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Addressing the Under-Translation Problem from the Entropy Perspective

Yang Zhao, Jiajun Zhang, Chengqing Zong, Zhongjun He, Hua Wu
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In fine-grained phase, we propose three methods, including pre-training method, multitask method and two-pass method, to encourage the neural model to correctly translate these high-entropy words.  ...  Through analysis, we observe that a source word with a large translation entropy is more inclined to be dropped. To address this problem, we propose a coarse-to-fine framework.  ...  If the translation entropy of a source word s exceeds the predefined threshold e 0 , i.e., E(s) > e 0 , we treat this word as a high-entropy word.  ... 
doi:10.1609/aaai.v33i01.3301451 fatcat:7kzp35j3n5fpdc24u74mh7natu

Target and Task specific Source-Free Domain Adaptive Image Segmentation [article]

Vibashan VS, Jeya Maria Jose Valanarasu, Vishal M. Patel
2022 arXiv   pre-print
However, due to domain shift, these pseudo-labels are usually of high entropy and denoising them still does not make them perfect labels to supervise the model.  ...  In the first stage, we focus on generating target-specific pseudo labels while suppressing high entropy regions by proposing an Ensemble Entropy Minimization loss.  ...  However, using just one input data to generate the entropy map might not give us all the accurate locations of high entropy.  ... 
arXiv:2203.15792v1 fatcat:vwlupjizyjhp7pbnf7ofz3trnm

Design Principles for True Random Number Generators for Security Applications

Miloš Grujić, Vladimir Rožić, David Johnston, John Kelsey, Ingrid Verbauwhede
2019 Proceedings of the 56th Annual Design Automation Conference 2019 on - DAC '19  
The generation of high quality true random numbers is essential in security applications.  ...  For secure communication, we also require high quality true random number generators (TRNGs) in embedded and IoT devices.  ...  Stochastic models of physical noise sources are used to guarantee entropy of the raw random bits.  ... 
doi:10.1145/3316781.3323482 dblp:conf/dac/GrujicRJKV19 fatcat:zmo6yqiqizchbpsokagvremqmq

Source-Relaxed Domain Adaptation for Image Segmentation [article]

Mathilde Bateson, Hoel Kervadec, Jose Dolz, Herve Lombaert, Ismail Ben Ayed
2020 arXiv   pre-print
Domain adaptation (DA) has drawn high interests for its capacity to adapt a model trained on labeled source data to perform well on unlabeled or weakly labeled target data from a different domain.  ...  We show the effectiveness of our prior-aware entropy minimization in adapting spine segmentation across different MRI modalities.  ...  Without adaptation, a model trained on source data only can't recover the structure of the IVD on the target data, and is very uncertain, as revealed by the high activations in the prediction entropy maps  ... 
arXiv:2005.03697v1 fatcat:37fikv6cgbc3ljdsfxstg27roq

ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, Patrick Perez
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
To this end, we propose two novel, complementary methods using (i) an entropy loss and (ii) an adversarial loss respectively.  ...  In this work, we address the task of unsupervised domain adaptation in semantic segmentation with losses based on the entropy of the pixel-wise predictions.  ...  We start from a simple observation: models trained only on source domain tend to produce over-confident, i.e., low-entropy, predictions on source-like images and under-confident, i.e., high-entropy, predictions  ... 
doi:10.1109/cvpr.2019.00262 dblp:conf/cvpr/VuJBCP19 fatcat:ywxufgupfvfejjfuzngweqcbeq

ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation [article]

Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, Patrick Pérez
2019 arXiv   pre-print
To this end, we propose two novel, complementary methods using (i) entropy loss and (ii) adversarial loss respectively.  ...  In this work, we address the task of unsupervised domain adaptation in semantic segmentation with losses based on the entropy of the pixel-wise predictions.  ...  We start from a simple observation: models trained only on source domain tend to produce over-confident, i.e., low-entropy, predictions on source-like images and under-confident, i.e., high-entropy, predictions  ... 
arXiv:1811.12833v2 fatcat:e7ox63x7svbzvlvnalfbleotp4

Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning

Weishi Shi, Xujiang Zhao, Feng Chen, Qi Yu
2020 Neural Information Processing Systems  
We present a novel multi-source uncertainty prediction approach that enables deep learning (DL) models to be actively trained with much less labeled data.  ...  Experiments conducted over both synthetic and real data and comparison with competitive AL methods demonstrate the effectiveness of the proposed ADL model.  ...  Evidence-Aware Entropy Decomposition As discussed earlier, a high entropy may be contributed by difference sources of uncertainty with distinct characteristics.  ... 
dblp:conf/nips/ShiZ0020 fatcat:5h3yhravpjcqfi7sjqmm6ld2km

Hierarchical Decomposition Thermodynamic Approach for the Study of Solar Absorption Refrigerator Performance

Emma Berrich Betouche, Ali Fellah, Ammar Ben Brahim, Fethi Aloui, Michel Feidt
2016 Entropy  
Under the hypothesis of an endoreversible model, the effects of the generator, the solar concentrator and the solar converter temperatures, on the coefficient of performance (COP), are presented and discussed  ...  In fact, the coefficient of performance variations, according to the ratio of the heat transfer areas of the high temperature part (the thermal engine 2) A h and the heat transfer areas of the low temperature  ...  The Carnot model is an ideal model far from the reality as it doesn't take into account the entropy production.  ... 
doi:10.3390/e18030082 fatcat:ssgk2vwl4vaztmzvyhoeylpccm

Physical and conceptual identifier dispersion: Measures and relation to fault proneness

Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Gael Gueheneuc, Giuliano Antoniol
2010 2010 IEEE International Conference on Software Maintenance  
We show statistically that methods containing terms with high entropy and context coverage are more fault-prone than others.  ...  Entropy measures the physical dispersion of terms in a program: the higher the entropy, the more scattered across the program the terms.  ...  reduce entropy and high context coverage.  ... 
doi:10.1109/icsm.2010.5609748 dblp:conf/icsm/ArnaoudovaEOGA10 fatcat:mgs2n2lrc5gqflqwnn56jhgiou

Predicting the Lossless Compression Ratio of Remote Sensing Images with Configurational Entropy

Xinghua Cheng, Zhilin Li
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
For lossless compression, the upper and lower limits of compression ratio are defined by Shannon's Source Coding Theorem with Shannon entropy as the metric, which measures the statistical information of  ...  This study provides a new direction for building a theoretical prediction model with configurational entropy.  ...  prediction models.  ... 
doi:10.1109/jstars.2021.3123650 fatcat:pgl3ejgj65citd3xlaylufxoxq

Convolution Neural Network-Based Sensitive Security Parameter Identification and Analysis

Hyunki Kim, Donghyun Kim, Okyeon Yi, Liran Ma
2022 Wireless Communications and Mobile Computing  
We identify noise sources that are used as entropy sources with our convolution neural network model.  ...  To ensure the security, the noise sources used to construct the entropy source must be securely collected.  ...  As a result of training the model with images of entropy sources, the patterns of bit strings that are difficult to identify are distinguished with the naked eye with high accuracy.  ... 
doi:10.1155/2022/9584894 fatcat:r2yz3ltcnbazfbcc4vut6rqkd4

An Optimal Framework for SDN Based on Deep Neural Network

Abdallah Abdallah, Mohamad Khairi Ishak, Nor Samsiah Sani, Imran Khan, Fahad R. Albogamy, Hirofumi Amano, Samih M. Mostafa
2022 Computers Materials & Continua  
The initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet's source and destination Internet Protocol (IP) addresses, and then identifies  ...  The false alarm rate (FAR) is much lower than that of the information entropy-based detection method.  ...  This model makes up for the shortcomings of the detection algorithm based on information entropy of low DR and high FAR.  ... 
doi:10.32604/cmc.2022.025810 fatcat:aya5eehdvrhxpgd3xvf7fg5vuy

Modelling the effects of internal heating in the core and lowermost mantle on the earth's magnetic history

S.O. Costin, S.L. Butler
2006 Physics of the Earth and Planetary Interiors  
This scenario has been discussed based on parameterized thermal and magnetic models of the core [Buffett, B.A., 2002.  ...  Recently, an incompatible-element enriched reservoir, bearing a high degree of radioactive heating, has been proposed to exist at the base of the mantle.  ...  The D series models explore the effects of high concentrations of radioactive internal heat sources in the lowermost 200 km of the mantle, while model H1 is used to explore the effects of high internal  ... 
doi:10.1016/j.pepi.2006.03.009 fatcat:3n5u5nkj6fbnfofm6dcpug27ou

Detection of High Frequency Sources in Random/Uncertain Media

Leon H. Sibul
2004 AIP Conference Proceedings  
band, high frequency source.  ...  Maximum entropy method (MEM) is used to incorporate essential uncertainty into model. Maximum entropy method uses what is known in its model, but models what is not known with maximum uncertainty.  ...  MODELING OF HIGH-FREQUENCY PROPAGATION IN TIME-VARYING RANDOM MEDIA We assume that both the source (or target) and receiver are in motion in a propagation medium with a random boundary and an inhomogeneous  ... 
doi:10.1063/1.1843018 fatcat:vu3x4nqgzzc5nn65gfmbqdzj7q

Frequency-specific brain dynamics related to prediction during language comprehension

Kristijan Armeni, Roel M. Willems, Antal van den Bosch, Jan-Mathijs Schoffelen
2019 NeuroImage  
We show that theta-band source dynamics are increased in high relative to low entropy states, likely reflecting lexical computations.  ...  Using trigram statistical language models, we estimated for every word in a story its conditional probability of occurrence.  ...  model) with 7842 source locations per hemisphere.  ... 
doi:10.1016/j.neuroimage.2019.04.083 pmid:31100432 fatcat:e4xm63ji3vd7znoi35i4eov5nq
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