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Latent Sequence Decompositions [article]

William Chan, Yu Zhang, Quoc Le, Navdeep Jaitly
2017 arXiv   pre-print
We present the Latent Sequence Decompositions (LSD) framework. LSD decomposes sequences with variable lengthed output units as a function of both the input sequence and the output sequence.  ...  LATENT SEQUENCE DECOMPOSITIONS In this section, we describe LSD more formally. Let x be our input sequence, y be our output sequence and z be a latent sequence decomposition of y.  ...  We present the Latent Sequence Decompositions (LSD) framework.  ... 
arXiv:1610.03035v6 fatcat:shmowkhp2rhmfpk32g2yzp4vyq

Decomposition of Pattern Structure with Latent Profile Periodicity in RNA Sequence
Декомпозиция структуры паттерна скрытой профильной периодичности в последовательностях ДНК

В.А. Кутыркин, М.Б. Чалей
2012 Engineering Journal Science and Innovation  
doi:10.18698/2308-6033-2012-2-66 fatcat:jtqz7fvg5vbupht5ef44spd26i

Latent periodicity of serine-threonine and tyrosine protein kinases and another protein families [article]

Andrew A. Laskin, Nikolai A. Kudryashov, Konstantin G.Skryabin, Eugene V. Korotkov
2004 arXiv   pre-print
We also designed the method of noise decomposition, which is aimed to distinguish between different periodicity types of the same period length.  ...  Summarizing, we presume that latent periodicity is the common property of many catalytic protein domains.  ...  In this combination, information decomposition can serve as the method that detects latent periodicity in some amino acid sequences and creates the periodicity matrix [26] , which can be used to determine  ... 
arXiv:q-bio/0409008v1 fatcat:3ahdzoziznchdmm3ijuikd4zoq

Information decomposition method to analyze symbolical sequences

E.V. Korotkov, M.A. Korotkova, N.A. Kudryashov
2003 Physics Letters A  
We developed a non-parametric method of Information Decomposition (ID) of a content of any symbolical sequence.  ...  The possible origin of latent periodicity for different symbolical sequences is discussed.  ...  Information decomposition method Firstly, let us define the concept of latent periodicity of a symbolical sequence.  ... 
doi:10.1016/s0375-9601(03)00641-8 fatcat:meolrbsgsrexdbiihltgooiriy

Learning Bounded Context-Free-Grammar via LSTM and the Transformer:Difference and Explanations [article]

Hui Shi, Sicun Gao, Yuandong Tian, Xinyun Chen, Jishen Zhao
2022 arXiv   pre-print
We study such practical differences between LSTM and Transformer and propose an explanation based on their latent space decomposition patterns.  ...  To achieve this goal, we introduce an oracle training paradigm, which forces the decomposition of the latent representation of LSTM and the Transformer and supervises with the transitions of the Pushdown  ...  (Figure 4 ) and latent decomposition (Figure 5 ).  ... 
arXiv:2112.09174v2 fatcat:t2tyu5ach5do3mvogiwfionu6q

Learning to Decompose and Disentangle Representations for Video Prediction [article]

Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li Fei-Fei, Juan Carlos Niebles
2018 arXiv   pre-print
Crucially, with an appropriately specified generative model of video frames, our DDPAE is able to learn both the latent decomposition and disentanglement without explicit supervision.  ...  Our goal is to predict future video frames given a sequence of input frames. Despite large amounts of video data, this remains a challenging task because of the high-dimensionality of video frames.  ...  Since our latent representations are decomposed and disentangled, we explain our model q in the following two sections: Video Decomposition and Disentangled Representation. Video Decomposition.  ... 
arXiv:1806.04166v2 fatcat:7vpjylwywng6pkhqvg7cbtk7pa

Why Plants Harbor Complex Endophytic Fungal Communities: Insights From Perennial Bunchgrass Stipagrostis sabulicola in the Namib Sand Sea

Anthony J. Wenndt, Sarah E. Evans, Anne D. van Diepeningen, J. Robert Logan, Peter J. Jacobson, Mary K. Seely, Kathryn M. Jacobson
2021 Frontiers in Microbiology  
Furthermore, profiling the community active during decomposition using next-generation sequencing revealed that 59–70% of the S. sabulicola endophyte community is comprised of latent saprophytes, and these  ...  Using frequent overnight non-rainfall moisture events (fog, dew, high humidity), these latent saprophytes can initiate decomposition of standing litter immediately after tiller senescence, thus maximizing  ...  Furthermore, profiling the community active during decomposition using next-generation sequencing revealed that 59-70% of the S. sabulicola endophyte community is comprised of latent saprophytes, and these  ... 
doi:10.3389/fmicb.2021.691584 pmid:34168636 pmcid:PMC8217645 fatcat:snqcnrvetrggnhovax4adnp6zm

Latent Periodicity of Many Genes

Eugene Korotkov, Nikolay Kudryaschov
2001 Genome Informatics Series  
Discussions The developed method of information decomposition (ID) of symbolical sequences proved to be capable to reveal latent structuredness of thousand genes.  ...  For the periods with the length greater than the size of the symbolical sequence alphabet, there is a possibility of "decomposition" of the statistical importance of the longer periods in favor of the  ... 
doi:10.11234/gi1990.12.437 fatcat:natn5uzdw5c5dijk5h3gdruljy

A Decision Tree Framework for Spatiotemporal Sequence Prediction

Taehwan Kim, Yisong Yue, Sarah Taylor, Iain Matthews
2015 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15  
Our approach enjoys several attractive properties, including ease of training, fast performance at test time, and the ability to robustly tolerate corrupted training data using a novel latent variable  ...  We study the problem of learning to predict a spatiotemporal output sequence given an input sequence.  ...  We propose a latent-variable extension to our basic decomposition framework to jointly estimate a "cleaner" version of training data while learning a contextual spatiotemporal sequence predictor.  ... 
doi:10.1145/2783258.2783356 dblp:conf/kdd/KimYTM15 fatcat:m3rppcnhwzeldpjumowhkbmiay

Personalized Modeling of Facial Action Unit Intensity [chapter]

Shuang Yang, Ognjen Rudovic, Vladimir Pavlovic, Maja Pantic
2014 Lecture Notes in Computer Science  
In the first step, we perform facial feature decomposition using the proposed matrix decomposition algorithm that separates the person's identity from facial expression.  ...  To address the limitations mentioned above, we propose a latent factor model for personalized facial feature decomposition that can deal efficiently with image sequences.  ...  The proposed decomposition algorithm can easily handle a large number of image sequences, with the fast convergence rate.  ... 
doi:10.1007/978-3-319-14364-4_26 fatcat:o7spx7cjwvgttoenbl6kfzesou

SPAMs: Structured Implicit Parametric Models [article]

Pablo Palafox, Nikolaos Sarafianos, Tony Tung, Angela Dai
2022 arXiv   pre-print
In particular, we can leverage the part decompositions at test time to fit to new depth sequences of unobserved shapes, by establishing part correspondences between the input observation and our learned  ...  part spaces; this guides a robust joint optimization between the shape and pose of all parts, even under dramatic motion sequences.  ...  At test time, we traverse the learned latent part spaces to fit to new depth sequences.  ... 
arXiv:2201.08141v1 fatcat:ki6oupfzoza7tkpytmkodg4nxa

Efficient training for future video generation based on hierarchical disentangled representation of latent variables [article]

Naoya Fushishita, Antonio Tejero-de-Pablos, Yusuke Mukuta, Tatsuya Harada
2021 arXiv   pre-print
We achieve high-efficiency by training our method in two stages: (1) image reconstruction to encode video frames into latent variables, and (2) latent variable prediction to generate the future sequence  ...  Generating videos predicting the future of a given sequence has been an area of active research in recent years.  ...  Finally, the resulting latent variable sequence dataset is used to train a latent variable sequence generator that predicts the future latent variable sequence from the given past latent variable sequence  ... 
arXiv:2106.03502v2 fatcat:6d7wf5gpp5d2pgrqbiqkt3nqle

Fast Decoding in Sequence Models using Discrete Latent Variables [article]

Łukasz Kaiser, Aurko Roy, Ashish Vaswani, Niki Parmar, Samy Bengio, Jakob Uszkoreit, Noam Shazeer
2018 arXiv   pre-print
latent sequence in parallel.  ...  We first auto-encode the target sequence into a shorter sequence of discrete latent variables, which at inference time is generated autoregressively, and finally decode the output sequence from this shorter  ...  latent vocabulary, the dimension D of the latent space, and the number of decompositions n d for DVQ.  ... 
arXiv:1803.03382v6 fatcat:j2w57uwmd5clhelp6mkiitf44m

Unsupervised Video Decomposition using Spatio-temporal Iterative Inference [article]

Polina Zablotskaia, Edoardo A. Dominici, Leonid Sigal, Andreas M. Lehrmann
2020 arXiv   pre-print
Unsupervised multi-object scene decomposition is a fast-emerging problem in representation learning.  ...  We propose a novel spatio-temporal iterative inference framework that is powerful enough to jointly model complex multi-object representations and explicit temporal dependencies between latent variables  ...  Video Decomposition We evaluate the models on a video decomposition task at different sequence lengths.  ... 
arXiv:2006.14727v1 fatcat:pvk3z4jqe5fkzogaaf7o73pz6i

Identification of Amino Acid Latent Periodicity within 94 Protein Families

Vera P. Turutina, Andrew A. Laskin, Nikolay A. Kudryashov, Konstantin G. Skryabin, Eugene V. Korotkov
2006 Journal of Computational Biology  
Here, we have applied information decomposition, cyclic profile alignment, and noise decomposition techniques to search for latent repeats within protein families of various functions.  ...  Latent periodicity profiles with specific length and signature were obtained in each case.  ...  Recently, we have reported the development of the information decomposition (ID) technique to search for weak or latent periodicity within symbol sequences.  ... 
doi:10.1089/cmb.2006.13.946 pmid:16761920 fatcat:skbcoebq7zeethfxcjwced4fxi
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