Sentences with phrase «used sparse data»

Sixteen - month - old infants (N = 83) rationally used sparse data about the distribution of outcomes among agents and objects to solve a fundamental inference problem: deciding whether event outcomes are due to themselves or the world.

Sadique Sheik, Michael Pfeiffer, Fabio Stefanini, Giacomo Indiveri Perception of simple stimuli using sparse data from a tactile whisker array.

Not exact matches

In a paper published in PLOS Computational Biology in May, computational neuroscientists in the United Kingdom and Australia found that when neural networks using an algorithm for sparse coding called Products of Experts, invented by Hinton in 2002, are exposed to the same abnormal visual data as live cats (for example, the cats and neural networks both see only striped images), their neurons develop almost exactly the same abnormalities.

Using float data, scientists recalibrated sparse historical measurements and estimates of ocean warming, concluding that the upper 2,700 feet of the world's oceans had warmed by between a quarter and a half more than had previously been realized.

It is used in compressed sensing for reconstructing sparse signals using few linear measurements; clustering high - dimensional data; and DNA motif finding in computational biology.

The sparse coding represents the data using just a few active neurons and the features associated with those neurons, he noted.

The Meiler laboratory is recognized for methods developed for the structure determination of proteins, in particular using sparse experimental data.

I have seen weight loss facilitated by the use of amla (Indian gooseberry) and bergamot, but the scientific data is sparse.

Other research interests include data mining using high - dimensional and sparse (regularized) methods, with a focus on text summarization and causal inference with text in contexts such as newspaper corpora, legal decisions, and databases of free - text reports.

There are very good scientific reasons for using observational datasets that fill in data sparse regions in many analyses — I will continue using them — but we should be aware of not only their strengths but also of their weaknesses.

Because the analysis method and sparse data used in this study will tend to blur out most century - scale changes, we can't use the analysis of Marcott et al. to draw any firm conclusions about how unique the rapid changes of the twentieth century are compared to the previous 10,000 years.

I'm interested to use a global reanalysis data to force hydrological models such as SWAT for a meso watershed with sparse hydrometeorological stations.

WMO - «Because the data with respect to in - situ surface air temperature across Africa is sparse, a oneyear regional assessment for Africa could not be based on any of the three standard global surface air temperature data sets from NOAANCDC, NASA - GISS or HadCRUT4 Instead, the combination of the Global Historical Climatology Network and the Climate Anomaly Monitoring System (CAMS GHCN) by NOAA's Earth System Research Laboratory was used to estimate s

«Major improvements include updated and substantially more complete input data from the ICOADS Release 2.5, revised Empirical Orthogonal Teleconnections (EOTs) and EOT acceptance criterion, updated sea surface temperature (SST) quality control procedures, revised SST anomaly (SSTA) evaluation methods, revised low - frequency data filing in data sparse regions using nearby available observations, updated bias adjustments of ship SSTs using Hadley Nighttime Marine Air Temperature version 2 (HadNMAT2), and buoy SST bias adjustments not previously made in v3b.»

Although I haven't scrutinized the algorithm, it looks similar to what we called «adaptive fitting,» often used in the oil business to generate contour maps from sparse data.

Due to sparse data cover of this product in some of the European domain, the C3S satellite soil moisture product is not used for trend assessments.

«Because the data with respect to in - situ surface air temperature across Africa is sparse, a oneyear regional assessment for Africa could not be based on any of the three standard global surface air temperature data sets from NOAANCDC, NASA - GISS or HadCRUT4 Instead, the combination of the Global Historical Climatology Network and the Climate Anomaly Monitoring System (CAMS GHCN) by NOAA's Earth System Research Laboratory was used to estimate surface air temperature patterns»

I just don't believe that we can use a mathematical network approach to extract knowledge from sparse data.

However, these datasets use different methodologies and different tolerances for interpolating across data - sparse regions.