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ANSWER

denseness

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The answer DENSENESS has 5 possible clue(s) in existing crosswords.

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The word DENSENESS is VALID in some board games. Check DENSENESS in word games in Scrabble, Words With Friends, see scores, anagrams etc.

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Definitions of denseness in various dictionaries:

noun - the quality of being mentally slow and limited

noun - the spatial property of being crowded together

noun - the amount per unit size

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Possible Crossword Clues
Not sparse stupidity
Ignorance
Quality of the theme word hidden in this crossword
Stupidity of duke jumbling a lot of points up
Stupidity

Last Seen in these Crosswords & Puzzles
May 15 2017 The Times - Concise
May 5 2016 The Washington Post
Feb 21 2016 The Washington Post
Nov 10 2003 The Times - Cryptic
Nov 15 2000 Irish Times (Crosaire)

Possible Dictionary Clues
The quality of being dense, especially in the sense of limited mental capacity.
relatively opaque transmitting little light, as a photographic negative, optical glass, or color. 5. difficult to understand or follow because of being closely packed with ideas or complexities of style: a bdenseb philosophical essay.
the quality of being mentally slow and limited
the amount per unit size
the spatial property of being crowded together

Denseness might refer to
A Residual neural network is an artificial neural network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex. Residual neural networks do this by utilizing skip connections or short-cuts to jump over some layers. In its limit as ResNets it will only skip over a single layer. With an additional weight matrix to learn the skip weights it is referred to as HighwayNets. With several parallel skips it is referred to as DenseNets. In comparison, a non-residual neural network is described as a plain network in the context of residual neural networks.* The brain has structures similar to residual nets, as layer VI neurons gets input from layer I, skipping over all intermediary layers. In the figure (below, on the right), this compares to signals from the (3) apical dendrite skipping over layers, while the (2) basal dendrite collecting signals from the previous and/or same layer. Similar structures exists for other layers. How many layers in the cerebral cortex compare to layers in an artificial neural network is not clear, neither if every area in cerebral cortex exhibits the same structure, but over large areas they look quite similar. * The motivation for skipping over layers in ANNs is to avoid the problem of vanishing gradients, by reusing activation from a previous layer until the layer next to the current one have learned its weights. During training the weights will adapt to mute the previous layer and amplify the layer next to the current. In the simplest case only the weights for the connection to the next to the current layer is adapted, with no explicit weights for the upstream previous layer. This usually works properly when a single non-linear layer is stepped over, or in the case when the intermediate layers are all linear. If not, then an explicit weight matrix should be learned for the skipped connection. * The intuition on why this works is that the neural network collapses into fewer layers in the initial phase, which makes it easier to learn, and thus gradually expands the layers as it learns more of the feature space. During later learning, when all layers are expanded, it will stay closer to the manifold and thus learn faster. A neural network without residual parts will explore more of the feature space. This makes it more vulnerable to small perturbations that cause it to leave the manifold altogether, and require extra training data to get back on track.

Denseness might refer to

A Residual neural network is an artificial neural network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex. Residual neural networks do this by utilizing skip connections or short-cuts to jump over some layers. In its limit as ResNets it will only skip over a single layer. With an additional weight matrix to learn the skip weights it is referred to as HighwayNets. With several parallel skips it is referred to as DenseNets. In comparison, a non-residual neural network is described as a plain network in the context of residual neural networks.* The brain has structures similar to residual nets, as layer VI neurons gets input from layer I, skipping over all intermediary layers. In the figure (below, on the right), this compares to signals from the (3) apical dendrite skipping over layers, while the (2) basal dendrite collecting signals from the previous and/or same layer. Similar structures exists for other layers. How many layers in the cerebral cortex compare to layers in an artificial neural network is not clear, neither if every area in cerebral cortex exhibits the same structure, but over large areas they look quite similar.
* The motivation for skipping over layers in ANNs is to avoid the problem of vanishing gradients, by reusing activation from a previous layer until the layer next to the current one have learned its weights. During training the weights will adapt to mute the previous layer and amplify the layer next to the current. In the simplest case only the weights for the connection to the next to the current layer is adapted, with no explicit weights for the upstream previous layer. This usually works properly when a single non-linear layer is stepped over, or in the case when the intermediate layers are all linear. If not, then an explicit weight matrix should be learned for the skipped connection.
* The intuition on why this works is that the neural network collapses into fewer layers in the initial phase, which makes it easier to learn, and thus gradually expands the layers as it learns more of the feature space. During later learning, when all layers are expanded, it will stay closer to the manifold and thus learn faster. A neural network without residual parts will explore more of the feature space. This makes it more vulnerable to small perturbations that cause it to leave the manifold altogether, and require extra training data to get back on track.

Related Answers
ASININITY
DOPINESS
DUMBNESS
FOLLY
FOOLISHNESS
IDIOCY
IGNORANCE
INANITY
SENSELESSNESS

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