
Hopfield Networks is All You Need
We show that the transformer attention mechanism is the update rule of a...
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Largescale ligandbased virtual screening for SARSCoV2 inhibitors using deep neural networks
Due to the current severe acute respiratory syndrome coronavirus 2 (SARS...
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Detecting cutaneous basal cell carcinomas in ultrahigh resolution and weakly labelled histopathological images
Diagnosing basal cell carcinomas (BCC), one of the most common cutaneous...
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Using LSTMs for climate change assessment studies on droughts and floods
Climate change affects occurrences of floods and droughts worldwide. How...
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Quantum Optical Experiments Modeled by Long ShortTerm Memory
We demonstrate how machine learning is able to model experiments in quan...
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Patch Refinement – Localized 3D Object Detection
We introduce Patch Refinement a twostage model for accurate 3D object d...
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Explaining and Interpreting LSTMs
While neural networks have acted as a strong unifying force in the desig...
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Benchmarking a CatchmentAware Long ShortTerm Memory Network (LSTM) for LargeScale Hydrological Modeling
Regional rainfallrunoff modeling is an old but still mostly outstandin...
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NeuralHydrology  Interpreting LSTMs in Hydrology
Despite the huge success of Long ShortTerm Memory networks, their appli...
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Interpretable Deep Learning in Drug Discovery
Without any means of interpretation, neural networks that predict molecu...
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RUDDER: Return Decomposition for Delayed Rewards
We propose a novel reinforcement learning approach for finite Markov dec...
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Fréchet ChemblNet Distance: A metric for generative models for molecules
The new wave of successful generative models in machine learning has inc...
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First Order Generative Adversarial Networks
GANs excel at learning high dimensional distributions, but they can upda...
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Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
Generative adversarial networks (GANs) evolved into one of the most succ...
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GANs Trained by a Two TimeScale Update Rule Converge to a Local Nash Equilibrium
Generative Adversarial Networks (GANs) excel at creating realistic image...
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SelfNormalizing Neural Networks
Deep Learning has revolutionized vision via convolutional neural network...
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Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
We introduce the "exponential linear unit" (ELU) which speeds up learnin...
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Toxicity Prediction using Deep Learning
Everyday we are exposed to various chemicals via food additives, cleanin...
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Rectified Factor Networks
We propose rectified factor networks (RFNs) to efficiently construct ver...
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Sepp Hochreiter
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Sepp Hochreiter is an informatician from Germany. Since 2018, after leading the Bioinformatics Institute from 2006 to 2018, he heads the Institute for Machine Learning at the Johannes Kepler University in Linz. He also heads the Linz Institute of Technology AI Lab since 2017, which is focused on promoting artificial intelligence research. He previously worked at Berlin University of Technology, Boulder University in Colorado and the University of Technology in Munich.
Sepp Hochreiter has contributed in numerous ways to master learning, deep learning and bioinformatics. He developed the longterm memory for which the first findings of his diploma thesis were reported in 1991. The main LSTM paper was published in 1997 as a discovery that is a milestone in machine learning. His analysis of the gradient disappearing or exploding led to the foundation of deep learning. He contributed to metalearning and proposed flat minimum learning solutions to ensure the low generalization error. Artificial neural networks To improve learning, he has developed new activation functions for neural networks such as exponential linear units or scaled ELUs. He helped to strengthen learning through actorcritical approaches and his RUDDER method. He used drug discovery and toxicology biclustering techniques. He extended support for vector systems to handle kernels with a “Potential Support Vector Machine” model and used the model to select features, especially for genes for microarray data. In addition to his research papers, Sepp Hochreiter is widely involved in his field: he has started the Bioinformatics Working Group of the Austrian Society for Computers; he is a founding board member of several startup bioinformatics companies; he was program chairman of the Bioinformatics conference Resea He founded the bioinformatics bachelor programme, a crossborder doubledegree program, in conjunction with the University of South Bohemia in České Budějovice, Czech Republic, as a faculty member at Johannes Kepler Linz. He also established the Bioinformatics Masters Program,
Where the acting dean of both studies is still.
The longterm memory for which the first results were reported was developed in 1991 by Sepp Hochreiter in his diploma thesis. The main LSTM paper was published in 1997 and is considered a finding which is a landmark in machine learning. LSTM overcomes recurring neural networks and deep networks in order to forget information over time or equally over layers.
LSTM learns from training sequences in order to process new sequences to generate an output or an output sequence. LSTM cell neural networks solved numerous tasks in biological sequency analysis, drug design, automatic composition of music, machine translation, language recognition, improved learning and robotics.
LSTM with an optimized architecture has been used very quickly
Detection of protein homology without sequence alignment.
LSTM has been used to learn an algorithm, i.e. LSTM works as a Turing machine, i.e. a computer that performs an algorithm of learning. As the LSTM Turing machine is a neural network, new learning algorithms can be developed by learning about problems of learning. The new learning techniques that have been learnt are superior to those designed by humans. LSTM Networks are used for Google Voice, Google Voice Search and Google’s Allo as core technology in Google App’s voice searches and commands and for Android dictation. Since iOS 10, Apple has also used LSTM in its Quicktype function.
Neural networks are various types
Simplified biological neural network mathematical models like
Information only moves in one direction in feedforward neural networks,
from the input layer receiving environmental information,
The hidden layers to the output layer that provides environmental information.
In contrast to NNs, recurring neural networks
You can use your internal memory to process arbitrary input sequences.
When data mining is based on neural networks, overfitting reduces the network’s ability to process future data correctly. Sepp Hochreiter to avoid overfitting
Developed algorithms to locate neural networks of low complexity such as “Flat Minimum Search”
What looks for a “flat” minimum — a large connected area in the parameter space
The function of the network is constant. Network parameters can therefore be given with low accuracy
Means a lowcomplex, overfitting network. Low complexity neural network is well suited for deep learning, because it controls the complexity of each network layer and therefore learns to represent the input in a hierarchical way.
The group from Sepp Hochreiter introduced “explosive linear units” that accelerate learning in deep neural networks and lead to more accurate classification. Like corrected linear units, leaky ReLUs and parametric ReLUs, ELUs alleviate the problem of vanishing gradients by identifying positive values. However, compared to the ReLUs, ELUs have improved learning properties due to negative values that push unit activations closer to zero. Mean shifts to zero speed up learning by closer to the natural unit gradient due to a reduced biasshift effect. Sepp Hochreiter has introduced neural selfnormalization
Networks that provide feedforward networks with abstract input representations
SNNs avoid batch normalization problems since sample activation
Converge automatically to mean zero and variance one.
SNNs a technology that enables
train very deep networks with i.e.
use new strategies of regularization and
Learn in many layers very robustly. Unattended profound learning,
Generative opponent networks are very popular because they are
Create more realistic new images than those obtained from other generative approaches.
Sepp Hochreiter suggested
a twotime update rule for learning Stochastic gradient descent GANs
Any differentiable function of loss.
Stochastic approximation methods have been used to demonstrate
The TTUR is converging to a stationary local Nash balance.
This is the first evidence of the overall convergence of GANs.
The introduction of Another contribution is
the more appropriate “Fréchet Inception Distance”
Quality measurement for GANs than the Inception Score previously used. He has developed corrected factor networks
To build very sparse, nonlinear, highdimensional input representations efficiently. RFN models identify rare and small input events, have low interference between the units, have small rehabilitation errors and explain the structure of data covariance. RFN learning is a widespread alternative minimisation algorithm derived from a post regularization method that enforces nonnegative and standard retroactive means. RFN has been applied to bioinformatics and genetics very successfully.
Sepp Hochreiter has been working on actorcritical systems in the field of strengthening learning
Learn by “model back propagation.” This approach, however, has major drawbacks
Analysis of sensitivity like
Local minima, various instabilities during online learning,
Explosion and disappearance of the world model gradients,
No contribution or relevance is assigned to the reward
“RUDDER: Return decomposition for delayed rewards,” introduced Sepp Hochreiter
which is intended to learn optimal policies with highly late rewards for Markov Decision Processes.
For retarded rewards, the biases of actionvalue assessments have been shown
The time difference is only slowly corrected exponentially
Number of steps of delay.
In addition, he has demonstrated that the variance of an action value estimate is
The Monte Carlo methods learned increase other estimates,
The number of which with the number of delay steps can grow exponentially.
Both the exponentially slow TD bias correction and the RUDDER solution
Increase of many MC variances by return decomposition exponentially.
A new MDP built by RUDDER returns for each episode and policy the same as the original
MDP, but the benefits are redistributed
Episode. Episode. The redistribution leads to significantly reduced reward delays.
In the best case, the new MDP does not have any delayed benefits and TD is
Unbiased. Unbiased. The redeployed rewards are designed to track Q values to
Always keep the expected reward for the future at zero. An action, therefore
Increasing the anticipated return receives a positive reward and
Action reducing the expected return receives a negative compensation.
A safe exploration strategy, a lesson for RUDDER
Replay buffer and a redistribution method based on LSTM
through decomposition of returns and analysis of backward contributions. Both source and source code
Videos of demonstration are available.
The exploration can be enhanced through active exploration strategies
Maximize future episodes information gain, often
Curiosity associated.
Many chemical compounds fail in the late phases of the drug development pipeline in the pharmaceutical industry. These failures are due to inadequate biomolecular target effectiveness, unwanted interactions with other biomolecules, or unforeseen toxic effects. Sepp Hochreiter’s profound learning and biclustering methods identified novel on and offtarget effects in various drug design projects. In 2013, the Group of Sepp Hochreiter won the DREAM subcontest to forecast the average compound toxicity. This success with Deep Learning was continued in 2014 by winning the NIH, FDA, and NCATS “Tox21 Data Challenge.” Tox21 Data Challenge’s objective was to correctly predict environmental chemicals’ offtarget and toxic effects on nutrients, household products and drugs. These impressive successes demonstrate that profound learning is superior to other virtual screening methods. In addition, the Hochreiter Group worked to identify synergistic effects of drug combinations.
Sepp Hochreiter has developed “Bicluster Acquisition Factor Analysis” for biclustering which simultaneously clusters rows and matrix columns. A transcriptomic data bicluster is a pair of genes and a set of samples for which the genes are similar in the samples and vice versa. For example, the effects of compounds on a subgroup of genes may be similar in drug design. FABIA is a multiplicative model that takes on realistic nonGussian signal distributions with heavy tails and uses wellunderstood models, like a Bayesian variational approach. FABIA supplies every bicluster with the information content to separate fake biclusters from true biclusters. Sepp Hochreiter published a reference book on biclustering which presents the most important algorithms, typical biclustering applications, bicluster visualization and evaluation and software in R.
Vector support machines are supervised methods of learning for
Analysis of classification and regression by recognizing patterns and data regularities. Standard SVMs demand a definite positive
Kernel to build a squared data kernel matrix. Sepp Hochreiter proposed the “Potential Support Vector Machine,” which can be used for nonsquare kernel matrices and with kernels which do not have a definite positive value. A efficient sequential minimum optimization algorithm has been developed for the PSVM model selection. The PSVM minimizes a new objective which ensures theoretical limitations of the generalization error and selects features which are used for classification or regression automatically.
Sepp Hochreiter used PSVM for selection of functions, in particular gene selection for microarray data.
The PSVM and standard vector support machines have been used to extract indicative features
Coiled oligomerization of the coil.
“HapFABIA: Identification of very short identity segments with descent characterized by rare variants in large sequence data,” was developed by Sepp Hochreiter in order to detect short identity segments with descent. A segment of DNA in two or more individuals is identical by state if the nucleotide sequences are identical in this segment. An IBS segment is identical in two or more descents
If they have inherited it from a common ancestor, the segment of these individuals has the same ancestral origin. HapFABIA identifies 100 times the smallest of IBD segments of stateoftheart methods. For stateoftheart methods 10kbp for HapFABIA vs 1Mbp. HapFABIA is adapted to nextgeneration sequencing data and uses rare variants for IBD detection as well as genotyping microarray data. HapFABIA enables evolutionary biology to be improved,
Population genetics and association trials because the genome has been decomposed in short, highresolution IBD segments that describe the genome. HapFABIA was used to analyze human, neanderthal, and denisovan IBD sharing.
Sepp Hochreiter’s research group is part of the US Food and Drug Administration’s SEQC/MAQCIII Consortium. This consortium examined the RNA sequencing performance of Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms in multiple laboratory sites. This project defined standard approaches for evaluating, reporting and comparing the technical performance of experiments on differential gene expression at the genome scale. Sepp Hochreiter’s research group proposed to analyze the structural variation of DNA “cn.MOPS: Poissons mixture to identify copy number variations for next generation data at a low false discovery rate”
For detecting variations in copy number of next generation data sequence. The local DNA copy number is estimated by cn.MOPS, suitable for both whole genome sequencing and exom sequencing,
And may be applied to genomes diploid, haploid, and polyploid. Sepp Hochreiter’s group suggested “DEXUS: Identification of the Differential Expression in RNASeq Studies with Unknown Conditions” for the identification of differential transcripts in RNAseq data. DEXUS can detect differential expression in RNAseq data for which the sample is used.
Conditions that do not include biological replicates are unknown.
Sequence data were analyzed in the Sepp Hochreiter group to gain insights into chromatin remodeling. The The The
The cell structure reorganization was determined through the following generation sequencing of the resting and activated T cells. The analysis of this data on cell chromatin sequence identified longlasting GCrich
Nucleosomefree regions that are chromatin reshaping hot spots. For targeted clinical diagnostic nextgeneration sequence panels, particularly for cancer,
Panelcn.MOPS was developed by Hochreiter’s group.
Sepp Hochreiter has developed “Robust Microarray Summary Factor Analysis.” FARMS is designed to pretreat and synthesize highdensity DNA microarrays at the sample level for the analysis of the expression of RNA genes. FARMS is based on a factor analysis model optimized by maximizing the retro probability in a Bayesian context. FARMS outperformed all other methods with Affymetrix spiked in and other benchmark data. FARMS’ informative/noninformative calls are a highly relevant feature. The I/NI call is a Bayesian filtering method that divides signal variance from noise variance. In analyzing microarray data, the I/NI call offers a solution to the main problem of high dimensionality by selecting high quality measured genes. FARMS was expanded to cn.FARMS
Structural variants like copy number variations for the detection of DNA
With a low rate of false discovery.