Juergen Schmidhuber

research

∙ 12/21/2022

Annotated History of Modern AI and Deep Learning

Machine learning is the science of credit assignment: finding patterns i...

0 Juergen Schmidhuber, et al. ∙

research

∙ 05/12/2020

Deep Learning: Our Miraculous Year 1990-1991

In 2020, we will celebrate that many of the basic ideas behind the deep ...

38 Juergen Schmidhuber, et al. ∙

research

∙ 12/05/2019

Reinforcement Learning Upside Down: Don't Predict Rewards – Just Map Them to Actions

We transform reinforcement learning (RL) into a form of supervised learn...

0 Juergen Schmidhuber, et al. ∙

research

∙ 06/11/2019

Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization

Generative Adversarial Networks (GANs) learn to model data distributions...

0 Juergen Schmidhuber, et al. ∙

research

∙ 04/30/2018

Investigations on End-to-End Audiovisual Fusion

Audiovisual speech recognition (AVSR) is a method to alleviate the adver...

0 Michael Wand, et al. ∙

research

∙ 02/24/2018

One Big Net For Everything

I apply recent work on "learning to think" (2015) and on PowerPlay (2011...

0 Juergen Schmidhuber, et al. ∙

research

∙ 11/16/2017

Hindsight policy gradients

Goal-conditional policies allow reinforcement learning agents to pursue ...

0 Paulo Rauber, et al. ∙

research

∙ 08/04/2017

Improving Speaker-Independent Lipreading with Domain-Adversarial Training

We present a Lipreading system, i.e. a speech recognition system using o...

0 Michael Wand, et al. ∙

research

∙ 11/30/2015

On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models

This paper addresses the general problem of reinforcement learning (RL) ...

0 Juergen Schmidhuber, et al. ∙

research

∙ 06/24/2015

Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation

Convolutional Neural Networks (CNNs) can be shifted across 2D images or ...

0 Marijn F. Stollenga, et al. ∙

research

∙ 07/11/2014

Deep Networks with Internal Selective Attention through Feedback Connections

Traditional convolutional neural networks (CNN) are stationary and feedf...

0 Marijn Stollenga, et al. ∙

research

∙ 04/30/2014

Deep Learning in Neural Networks: An Overview

In recent years, deep artificial neural networks (including recurrent on...

0 Juergen Schmidhuber, et al. ∙

research

∙ 05/02/2013

Testing Hypotheses by Regularized Maximum Mean Discrepancy

Do two data samples come from different distributions? Recent studies of...

0 Somayeh Danafar, et al. ∙

research

∙ 12/28/2012

A Frequency-Domain Encoding for Neuroevolution

Neuroevolution has yet to scale up to complex reinforcement learning tas...

0 Jan Koutnik, et al. ∙

research

∙ 09/29/2012

Self-Delimiting Neural Networks

Self-delimiting (SLIM) programs are a central concept of theoretical com...

0 Juergen Schmidhuber, et al. ∙

research

∙ 09/26/2012

Efficient Natural Evolution Strategies

Efficient Natural Evolution Strategies (eNES) is a novel alternative to ...

0 Yi Sun, et al. ∙

research

∙ 06/18/2012

On the Size of the Online Kernel Sparsification Dictionary

We analyze the size of the dictionary constructed from online kernel spa...

0 Yi Sun, et al. ∙

research

∙ 02/13/2012

Multi-column Deep Neural Networks for Image Classification

Traditional methods of computer vision and machine learning cannot match...

0 Dan Cireşan, et al. ∙

research

∙ 12/31/2011

T-Learning

Traditional Reinforcement Learning (RL) has focused on problems involvin...

0 Vincent Graziano, et al. ∙

research

∙ 12/09/2011

Incremental Slow Feature Analysis: Adaptive and Episodic Learning from High-Dimensional Input Streams

Slow Feature Analysis (SFA) extracts features representing the underlyin...

0 Varun Raj Kompella, et al. ∙

research

∙ 06/10/2011

A Linear Time Natural Evolution Strategy for Non-Separable Functions

We present a novel Natural Evolution Strategy (NES) variant, the Rank-On...

0 Yi Sun, et al. ∙

research

∙ 03/29/2011

Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments

To maximize its success, an AGI typically needs to explore its initially...

0 Yi Sun, et al. ∙

research

∙ 09/14/2010

Evolution of National Nobel Prize Shares in the 20th Century

We analyze the evolution of cumulative national shares of Nobel Prizes s...

0 Juergen Schmidhuber, et al. ∙

research

∙ 03/01/2010

Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition

Good old on-line back-propagation for plain multi-layer perceptrons yiel...

0 Dan Claudiu Ciresan, et al. ∙

research

∙ 12/23/2008

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...

0 Juergen Schmidhuber, et al. ∙

research

∙ 07/09/2008

Algorithm Selection as a Bandit Problem with Unbounded Losses

Algorithm selection is typically based on models of algorithm performanc...

0 Matteo Gagliolo, et al. ∙

research

∙ 04/21/2008

Phoneme recognition in TIMIT with BLSTM-CTC

We compare the performance of a recurrent neural network with the best r...

0 Santiago Fernández, et al. ∙

research

∙ 09/05/2007

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

I postulate that human or other intelligent agents function or should fu...

0 Juergen Schmidhuber, et al. ∙

research

∙ 09/05/2007

Using Data Compressors to Construct Rank Tests

Nonparametric rank tests for homogeneity and component independence are ...

0 Daniil Ryabko, et al. ∙

research

∙ 08/31/2007

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

When Kurt Goedel layed the foundations of theoretical computer science i...

0 Juergen Schmidhuber, et al. ∙

research

∙ 05/14/2007

Multi-Dimensional Recurrent Neural Networks

Recurrent neural networks (RNNs) have proved effective at one dimensiona...

0 Alex Graves, et al. ∙

research

∙ 06/19/2006

New Millennium AI and the Convergence of History

Artificial Intelligence (AI) has recently become a real formal science: ...

0 Juergen Schmidhuber, et al. ∙

research

∙ 03/07/2006

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

We address the problem of autonomously learning controllers for vision-c...

0 Viktor Zhumatiy, et al. ∙

research

∙ 12/15/2005

Evolino for recurrent support vector machines

Traditional Support Vector Machines (SVMs) need pre-wired finite time wi...

0 Juergen Schmidhuber, et al. ∙

research

∙ 09/25/2003

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

We present the first class of mathematically rigorous, general, fully se...

0 Juergen Schmidhuber, et al. ∙

research

∙ 02/10/2003

The New AI: General & Sound & Relevant for Physics

Most traditional artificial intelligence (AI) systems of the past 50 yea...

0 Juergen Schmidhuber, et al. ∙

research

∙ 07/31/2002

Optimal Ordered Problem Solver

We present a novel, general, optimally fast, incremental way of searchin...

0 Juergen Schmidhuber, et al. ∙

research

∙ 11/28/2001

Gradient-based Reinforcement Planning in Policy-Search Methods

We introduce a learning method called "gradient-based reinforcement plan...

0 Ivo Kwee, et al. ∙

research

∙ 05/15/2001

Market-Based Reinforcement Learning in Partially Observable Worlds

Unlike traditional reinforcement learning (RL), market-based RL is in pr...

0 Ivo Kwee, et al. ∙

research

∙ 11/30/2000

Algorithmic Theories of Everything

The probability distribution P from which the history of our universe is...

0 Juergen Schmidhuber, et al. ∙

research

∙ 04/13/1999

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...

0 Juergen Schmidhuber, et al. ∙

Juergen Schmidhuber

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