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Deep Exploration Technologies Deep Exploration Technologies Deep Exploration Technologies
"... Pre-stack time migration in common source domain without velocity model ..."
A fast learning algorithm for deep belief nets
- Neural Computation
, 2006
"... We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in densely-connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a ..."
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Cited by 970 (49 self)
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We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in densely-connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer
Greedy layer-wise training of deep networks
, 2006
"... Complexity theory of circuits strongly suggests that deep architectures can be much more efficient (sometimes exponentially) than shallow architectures, in terms of computational elements required to represent some functions. Deep multi-layer neural networks have many levels of non-linearities allow ..."
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Cited by 394 (48 self)
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introduced a greedy layer-wise unsupervised learning algorithm for Deep Belief Networks (DBN), a generative model with many layers of hidden causal variables. In the context of the above optimization problem, we study this al-gorithm empirically and explore variants to better understand its success
Simple Heuristics That Make Us Smart
, 2008
"... To survive in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury, decision-makers must use bounded rationality. In this precis of Simple heuristics that make us smart, we explore fast and frugal heuristics—simple rules for making decisions with re ..."
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Cited by 456 (15 self)
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To survive in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury, decision-makers must use bounded rationality. In this precis of Simple heuristics that make us smart, we explore fast and frugal heuristics—simple rules for making decisions
A Deep Exploration of the Transcriptome and “Excretory/Secretory ” Proteome of Adult
"... Parasitic liver flukes of the family Fasciolidae are respon-sible for major socioeconomic losses worldwide. However, at present, knowledge of the fundamental molecular biol-ogy of these organisms is scant. Here, we characterize, for the first time, the transcriptome and secreted proteome of the adul ..."
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involved in carbohydrate metabolism. The integration of transcriptomic and proteomic datasets generated herein sets the scene for future studies aimed at exploring the potential role(s) that molecules might play at the host– parasite interface and for establishing novel strategies for the treatment
Targeted Geoscience Initiative 4: Increasing Deep Exploration EffectivenessRessources naturelles Canada Natural Resources
"... The P2 reverse fault (Figure 1) is a major structure within the Athabasca Basin, Saskatchewan, and is spatially associated with the McArthur River deposit, the world’s largest high-grade uranium mine. The McArthur River deposit is classified as an unconformity-type deposit as the mineralization is l ..."
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The P2 reverse fault (Figure 1) is a major structure within the Athabasca Basin, Saskatchewan, and is spatially associated with the McArthur River deposit, the world’s largest high-grade uranium mine. The McArthur River deposit is classified as an unconformity-type deposit as the mineralization is located at the unconformity (~500 m depth) between the Athabasca sandstone and basement rocks. The P2 is constrained to graphite-bearing paragneiss of the basement and is refracted within
Gated-V dd : A Circuit Technique to Reduce Leakage in Deep-Submicron Cache Memories
, 2000
"... Deep-submicron CMOS designs have resulted in large leakage energy dissipation in microprocessors. While SRAM cells in onchip cache memories always contribute to this leakage, there is a large variability in active cell usage both within and across applications. This paper explores an integrated arch ..."
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Cited by 227 (11 self)
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Deep-submicron CMOS designs have resulted in large leakage energy dissipation in microprocessors. While SRAM cells in onchip cache memories always contribute to this leakage, there is a large variability in active cell usage both within and across applications. This paper explores an integrated
2012b, 3d Reflection Seismic Imaging for Open-Pit Mine Planning and Deep Exploration in the Kevitsa Ni-Cu-Pge Deposit
- Geophysics
"... A 3D reflection seismic survey was conducted over an area of about 9 km2 at the Kevitsa Ni-Cu-PGE (platinum group elements) orebody, northern Finland, where open-pit mining started in mid-2012. The principal objective of the survey was to image major fault and fracture zones at depth that may have a ..."
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Cited by 2 (1 self)
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time shifts or phase changes along horizontal to gently dipping reflections. Some of these faults cross the planned open-pit mine at depths of about 300–500 m, and are therefore critical for geotechnical planning. In terms of in-pit and near-mine exploration, the mag-matic layering internal
Natural Resources Canada Targeted Geoscience Initiative 4: Increasing Deep Exploration Effectiveness Geological Survey of Canada with Provincial and Territorial Collaboration
"... The Wheeler River Property, host of Denison Mine’s Phoenix uranium deposit, is situated near the southeastern rim of the Athabasca Basin in Northern Saskatchewan (Figure 1). The mineralization drilled to date is estimated to contain from 45 to 90 million pounds U O making Phoenix, even at the lower ..."
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The Wheeler River Property, host of Denison Mine’s Phoenix uranium deposit, is situated near the southeastern rim of the Athabasca Basin in Northern Saskatchewan (Figure 1). The mineralization drilled to date is estimated to contain from 45 to 90 million pounds U O making Phoenix, even at the lower figure, the sixth largest known individual deposit in the 3 8
Exploring strategies for training deep neural networks
- Journal of Machine Learning Research
"... Département d’informatique et de recherche opérationnelle ..."
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Cited by 90 (12 self)
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Département d’informatique et de recherche opérationnelle
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