A supervised deep convolutional based bidirectional long short term memory video hashing for large scale video retrieval applications

Simulation accuracy among traditional hydrological models usually degrades significantly when going from single basin to regional scale. Hydrological models perform best when calibrated for specific basins, and do worse when a regional calibration scheme is used.&#160;One reason for this is that these models do not (have to) learn hydrological processes from data. Rather, they have a predefined model structure and only a handful of parameters adapt to specific basins. This often yields less-than-optimal parameter values when the loss is not determined by a single basin, but by many through regional calibration.The opposite is true for data driven approaches where models tend to get better with more and diverse training data. We examine whether this holds true when modeling rainfall-runoff processes with deep learning, or if, like their process-based counterparts, data-driven hydrological models degrade when going from basin to regional scale.Recently, Kratzert et al. (2018) showed that the Long Short-Term Memory network (LSTM), a special type of recurrent neural network, achieves comparable performance to the SAC-SMA at basin scale. In follow up work Kratzert et al. (2019a) trained a single LSTM for hundreds of basins in the continental US, which outperformed a set of hydrological models significantly, even compared to basin-calibrated hydrological models. On average, a single LSTM is even better in out-of-sample predictions (ungauged) compared to the SAC-SMA in-sample (gauged) or US National Water Model (Kratzert et al. 2019b).LSTM-based approaches usually involve tuning a large number of hyperparameters, such as the number of neurons, number of layers, and learning rate, that are critical for the predictive performance. Therefore, large-scale hyperparameter search has to be performed to obtain a proficient LSTM network.&#160;&#160;However, in the abovementioned studies, hyperparameter optimization was not conducted at large scale and e.g. in Kratzert et al. (2018) the same network hyperparameters were used in all basins, instead of tuning hyperparameters for each basin separately. It is yet unclear whether LSTMs follow the same trend of traditional hydrological models to degrade performance from basin to regional scale.&#160;In the current study, we performed a computational expensive, basin-specific hyperparameter search to explore how site-specific LSTMs differ in performance compared to regionally calibrated LSTMs. We compared our results to the mHM and VIC models, once calibrated per-basin and once using an MPR regionalization scheme. These benchmark models were calibrated individual research groups, to eliminate bias in our study. We analyse whether differences in basin-specific vs regional model performance can be linked to basin attributes or data set characteristics.References:Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall&#8211;runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005&#8211;6022, https://doi.org/10.5194/hess-22-6005-2018, 2018.&#160;Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089&#8211;5110, https://doi.org/10.5194/hess-23-5089-2019, 2019a.&#160;Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., & Nearing, G. S.: Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resources Research, 55. https://doi.org/10.1029/2019WR026065, 2019b.

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Explicit Knowledge-based Reasoning for Visual Question Answering

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/179 ◽

2017 ◽

Cited By ~ 14

Author(s):

Peng Wang ◽

Qi Wu ◽

Chunhua Shen ◽

Anthony Dick ◽

Anton van den Hengel

Keyword(s):

Natural Language ◽

Large Scale ◽

Question Answering ◽

Explicit Knowledge ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Visual Question Answering ◽

Knowledge Based ◽

Long Short Term Memory

We describe a method for visual question answering which is capable of reasoning about an image on the basis of information extracted from a large-scale knowledge base. The method not only answers natural language questions using concepts not contained in the image, but can explain the reasoning by which it developed its answer. It is capable of answering far more complex questions than the predominant long short-term memory-based approach, and outperforms it significantly in testing. We also provide a dataset and a protocol by which to evaluate general visual question answering methods.

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A fast and accurate hybrid simulation model for the large-scale testing of automated driving functions

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407019861245 ◽

2019 ◽

Vol 234 (4) ◽

pp. 1183-1196 ◽

Cited By ~ 1

Author(s):

Nicolas Fraikin ◽

Kilian Funk ◽

Michael Frey ◽

Frank Gauterin

Keyword(s):

Simulation Model ◽

Large Scale ◽

Short Term Memory ◽

Ground Truth ◽

Computational Effort ◽

Single Track ◽

Automated Driving ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

The upcoming market introduction of highly automated driving functions and associated requirements on reliability and safety require new tools for the virtual test coverage to lower development expenses. In this contribution, a computationally efficient and accurate simulation environment for the vehicle’s lateral dynamics is introduced. Therefore, an analytic single track model is coupled with a long-short-term-memory neural network to compensate modelling inaccuracies of the single track model. This ‘Hybrid Vehicle Model’ is parameterized with selected training batches obtained from a complex simulation model serving as a reference to simplify the data acquisition. The single track model is parameterized using given catalogue data. Thereafter, the long-short-term-memory network is trained to cover for the single track model’s shortcomings compared to the ground truth in a closed-loop setup. The evaluation with measurements from the real vehicle shows that the hybrid model can provide accurate long-term predictions with low computational effort that outperform results achieved when using the models isolated.

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Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model

Scientific Reports ◽

10.1038/s41598-021-83966-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yesol Park ◽

Joohong Lee ◽

Heesang Moon ◽

Yong Suk Choi ◽

Mina Rho

Keyword(s):

Language Processing ◽

Large Scale ◽

Short Term Memory ◽

Relation Extraction ◽

Parse Tree ◽

Short Term ◽

Term Memory ◽

Disease Associations ◽

Memory Network ◽

Long Short Term Memory

AbstractWith recent advances in biotechnology and sequencing technology, the microbial community has been intensively studied and discovered to be associated with many chronic as well as acute diseases. Even though a tremendous number of studies describing the association between microbes and diseases have been published, text mining methods that focus on such associations have been rarely studied. We propose a framework that combines machine learning and natural language processing methods to analyze the association between microbes and diseases. A hierarchical long short-term memory network was used to detect sentences that describe the association. For the sentences determined, two different parse tree-based search methods were combined to find the relation-describing word. The ensemble model of constituency parsing for structural pattern matching and dependency-based relation extraction improved the prediction accuracy. By combining deep learning and parse tree-based extractions, our proposed framework could extract the microbe-disease association with higher accuracy. The evaluation results showed that our system achieved an F-score of 0.8764 and 0.8524 in binary decisions and extracting relation words, respectively. As a case study, we performed a large-scale analysis of the association between microbes and diseases. Additionally, a set of common microbes shared by multiple diseases were also identified in this study. This study could provide valuable information for the major microbes that were studied for a specific disease. The code and data are available at https://github.com/DMnBI/mdi_predictor.

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Comparison of Long Short-Term Memory Networks and Random Forest for Sentinel-1 Time Series Based Large Scale Crop Classification

Remote Sensing ◽

10.3390/rs13245000 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5000

Author(s):

Felix Reuß ◽

Isabella Greimeister-Pfeil ◽

Mariette Vreugdenhil ◽

Wolfgang Wagner

Keyword(s):

Time Series ◽

Deep Learning ◽

Large Scale ◽

Short Term Memory ◽

Meteorological Data ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Crop Classification

To ensure future food security, improved agricultural management approaches are required. For many of those applications, precise knowledge of the distribution of crop types is essential. Various machine and deep learning models have been used for automated crop classification using microwave remote sensing time series. However, the application of these approaches on a large spatial and temporal scale is barely investigated. In this study, the performance of two frequently used algorithms, Long Short-Term Memory (LSTM) networks and Random Forest (RF), for crop classification based on Sentinel-1 time series and meteorological data on a large spatial and temporal scale is assessed. For data from Austria, the Netherlands, and France and the years 2015–2019, scenarios with different spatial and temporal scales were defined. To quantify the complexity of these scenarios, the Fisher Discriminant measurement F1 (FDR1) was used. The results demonstrate that both classifiers achieve similar results for simple classification tasks with low FDR1 values. With increasing FDR1 values, however, LSTM networks outperform RF. This suggests that the ability of LSTM networks to learn long-term dependencies and identify the relation between radar time series and meteorological data becomes increasingly important for more complex applications. Thus, the study underlines the importance of deep learning models, including LSTM networks, for large-scale applications.

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Multilabel Text Classification in News Articles Using Long-Term Memory with Word2Vec

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i2.1655 ◽

2020 ◽

Vol 4 (2) ◽

pp. 276-285

Author(s):

Winda Kurnia Sari ◽

Dian Palupi Rini ◽

Reza Firsandaya Malik ◽

Iman Saladin B. Azhar

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Text Classification ◽

Large Scale ◽

Short Term Memory ◽

Short Term ◽

Learning Methods ◽

Processing Variable ◽

Term Memory ◽

Long Short Term Memory

Multilabel text classification is a task of categorizing text into one or more categories. Like other machine learning, multilabel classification performance is limited to the small labeled data and leads to the difficulty of capturing semantic relationships. It requires a multilabel text classification technique that can group four labels from news articles. Deep Learning is a proposed method for solving problems in multilabel text classification techniques. Some of the deep learning methods used for text classification include Convolutional Neural Networks, Autoencoders, Deep Belief Networks, and Recurrent Neural Networks (RNN). RNN is one of the most popular architectures used in natural language processing (NLP) because the recurrent structure is appropriate for processing variable-length text. One of the deep learning methods proposed in this study is RNN with the application of the Long Short-Term Memory (LSTM) architecture. The models are trained based on trial and error experiments using LSTM and 300-dimensional words embedding features with Word2Vec. By tuning the parameters and comparing the eight proposed Long Short-Term Memory (LSTM) models with a large-scale dataset, to show that LSTM with features Word2Vec can achieve good performance in text classification. The results show that text classification using LSTM with Word2Vec obtain the highest accuracy is in the fifth model with 95.38, the average of precision, recall, and F1-score is 95. Also, LSTM with the Word2Vec feature gets graphic results that are close to good-fit on seventh and eighth models.

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Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling

10.5194/hess-2019-368 ◽

2019 ◽

Cited By ~ 6

Author(s):

Frederik Kratzert ◽

Daniel Klotz ◽

Guy Shalev ◽

Günter Klambauer ◽

Sepp Hochreiter ◽

...

Keyword(s):

Large Scale ◽

Hydrological Modeling ◽

Time Series Data ◽

Short Term Memory ◽

Series Data ◽

Hydrological Models ◽

Short Term ◽

Data Set ◽

Term Memory ◽

Long Short Term Memory

Abstract. Regional rainfall-runoff modeling is an old but still mostly out-standing problem in Hydrological Sciences. The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone. In this paper, we propose a novel, data-driven approach using Long Short-Term Memory networks (LSTMs), and demonstrate that under a big data paradigm, this is not necessarily the case. By training a single LSTM model on 531 basins from the CAMELS data set using meteorological time series data and static catchment attributes, we were able to significantly improve performance compared to a set of several different hydrological benchmark models. Our proposed approach not only significantly outperforms hydrological models that were calibrated regionally but also achieves better performance than hydrological models that were calibrated for each basin individually. Furthermore, we propose an adaption to the standard LSTM architecture, which we call an Entity-Aware-LSTM (EA-LSTM), that allows for learning, and embedding as a feature layer in a deep learning model, catchment similarities. We show that this learned catchment similarity corresponds well with what we would expect from prior hydrological understanding.

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