirability and the birth of incomplete preferences

We establish an equivalence between two seemingly different theories: on...
06/01/2015 ∙ by Marco Zaffalon, et al. ∙ 0 ∙ share

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Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis

The machine learning community adopted the use of null hypothesis signif...
06/14/2016 ∙ by Alessio Benavoli, et al. ∙ 0 ∙ share

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Advances in Learning Bayesian Networks of Bounded Treewidth

This work presents novel algorithms for learning Bayesian network struct...
06/05/2014 ∙ by Siqi Nie, et al. ∙ 0 ∙ share

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State Space representation of non-stationary Gaussian Processes

The state space (SS) representation of Gaussian processes (GP) has recen...
01/07/2016 ∙ by Alessio Benavoli, et al. ∙ 0 ∙ share

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Should we really use post-hoc tests based on mean-ranks?

The statistical comparison of multiple algorithms over multiple data set...
05/09/2015 ∙ by Alessio Benavoli, et al. ∙ 0 ∙ share

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New Error Bounds for Solomonoff Prediction

Solomonoff sequence prediction is a scheme to predict digits of binary s...
12/13/1999 ∙ by Marcus Hutter, et al. ∙ 0 ∙ share

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High Degree Sum of Squares Proofs, Bienstock-Zuckerberg hierarchy and Chvatal-Gomory cuts

Chvatal-Gomory (CG) cuts and the Bienstock-Zuckerberg hierarchy capture ...
09/22/2017 ∙ by Monaldo Mastrolilli, et al. ∙ 0 ∙ share

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When the Optimum is also Blind: a New Perspective on Universal Optimization

Consider the following variant of the set cover problem. We are given a ...
07/06/2017 ∙ by Marek Adamczyk, et al. ∙ 0 ∙ share

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