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Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation [article]

Xinjie Fan, Yizhe Zhang, Zhendong Wang, Mingyuan Zhou
2020 arXiv   pre-print
To stabilize this method, we adapt to contextual generation of categorical sequences a policy gradient estimator, which evaluates a set of correlated Monte Carlo (MC) rollouts for variance control.  ...  We also demonstrate the use of correlated MC rollouts for binary-tree softmax models, which reduce the high generation cost in large vocabulary scenarios by decomposing each categorical action into a sequence  ...  The authors acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research, and the computational support of Texas Advanced Computing Center.  ... 
arXiv:1912.13151v2 fatcat:ltcnmfj7efbwbeocpjjvdkbanm

Genz and Mendell-Elston Estimation of the High-Dimensional Multivariate Normal Distribution

Lucy Blondell, Mark Z. Kos, John Blangero, Harald H. H. Göring
2021 Algorithms  
The Monte Carlo-based approach described by Genz returns unbiased and error-bounded estimates, but execution time is more sensitive to the correlation between variables.  ...  The correlation between variables significantly affects absolute error but not overall execution time.  ...  (For discussion of these and other variance reduction techniques in Monte Carlo integration, see [42, 43] .)  ... 
doi:10.3390/a14100296 fatcat:wsvo5yyx7jbsvaao2zu3rkvqtm

Brain Networks for Exploration Decisions Utilizing Distinct Modeled Information Types during Contextual Learning

Jane X. Wang, Joel L. Voss
2014 Neuron  
We modeled distinct information types available during contextual association learning and used model-based fMRI in conjunction with manipulation of exploratory decision making to identify neural activity  ...  These findings link strategic exploration decisions during learning to quantifiable information and advance understanding of adaptive behavior by identifying the distinct and interactive nature of brain-network  ...  ACKNOWLEDGMENTS We thank Neal Cohen, Patrick Watson, Hillary Schwarb, and Kelly Brandstatt for helpful comments.  ... 
doi:10.1016/j.neuron.2014.04.028 pmid:24908493 pmcid:PMC4081497 fatcat:u4thjb3yrbftdh24i2adu7xria

Robust Speech Rate Estimation for Spontaneous Speech

Dagen Wang, Shrikanth S. Narayanan
2007 IEEE Transactions on Audio, Speech, and Language Processing  
We also describe an automated approach for learning algorithm parameters from data, and find the optimal settings through Monte Carlo simulations and parameter sensitivity analysis.  ...  The proposed algorithm extends the methods of spectral subband correlation by including temporal correlation and the use of prominent spectral subbands for improving the signal correlation essential for  ...  We adopt the Monte Carlo method to bootstrap the parameter value initialization. The first step is generating the possible ranges for the parameter values.  ... 
doi:10.1109/tasl.2007.905178 pmid:20428476 pmcid:PMC2860302 fatcat:bwyvmr32bvbktnkcjjfvpqursm

Context-gated statistical learning and its role in visual-saccadic decisions

Casimir J. H. Ludwig, Simon Farrell, Lucy A. Ellis, Tom E. Hardwicke, Iain D. Gilchrist
2012 Journal of experimental psychology. General  
Participants generated sequences of 2 saccadic eye movements in conditions where the probability that the 2nd saccade was directed back to the previously fixated location varied from low (.17) to high  ...  Adaptation occurred in the absence of contextual signals but was more pronounced in the presence of contextual cues.  ...  Finally, a class of very promising methods is based on sequential Monte Carlo integration or particle-filtering models (Brown & Steyvers, 2009; Sanborn, Griffiths, & Navarro, 2006) .  ... 
doi:10.1037/a0024916 pmid:21843019 pmcid:PMC3268529 fatcat:4js54oigcfgbtbdbjaoftrhxly

Contextual prediction errors reorganize naturalistic episodic memories in time

Fahd Yazin, Moumita Das, Arpan Banerjee, Dipanjan Roy
2021 Scientific Reports  
These results suggest a temporally distinct and adaptive role for prediction errors in learning and reorganizing episodic temporal sequences.  ...  Drift–diffusion modelling revealed a lower decision threshold for the newer sequences than older sequences, reflected by their faster recall.  ...  We would also like to thank Gargi Majumdar and Dipanjan Ray for their helpful suggestions and comments on the manuscript.  ... 
doi:10.1038/s41598-021-90990-1 pmid:34117294 fatcat:anwva5wc7vhpdo7vylz7pbtgfu

Incremental implicit learning of bundles of statistical patterns

Ting Qian, T. Florian Jaeger, Richard N. Aslin
2016 Cognition  
In the latter case, however, the learner must first parse the sequence of stimuli into different bundles, so as to not conflate the multiple patterns.  ...  However, to a naive learner, the learning of seasons can be (almost) treated as a categorization problem where the concept of seasons emerges primarily from the observation of the statistical patterns  ...  After the practice trials, the game randomly generated the first categorical distribution that controlled the positions of the mole across trials (e.g., Categorical(θ = 0.42, 0.17, 0.29, 0.12)), along  ... 
doi:10.1016/j.cognition.2016.09.002 pmid:27639552 pmcid:PMC5181648 fatcat:ukhyi245n5bvlem6pvxlyt572i

Global Patterns of Bacterial Beta-Diversity in Seafloor and Seawater Ecosystems

Lucie Zinger, Linda A. Amaral-Zettler, Jed A. Fuhrman, M. Claire Horner-Devine, Susan M. Huse, David B. Mark Welch, Jennifer B. H. Martiny, Mitchell Sogin, Antje Boetius, Alban Ramette, Jack Anthony Gilbert
2011 PLoS ONE  
greatly differ, at all taxonomic levels, and share ,10% bacterial types defined at 3% sequence similarity level.  ...  This opens interesting perspectives for the definition of biogeographical biomes for bacteria of ocean waters and the seabed.  ...  by 1000 Monte Carlo permutation tests followed by Bonferroni correction for multiple testing.  ... 
doi:10.1371/journal.pone.0024570 pmid:21931760 pmcid:PMC3169623 fatcat:x4xeodfse5hffa74uq3x4uw56u

Raising context awareness in motion forecasting [article]

Hédi Ben-Younes, Éloi Zablocki, Mickaël Chen, Patrick Pérez, Matthieu Cord
2022 arXiv   pre-print
Learning-based trajectory prediction models have encountered great success, with the promise of leveraging contextual information in addition to motion history.  ...  To alleviate this issue, we introduce CAB, a motion forecasting model equipped with a training procedure designed to promote the use of semantic contextual information.  ...  ACKNOWLEDGMENT We thank Thibault Buhet, Auguste Lehuger and Ivan Novikov for insightful comments on an early paper version.  ... 
arXiv:2109.08048v2 fatcat:lp2ynmbofzcjrjv7wwxszltsoi

Bayesian multiple-instance motif discovery with BAMBI: inference of recombinase and transcription factor binding sites

Guido H. Jajamovich, Xiaodong Wang, Adam P. Arkin, Michael S. Samoilov
2011 Nucleic Acids Research  
This work describes BAMBI-a sequential Monte Carlo motifidentification algorithm, which is based on a position weight matrix model that does not require additional constraints and is able to estimate such  ...  Finding conserved motifs in genomic sequences represents one of essential bioinformatic problems.  ...  Here, we overcome this impediment through the use of a sequential Monte Carlo technique.  ... 
doi:10.1093/nar/gkr745 pmid:21948794 pmcid:PMC3241671 fatcat:rfg4cgmfdjexlj52sraedam3vq

Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes

Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan
2007 2007 IEEE 11th International Conference on Computer Vision  
Dependencies between features are then captured with a hidden Markov tree, and Markov chain Monte Carlo methods used to learn models whose latent state space grows in complexity as more images are observed  ...  We show that our generative models capture interesting qualitative structure in natural scenes, and more accurately categorize novel images than models which ignore spatial relationships among features  ...  Learning Hierarchical Scene Models In this section, we propose a novel Monte Carlo method for learning HPD-HMT parameters from training images.  ... 
doi:10.1109/iccv.2007.4408870 dblp:conf/iccv/KivinenSJ07 fatcat:e5k3pvkspbcozitnkwfsnljuny

Fronto-Parietal Interactions With Task-Evoked Functional Connectivity During Cognitive Control [article]

Kai Hwang, James M. Shine, Mark D'Esposito
2017 bioRxiv   pre-print
Flexible interaction between brain regions enables neural systems to transfer and process information adaptively for goal-directed behaviors.  ...  Consistent with theoretical predictions, we found that cognitive control selectively enhances functional connectivity for prioritizing the processing of task-relevant information.  ...  To correct for multiple comparisons, a Monte Carlo simulation of 5000 iterations was performed to identify minimal cluster size that reached a corrected family-wise error rate of 0.05 (AFNI's 3dClustSim  ... 
doi:10.1101/133611 fatcat:3we5duz6zzgkhah7mgblcszst4

A Spatiotemporal Agent for Robust Multimodal Registration

Ziwei Luo, Xin Wang, Xi Wu, Youbing Yin, Kunlin Cao, Qi Song, Jing Hu
2020 IEEE Access  
A Monte Carlo rollout strategy is also leveraged to perform a look-ahead inference to the elimination of jitter in the test stage.  ...  Multimodal image registration is a crucial step for a variety of medical applications to provide complementary information from the combination of various data sources.  ...  -A Monte Carlo rollout (MC rollout) method is adopted in the inference to improve the registration capability.  ... 
doi:10.1109/access.2020.2989150 fatcat:yhgerk2w2zc3dkzekmm7iewmgq

The Use of Bandit Algorithms in Intelligent Interactive Recommender Systems [article]

Qing Wang
2021 arXiv   pre-print
However, few existing bandit models are able to adapt to new changes introduced by the modern recommender systems.  ...  In today's business marketplace, many high-tech Internet enterprises constantly explore innovative ways to provide optimal online user experiences for gaining competitive advantages.  ...  Sequential Monte Carlo (SMC) methods consist of a set of Monte Carlo methodologies to solve the filtering problem [9] , which provides a set of simulation-based methods for computing the posterior distribution  ... 
arXiv:2107.00161v1 fatcat:dv2s3ezfmjazxpeekhskalwh5u

Acoustic–phonetic and auditory mechanisms of adaptation in the perception of sibilant fricatives

Eleanor Chodroff, Colin Wilson
2019 Attention, Perception & Psychophysics  
Listeners are highly proficient at adapting to contextual variation when perceiving speech.  ...  We explored three theoretically motivated accounts of contextual adaptation, based on phonetic cue calibration, phonetic covariation, and auditory contrast.  ...  Sohn for their help in running the experiments, as well as  ... 
doi:10.3758/s13414-019-01894-2 pmid:31875314 fatcat:ol44tsx3zvcprepcnwyvlggxhi
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