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1 code implementation • 26 Feb 2024 • Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber

Various human-designed prompt engineering techniques have been proposed to improve problem solvers based on Large Language Models (LLMs), yielding many disparate code bases.

1 code implementation • 13 Dec 2023 • Róbert Csordás, Piotr Piękos, Kazuki Irie, Jürgen Schmidhuber

The costly self-attention layers in modern Transformers require memory and compute quadratic in sequence length.

1 code implementation • 1 Dec 2023 • Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber

General-purpose learning systems should improve themselves in open-ended fashion in ever-changing environments.

no code implementations • 14 Nov 2023 • Lukas Tuggener, Thilo Stadelmann, Jürgen Schmidhuber

Humans and animals recognize objects irrespective of the beholder's point of view, which may drastically change their appearances.

1 code implementation • 13 Nov 2023 • Joonsu Gha, Vincent Herrmann, Benjamin Grewe, Jürgen Schmidhuber, Anand Gopalakrishnan

Our novel MusicSlots method adapts SlotAttention to the audio domain, to achieve unsupervised music decomposition.

1 code implementation • 24 Oct 2023 • Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber

Recent studies of the computational power of recurrent neural networks (RNNs) reveal a hierarchy of RNN architectures, given real-time and finite-precision assumptions.

2 code implementations • 16 Oct 2023 • Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber

Unlike prior work that compares MoEs with dense baselines under the compute-equal condition, our evaluation condition is parameter-equal, which is crucial to properly evaluate LMs.

1 code implementation • 20 Sep 2023 • Aleksandar Stanić, Dylan Ashley, Oleg Serikov, Louis Kirsch, Francesco Faccio, Jürgen Schmidhuber, Thomas Hofmann, Imanol Schlag

We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours.

1 code implementation • ICCV 2023 • Haozhe Liu, Mingchen Zhuge, Bing Li, Yuhui Wang, Francesco Faccio, Bernard Ghanem, Jürgen Schmidhuber

Recent work on deep reinforcement learning (DRL) has pointed out that algorithmic information about good policies can be extracted from offline data which lack explicit information about executed actions.

1 code implementation • 1 Aug 2023 • Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Ceyao Zhang, Jinlin Wang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, Jürgen Schmidhuber

Remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs).

Ranked #7 on Code Generation on HumanEval

1 code implementation • 30 May 2023 • Kazuki Irie, Anand Gopalakrishnan, Jürgen Schmidhuber

To scale to such challenging tasks, we focus on certain well-known neural architectures with element-wise recurrence, allowing for tractable RTRL without approximation.

no code implementations • 26 May 2023 • Mingchen Zhuge, Haozhe Liu, Francesco Faccio, Dylan R. Ashley, Róbert Csordás, Anand Gopalakrishnan, Abdullah Hamdi, Hasan Abed Al Kader Hammoud, Vincent Herrmann, Kazuki Irie, Louis Kirsch, Bing Li, Guohao Li, Shuming Liu, Jinjie Mai, Piotr Piękos, Aditya Ramesh, Imanol Schlag, Weimin Shi, Aleksandar Stanić, Wenyi Wang, Yuhui Wang, Mengmeng Xu, Deng-Ping Fan, Bernard Ghanem, Jürgen Schmidhuber

What should be the social structure of an NLSOM?

1 code implementation • NeurIPS 2023 • Aleksandar Stanić, Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber

Current state-of-the-art object-centric models use slots and attention-based routing for binding.

no code implementations • 9 May 2023 • Imanol Schlag, Sainbayar Sukhbaatar, Asli Celikyilmaz, Wen-tau Yih, Jason Weston, Jürgen Schmidhuber, Xian Li

In recent years, large pre-trained language models (LLMs) have demonstrated the ability to follow instructions and perform novel tasks from a few examples.

1 code implementation • 2 May 2023 • Kazuki Irie, Jürgen Schmidhuber

Few-shot learning with sequence-processing neural networks (NNs) has recently attracted a new wave of attention in the context of large language models.

1 code implementation • 15 Feb 2023 • Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber

Unsupervised learning of discrete representations from continuous ones in neural networks (NNs) is the cornerstone of several applications today.

1 code implementation • 30 Jan 2023 • Deyao Zhu, Yuhui Wang, Jürgen Schmidhuber, Mohamed Elhoseiny

In this paper, we investigate the potential of using action-free offline datasets to improve online reinforcement learning, name this problem Reinforcement Learning with Action-Free Offline Pretraining (AFP-RL).

no code implementations • 29 Dec 2022 • Louis Kirsch, Jürgen Schmidhuber

We discuss the relationship of such systems to in-context and memory-based meta learning and show that self-referential neural networks require functionality to be reused in the form of parameter sharing.

no code implementations • 29 Dec 2022 • Vincent Herrmann, Louis Kirsch, Jürgen Schmidhuber

There are two important things in science: (A) Finding answers to given questions, and (B) Coming up with good questions.

1 code implementation • 22 Nov 2022 • Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad, Jürgen Schmidhuber

We then demonstrate how evolutionary algorithms can leverage this to extract a set of narrative templates and how these templates -- in tandem with a novel curve-fitting algorithm we introduce -- can reorder music albums to automatically induce stories in them.

1 code implementation • 18 Nov 2022 • Aditya Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber

Furthermore, RC-GVF significantly outperforms previous methods in the absence of ground-truth episodic counts in the partially observable MiniGrid environments.

no code implementations • 17 Nov 2022 • Kazuki Irie, Jürgen Schmidhuber

Short-term memory in standard, general-purpose, sequence-processing recurrent neural networks (RNNs) is stored as activations of nodes or "neurons."

1 code implementation • 4 Nov 2022 • Kenny Young, Aditya Ramesh, Louis Kirsch, Jürgen Schmidhuber

First, we provide a simple theorem motivating how learning a model as an intermediate step can narrow down the set of possible value functions more than learning a value function directly from data using the Bellman equation.

Model-based Reinforcement Learning
reinforcement-learning
**+1**

1 code implementation • 12 Oct 2022 • Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber

While the original CTL is used to test length generalization or productivity, CTL++ is designed to test systematicity of NNs, that is, their capability to generalize to unseen compositions of known functions.

1 code implementation • 7 Oct 2022 • Kazuki Irie, Jürgen Schmidhuber

Work on fast weight programmers has demonstrated the effectiveness of key/value outer product-based learning rules for sequentially generating a weight matrix (WM) of a neural net (NN) by another NN or itself.

1 code implementation • 5 Aug 2022 • Aleksandar Stanić, Yujin Tang, David Ha, Jürgen Schmidhuber

We show that current agents struggle to generalize, and introduce novel object-centric agents that improve over strong baselines.

1 code implementation • 4 Jul 2022 • Francesco Faccio, Aditya Ramesh, Vincent Herrmann, Jean Harb, Jürgen Schmidhuber

In continuous control problems with infinitely many states, our value function minimizes its prediction error by simultaneously learning a small set of `probing states' and a mapping from actions produced in probing states to the policy's return.

1 code implementation • 4 Jul 2022 • Francesco Faccio, Vincent Herrmann, Aditya Ramesh, Louis Kirsch, Jürgen Schmidhuber

A form of weight-sharing HyperNetworks and policy embeddings scales our method to generate deep NNs.

2 code implementations • 3 Jun 2022 • Kazuki Irie, Francesco Faccio, Jürgen Schmidhuber

Neural ordinary differential equations (ODEs) have attracted much attention as continuous-time counterparts of deep residual neural networks (NNs), and numerous extensions for recurrent NNs have been proposed.

1 code implementation • 13 May 2022 • Miroslav Štrupl, Francesco Faccio, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh Kumar Srivastava

Upside-Down Reinforcement Learning (UDRL) is an approach for solving RL problems that does not require value functions and uses only supervised learning, where the targets for given inputs in a dataset do not change over time.

1 code implementation • 25 Mar 2022 • Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber, Sjoerd van Steenkiste

The discovery of reusable sub-routines simplifies decision-making and planning in complex reinforcement learning problems.

1 code implementation • 24 Feb 2022 • Kai Arulkumaran, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh K. Srivastava

Upside down reinforcement learning (UDRL) flips the conventional use of the return in the objective function in RL upside down, by taking returns as input and predicting actions.

no code implementations • 23 Feb 2022 • Dylan R. Ashley, Kai Arulkumaran, Jürgen Schmidhuber, Rupesh Kumar Srivastava

Lately, there has been a resurgence of interest in using supervised learning to solve reinforcement learning problems.

2 code implementations • 11 Feb 2022 • Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber

The weight matrix (WM) of a neural network (NN) is its program.

1 code implementation • 11 Feb 2022 • Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber

Linear layers in neural networks (NNs) trained by gradient descent can be expressed as a key-value memory system which stores all training datapoints and the initial weights, and produces outputs using unnormalised dot attention over the entire training experience.

1 code implementation • 31 Dec 2021 • Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber

We share our experience with the recently released WILDS benchmark, a collection of ten datasets dedicated to developing models and training strategies which are robust to domain shifts.

1 code implementation • ICLR Workshop Neural_Compression 2021 • Kazuki Irie, Jürgen Schmidhuber

The inputs and/or outputs of some neural nets are weight matrices of other neural nets.

1 code implementation • 3 Nov 2021 • Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad, Kory W. Mathewson, Jürgen Schmidhuber

We look at how machine learning techniques that derive properties of items in a collection of independent media can be used to automatically embed stories into such collections.

1 code implementation • 14 Oct 2021 • Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber

Despite progress across a broad range of applications, Transformers have limited success in systematic generalization.

no code implementations • NeurIPS Workshop AIPLANS 2021 • Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber

Despite successes across a broad range of applications, Transformers have limited capability in systematic generalization.

no code implementations • NeurIPS Workshop AIPLANS 2021 • Imanol Schlag, Jürgen Schmidhuber

We augment classic algorithms with learned components to adapt them to domains currently dominated by deep learning models.

no code implementations • ICLR 2022 • Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber

Despite successes across a broad range of applications, Transformers have limited capability in systematic generalization.

1 code implementation • EMNLP 2021 • Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber

Our models improve accuracy from 50% to 85% on the PCFG productivity split, and from 35% to 81% on COGS.

1 code implementation • 19 Jul 2021 • Miroslav Štrupl, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, Jürgen Schmidhuber

Reward-Weighted Regression (RWR) belongs to a family of widely known iterative Reinforcement Learning algorithms based on the Expectation-Maximization framework.

no code implementations • 12 Jul 2021 • Noor Sajid, Francesco Faccio, Lancelot Da Costa, Thomas Parr, Jürgen Schmidhuber, Karl Friston

Under the Bayesian brain hypothesis, behavioural variations can be attributed to different priors over generative model parameters.

5 code implementations • NeurIPS 2021 • Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber

Transformers with linearised attention (''linear Transformers'') have demonstrated the practical scalability and effectiveness of outer product-based Fast Weight Programmers (FWPs) from the '90s.

1 code implementation • 16 Mar 2021 • Lukas Tuggener, Jürgen Schmidhuber, Thilo Stadelmann

In this work we challenge the notion that CNN architecture design solely based on ImageNet leads to generally effective convolutional neural network (CNN) architectures that perform well on a diverse set of datasets and application domains.

1 code implementation • ICLR 2021 • Đorđe Miladinović, Aleksandar Stanić, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann

We show that augmenting the decoder of a hierarchical VAE by spatial dependency layers considerably improves density estimation over baseline convolutional architectures and the state-of-the-art among the models within the same class.

9 code implementations • 22 Feb 2021 • Imanol Schlag, Kazuki Irie, Jürgen Schmidhuber

We show the formal equivalence of linearised self-attention mechanisms and fast weight controllers from the early '90s, where a ``slow" neural net learns by gradient descent to program the ``fast weights" of another net through sequences of elementary programming instructions which are additive outer products of self-invented activation patterns (today called keys and values).

no code implementations • NeurIPS 2021 • Louis Kirsch, Jürgen Schmidhuber

Many concepts have been proposed for meta learning with neural networks (NNs), e. g., NNs that learn to reprogram fast weights, Hebbian plasticity, learned learning rules, and meta recurrent NNs.

no code implementations • 9 Dec 2020 • Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber

Contemporary neural networks still fall short of human-level generalization, which extends far beyond our direct experiences.

1 code implementation • ICLR 2021 • Anand Gopalakrishnan, Sjoerd van Steenkiste, Jürgen Schmidhuber

We propose PermaKey, a novel approach to representation learning based on object keypoints.

1 code implementation • ICLR 2021 • Imanol Schlag, Tsendsuren Munkhdalai, Jürgen Schmidhuber

Humans can quickly associate stimuli to solve problems in novel contexts.

Ranked #1 on Question Answering on catbAbI LM-mode

no code implementations • 7 Oct 2020 • Aleksandar Stanić, Sjoerd van Steenkiste, Jürgen Schmidhuber

Common-sense physical reasoning in the real world requires learning about the interactions of objects and their dynamics.

1 code implementation • ICLR 2021 • Róbert Csordás, Sjoerd van Steenkiste, Jürgen Schmidhuber

Neural networks (NNs) whose subnetworks implement reusable functions are expected to offer numerous advantages, including compositionality through efficient recombination of functional building blocks, interpretability, preventing catastrophic interference, etc.

1 code implementation • 9 Jul 2020 • Aditya Ramesh, Paulo Rauber, Michelangelo Conserva, Jürgen Schmidhuber

An agent in a nonstationary contextual bandit problem should balance between exploration and the exploitation of (periodic or structured) patterns present in its previous experiences.

1 code implementation • ICLR 2021 • Francesco Faccio, Louis Kirsch, Jürgen Schmidhuber

We introduce a class of value functions called Parameter-Based Value Functions (PBVFs) whose inputs include the policy parameters.

no code implementations • ICML Workshop LifelongML 2020 • Krsto Proroković, Michael Wand, Jürgen Schmidhuber

An EMG-based upper limb prosthesis relies on a statistical pattern recognition system to map the EMG signal of residual forearm muscles into the appropriate hand movements.

7 code implementations • 5 Dec 2019 • Rupesh Kumar Srivastava, Pranav Shyam, Filipe Mutz, Wojciech Jaśkowski, Jürgen Schmidhuber

Many of its general principles are outlined in a companion report; the goal of this paper is to develop a practical learning algorithm and show that this conceptually simple perspective on agent training can produce a range of rewarding behaviors for multiple episodic environments.

3 code implementations • 15 Oct 2019 • Imanol Schlag, Paul Smolensky, Roland Fernandez, Nebojsa Jojic, Jürgen Schmidhuber, Jianfeng Gao

We incorporate Tensor-Product Representations within the Transformer in order to better support the explicit representation of relation structure.

Ranked #1 on Question Answering on Mathematics Dataset

no code implementations • 11 Oct 2019 • Aleksandar Stanić, Jürgen Schmidhuber

Traditional sequential multi-object attention models rely on a recurrent mechanism to infer object relations.

no code implementations • ICLR 2020 • Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber

Biological evolution has distilled the experiences of many learners into the general learning algorithms of humans.

2 code implementations • 13 Jun 2019 • Timon Willi, Jonathan Masci, Jürgen Schmidhuber, Christian Osendorfer

We extend Neural Processes (NPs) to sequential data through Recurrent NPs or RNPs, a family of conditional state space models.

no code implementations • 3 Jun 2019 • Sjoerd van Steenkiste, Klaus Greff, Jürgen Schmidhuber

In order to meet the diverse challenges in solving many real-world problems, an intelligent agent has to be able to dynamically construct a model of its environment.

no code implementations • NeurIPS 2019 • Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem

A disentangled representation encodes information about the salient factors of variation in the data independently.

1 code implementation • 23 Apr 2019 • Róbert Csordás, Jürgen Schmidhuber

The Differentiable Neural Computer (DNC) can learn algorithmic and question answering tasks.

1 code implementation • NeurIPS 2018 • Imanol Schlag, Jürgen Schmidhuber

We combine Recurrent Neural Networks with Tensor Product Representations to learn combinatorial representations of sequential data.

1 code implementation • 29 Nov 2018 • Imanol Schlag, Jürgen Schmidhuber

We combine Recurrent Neural Networks with Tensor Product Representations to learn combinatorial representations of sequential data.

no code implementations • ICLR 2019 • Sjoerd van Steenkiste, Karol Kurach, Jürgen Schmidhuber, Sylvain Gelly

We present a minimal modification of a standard generator to incorporate this inductive bias and find that it reliably learns to generate images as compositions of objects.

no code implementations • NeurIPS 2018 • David Ha, Jürgen Schmidhuber

A generative recurrent neural network is quickly trained in an unsupervised manner to model popular reinforcement learning environments through compressed spatio-temporal representations.

2 code implementations • 27 Mar 2018 • Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Marcello Pelillo, Thilo Stadelmann

We present the DeepScores dataset with the goal of advancing the state-of-the-art in small objects recognition, and by placing the question of object recognition in the context of scene understanding.

21 code implementations • 27 Mar 2018 • David Ha, Jürgen Schmidhuber

We explore building generative neural network models of popular reinforcement learning environments.

3 code implementations • ICLR 2018 • Sjoerd van Steenkiste, Michael Chang, Klaus Greff, Jürgen Schmidhuber

Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world.

no code implementations • ICLR 2018 • Imanol Schlag, Jürgen Schmidhuber

We improve previous end-to-end differentiable neural networks (NNs) with fast weight memories.

1 code implementation • NeurIPS 2017 • Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber

Many real world tasks such as reasoning and physical interaction require identification and manipulation of conceptual entities.

no code implementations • 22 Dec 2016 • Klaus Greff, Rupesh K. Srivastava, Jürgen Schmidhuber

We demonstrate that this viewpoint directly leads to the construction of Highway and Residual networks.

5 code implementations • ICML 2017 • Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, Jürgen Schmidhuber

We introduce a novel theoretical analysis of recurrent networks based on Gersgorin's circle theorem that illuminates several modeling and optimization issues and improves our understanding of the LSTM cell.

Ranked #16 on Language Modelling on Hutter Prize

2 code implementations • NeurIPS 2016 • Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hotloo Hao, Jürgen Schmidhuber, Harri Valpola

We present a framework for efficient perceptual inference that explicitly reasons about the segmentation of its inputs and features.

no code implementations • 29 Jan 2016 • Michael Wand, Jan Koutník, Jürgen Schmidhuber

Lipreading, i. e. speech recognition from visual-only recordings of a speaker's face, can be achieved with a processing pipeline based solely on neural networks, yielding significantly better accuracy than conventional methods.

1 code implementation • 19 Nov 2015 • Klaus Greff, Rupesh Kumar Srivastava, Jürgen Schmidhuber

Disentangled distributed representations of data are desirable for machine learning, since they are more expressive and can generalize from fewer examples.

3 code implementations • NeurIPS 2015 • Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber

Theoretical and empirical evidence indicates that the depth of neural networks is crucial for their success.

Ranked #34 on Image Classification on MNIST

3 code implementations • 3 May 2015 • Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber

There is plenty of theoretical and empirical evidence that depth of neural networks is a crucial ingredient for their success.

15 code implementations • 13 Mar 2015 • Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink, Jürgen Schmidhuber

Several variants of the Long Short-Term Memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995.

no code implementations • 21 Nov 2014 • Mitko Veta, Paul J. van Diest, Stefan M. Willems, Haibo Wang, Anant Madabhushi, Angel Cruz-Roa, Fabio Gonzalez, Anders B. L. Larsen, Jacob S. Vestergaard, Anders B. Dahl, Dan C. Cireşan, Jürgen Schmidhuber, Alessandro Giusti, Luca M. Gambardella, F. Boray Tek, Thomas Walter, Ching-Wei Wang, Satoshi Kondo, Bogdan J. Matuszewski, Frederic Precioso, Violet Snell, Josef Kittler, Teofilo E. de Campos, Adnan M. Khan, Nasir M. Rajpoot, Evdokia Arkoumani, Miangela M. Lacle, Max A. Viergever, Josien P. W. Pluim

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers.

no code implementations • 5 Oct 2014 • Rupesh Kumar Srivastava, Jonathan Masci, Faustino Gomez, Jürgen Schmidhuber

Recently proposed neural network activation functions such as rectified linear, maxout, and local winner-take-all have allowed for faster and more effective training of deep neural architectures on large and complex datasets.

5 code implementations • 14 Feb 2014 • Jan Koutník, Klaus Greff, Faustino Gomez, Jürgen Schmidhuber

Sequence prediction and classification are ubiquitous and challenging problems in machine learning that can require identifying complex dependencies between temporally distant inputs.

no code implementations • 19 Dec 2013 • Jürgen Schmidhuber

Deep Learning has attracted significant attention in recent years.

no code implementations • NeurIPS 2013 • Rupesh K. Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez, Jürgen Schmidhuber

Local competition among neighboring neurons is common in biological neural networks (NNs).

no code implementations • 1 Sep 2013 • Dan Cireşan, Jürgen Schmidhuber

Our Multi-Column Deep Neural Networks achieve best known recognition rates on Chinese characters from the ICDAR 2011 and 2013 offline handwriting competitions, approaching human performance.

no code implementations • 7 Feb 2013 • Alessandro Giusti, Dan C. Cireşan, Jonathan Masci, Luca M. Gambardella, Jürgen Schmidhuber

Deep Neural Networks now excel at image classification, detection and segmentation.

no code implementations • NeurIPS 2012 • Dan Ciresan, Alessandro Giusti, Luca M. Gambardella, Jürgen Schmidhuber

The input layer maps each window pixel to a neuron.

no code implementations • 6 Sep 2011 • Tom Schaul, Julian Togelius, Jürgen Schmidhuber

Artificial general intelligence (AGI) refers to research aimed at tackling the full problem of artificial intelligence, that is, create truly intelligent agents.

1 code implementation • 22 Jun 2011 • Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jürgen Schmidhuber

This paper presents Natural Evolution Strategies (NES), a recent family of algorithms that constitute a more principled approach to black-box optimization than established evolutionary algorithms.

no code implementations • 1 Feb 2011 • Dan C. Cireşan, Ueli Meier, Jonathan Masci, Luca M. Gambardella, Jürgen Schmidhuber

We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants.

no code implementations • NeurIPS 2010 • Yi Sun, Jürgen Schmidhuber, Faustino J. Gomez

We present a new way of converting a reversible finite Markov chain into a nonreversible one, with a theoretical guarantee that the asymptotic variance of the MCMC estimator based on the non-reversible chain is reduced.

no code implementations • NeurIPS 2008 • Alex Graves, Jürgen Schmidhuber

Offline handwriting recognition---the transcription of images of handwritten text---is an interesting task, in that it combines computer vision with sequence learning.

no code implementations • NeurIPS 2007 • Alex Graves, Marcus Liwicki, Horst Bunke, Jürgen Schmidhuber, Santiago Fernández

On-line handwriting recognition is unusual among sequence labelling tasks in that the underlying generator of the observed data, i. e. the movement of the pen, is recorded directly.

1 code implementation • ICML 2006 2006 • Alex Graves, Santiago Fernández, Faustino Gomez, Jürgen Schmidhuber

Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data.

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