---

input (aspect of neural network) → ReceivesAction → weighted	0.84
layer (aspect of neural network) → ReceivesAction → connected	0.84
neural network → ReceivesAction → trained	0.79
input (aspect of neural network) → ReceivesAction → normalized	0.74
artificial neural network (subgroup of neural network) → HasProperty → effective	0.68
convolutional neural network (subgroup of neural network) → CapableOf → comprise network parameters	0.68
deep neural network (subgroup of neural network) → IsA → artificial neural network	0.68
number (aspect of neural network) → HasProperty → equal	0.68
output layer (aspect of neural network) → ReceivesAction → reached	0.68
output layer (aspect of neural network) → CapableOf → provide result	0.68
weight (aspect of neural network) → ReceivesAction → adjusted	0.68
weight (aspect of neural network) → ReceivesAction → determined	0.66
neural network → ReceivesAction → employed	0.65
artificial neural network (subgroup of neural network) → CapableOf → compute system	0.63
neural network → CapableOf → make prediction	0.61
neural network → ReceivesAction → created	0.60
neural network → CapableOf → classify image	0.59
input (aspect of neural network) → ReceivesAction → received	0.58
neural network → HasA → an input layer	0.57
neural network → HasA → multiple layers	0.57
neural network → CapableOf → use datum	0.56
neural network → UsedFor → classification	0.55
neural network → CapableOf → create connection	0.55
neural network → CapableOf → produce output	0.55
number (aspect of neural network) → ReceivesAction → determined	0.55
weight (aspect of neural network) → ReceivesAction → modified	0.55
neural network → CapableOf → recognize pattern	0.53
input (aspect of neural network) → HasA → weight	0.52
input (aspect of neural network) → ReceivesAction → sampled	0.52
neural network → CapableOf → learn by example	0.52
neural network → UsedFor → deep learning	0.52
number (aspect of neural network) → HasProperty → small	0.51
number (aspect of neural network) → HasProperty → large	0.51
neural network → HasProperty → effective	0.51
neural network → CapableOf → learn	0.51
neural network → ReceivesAction → adjusted	0.49
neural network → ReceivesAction → modeled after human brain	0.49
layer (aspect of neural network) → ReceivesAction → added	0.48
neural network → CapableOf → take input	0.48
neural network → HasProperty → complex	0.48
neural network → CapableOf → process information	0.47
neural network → CapableOf → analyze datum	0.47
neural network → CapableOf → extract feature	0.47
neural network → ReceivesAction → organized in layer	0.46
neural network → CapableOf → make decision	0.46
number (aspect of neural network) → HasProperty → larger	0.45
neural network → HasProperty → useful	0.45
input (aspect of neural network) → ReceivesAction → multiplied by weight	0.45
neural network → ReceivesAction → described	0.44
layer (aspect of neural network) → CapableOf → take input	0.44
layer (aspect of neural network) → CapableOf → search for feature	0.44
neural network → CapableOf → identify object	0.44
weight (aspect of neural network) → HasProperty → zero	0.43
neural network → ReceivesAction → inspired by brain	0.43
neural network → CapableOf → solve problem	0.43
neural network → ReceivesAction → reprogrammed	0.43
neural network → HasProperty → fast	0.43
weight (aspect of neural network) → ReceivesAction → fixed	0.42
neural network → CapableOf → mimic the brain	0.42
neural network → CapableOf → learn task	0.42
neural network → CapableOf → pattern recognition	0.41
neural network → ReceivesAction → shown in figure	0.41
neural network → CapableOf → provide solution	0.40
neural network → CapableOf → involve large number of processors	0.40
neural network → ReceivesAction → used in speech	0.40
weight (aspect of neural network) → ReceivesAction → increased	0.40
neural network → ReceivesAction → utilized to volatility modeling	0.39
neural network → ReceivesAction → utilized to choice pricing	0.39
neural network → CapableOf → perform specific task	0.39
neural network → ReceivesAction → inspired by the way	0.39
neural network → ReceivesAction → based on mathematic	0.39
neural network → CapableOf → adjust weight	0.38
neural network → CapableOf → learn to recognize cat	0.38
number (aspect of neural network) → HasProperty → important	0.37
number (aspect of neural network) → HasProperty → high	0.37
weight (aspect of neural network) → ReceivesAction → associated with input	0.37
neural network → CapableOf → choose the most suitable advertisement	0.36
neural network → ReceivesAction → trained by supervised learning	0.36
neural network → ReceivesAction → trained to predict alphago’s own move selections	0.36
neural network → HasSubevent → technological advances	0.36
neural network → ReceivesAction → composed of neuron	0.36
neural network → HasProperty → deep	0.36
input (aspect of neural network) → CapableOf → produce output	0.36
neural network → CapableOf → use keras	0.35
neural network → CapableOf → evaluate the interests of users	0.35
neural network → CapableOf → calculate the likelihood of targeted actions	0.35
neural network → ReceivesAction → trained to predict the winner of alphago’s games	0.35
neural network → CapableOf → recognize face	0.35
neural network → HasProperty → flexible	0.35
neural network → HasProperty → powerful	0.34
neural network → CapableOf → communicate with each other	0.34
neural network → CapableOf → make guess	0.34
neural network → CapableOf → lack explicit information	0.34
neural network → ReceivesAction → applied to economic and business problems	0.34
neural network → CapableOf → learn the relationship	0.34
neural network → CapableOf → compute system	0.34
neural network → CapableOf → generate image	0.34
neural network → CapableOf → generalize	0.34
weight (aspect of neural network) → HasProperty → small	0.34
layer (aspect of neural network) → HasA → a number of neurons	0.33
artificial neural network (subgroup of neural network) → CapableOf → call neural network	0.33
neural network → HasProperty → well-suited	0.32
neural network → CapableOf → evolve over time	0.32
neural network → HasProperty → sensitive	0.32
neural network → CapableOf → predict the way	0.32
neural network → CapableOf → take into account	0.32
neural network → CapableOf → approached human ability	0.32
neural network → ReceivesAction → composed of node	0.32
neural network → CapableOf → form memory	0.32
neural network → CapableOf → underlie decision-making	0.32
neural network → CapableOf → learn on own	0.32
neural network → HasProperty → intensive	0.32
neural network → CapableOf → make mistake	0.32
number (aspect of neural network) → ReceivesAction → reduced	0.32
number (aspect of neural network) → ReceivesAction → increased	0.32
number (aspect of neural network) → ReceivesAction → limited	0.32
number (aspect of neural network) → HasProperty → zero	0.32
neural network → CapableOf → become popular	0.32
neural network → CapableOf → compete with each other	0.30
neural network → ReceivesAction → composed of synapsis	0.30
neural network → ReceivesAction → tested	0.30
neural network → CapableOf → predict the winner of the game	0.30
neural network → CapableOf → learn differential data	0.30
neural network → ReceivesAction → updated	0.30
weight (aspect of neural network) → CapableOf → specify the strength of the influence	0.30
weight (aspect of neural network) → CapableOf → minimize the error	0.30
weight (aspect of neural network) → ReceivesAction → tuned	0.30
weight (aspect of neural network) → HasProperty → negative	0.30
neural network → HasProperty → simple	0.29
neural network → HasA → dozens of neurons	0.28
weight (aspect of neural network) → ReceivesAction → shared	0.28
neural network → CapableOf → combine input	0.28
neural network → ReceivesAction → involved in attention	0.28
neural network → ReceivesAction → coded	0.28
neural network → ReceivesAction → activated	0.28
neural network → UsedFor → regression	0.28
neural network → CapableOf → identify feature	0.28
neural network → ReceivesAction → trained for 200 epochs	0.28
neural network → HasA → hidden layer	0.28
neural network → ReceivesAction → learned as the fluctuation width	0.28
neural network → ReceivesAction → associated with emotion	0.28
neural network → CapableOf → generate functional mapping	0.28
neural network → CapableOf → build insulation	0.28
neural network → HasSubevent → higher quality move selection	0.28
neural network → HasSubevent → next iteration	0.28
neural network → HasProperty → robust	0.28
input (aspect of neural network) → ReceivesAction → determined to be unreliable	0.27
neural network → CapableOf → grow new connections	0.26
neural network → CapableOf → strengthen existing ones	0.26
neural network → HasSubevent → stronger self-play	0.26
neural network → HasProperty → accurate	0.26
neural network → CapableOf → recognize number	0.25
neural network → CapableOf → detect	0.25
neural network → CapableOf → use backpropagation	0.25
neural network → CapableOf → track the way	0.25
neural network → CapableOf → analyze their expressions	0.25
neural network → CapableOf → guess at their emotions	0.25
neural network → CapableOf → learn the mapping	0.25
neural network → ReceivesAction → used as classifier	0.25
neural network → CapableOf → select next move	0.25
neural network → HasA → weight	0.25
neural network → CapableOf → underlie the encoding	0.25
neural network → CapableOf → access conventional turing machine	0.25
neural network → CapableOf → learn like human brain	0.25
neural network → ReceivesAction → connected to 3d sensor	0.25
neural network → ReceivesAction → connected to robotic arm	0.25
neural network → CapableOf → minimize the cross entropy	0.25
neural network → CapableOf → do the rest	0.25
neural network → ReceivesAction → patterned after the operation of neurons	0.25
neural network → CapableOf → use gpu	0.25
neural network → ReceivesAction → based on observed efficacy	0.25
neural network → ReceivesAction → based on statistic	0.25
neural network → ReceivesAction → based on probability	0.25
neural network → CapableOf → provide low dimensional embedding	0.25
neural network → CapableOf → outperform human	0.25
neural network → CapableOf → perform on constant basis	0.25
neural network → CapableOf → classify remaining photos	0.25
neural network → CapableOf → use gradient descent	0.25
neural network → CapableOf → learn embedding	0.25
neural network → CapableOf → return value	0.25
neural network → CapableOf → repeat the process	0.25
neural network → CapableOf → learn quantum mechanics	0.25
neural network → HasProperty → nonlinear	0.25
neural network → CapableOf → understand the world	0.25
neural network → HasProperty → unpredictable	0.25
neural network → CapableOf → use tensorflow	0.25
neural network → HasProperty → recurrent	0.25
neural network → ReceivesAction → based on self-organizing maps	0.25
neural network → HasA → 60 million parameters	0.25
neural network → CapableOf → consist of five convolutional layers	0.25
neural network → CapableOf → account	0.25
neural network → ReceivesAction → trained to perform more than 20 tasks	0.25
neural network → CapableOf → call isthmic system	0.25
neural network → HasA → single layer of data	0.25
neural network → CapableOf → understand style preferences	0.25
neural network → CapableOf → understand other aesthetic parameters	0.25
neural network → ReceivesAction → given a patient’s symptoms	0.25
neural network → HasA → cycle	0.25
neural network → CapableOf → explore possible ways	0.25
neural network → CapableOf → outperform svm	0.25
neural network → HasA → many parameters	0.25
neural network → ReceivesAction → configured	0.25
neural network → ReceivesAction → made up of real biological neurons	0.25
neural network → CapableOf → make probabilistic predictions	0.25
neural network → ReceivesAction → likely to pick	0.25
neural network → CapableOf → use reinforcement learning	0.25
artificial neural network (subgroup of neural network) → CapableOf → make prediction	0.25
artificial neural network (subgroup of neural network) → ReceivesAction → implemented on von neumann computers	0.25
artificial neural network (subgroup of neural network) → CapableOf → emulate process	0.25
artificial neural network (subgroup of neural network) → ReceivesAction → inspired by the way	0.25
input (aspect of neural network) → ReceivesAction → multiplied by the weights of the connections	0.25
input (aspect of neural network) → HasProperty → zero	0.25
input (aspect of neural network) → HasProperty → positive	0.25
input (aspect of neural network) → CapableOf → represent query	0.25
input (aspect of neural network) → CapableOf → apply the high-capacity sub	0.25
input (aspect of neural network) → ReceivesAction → transmitted to remote processing system	0.25
layer (aspect of neural network) → MadeOf → different material	0.25
layer (aspect of neural network) → HasA → different thickness	0.25
layer (aspect of neural network) → ReceivesAction → removed	0.25
layer (aspect of neural network) → CapableOf → form a hierarchy of low-level	0.25
layer (aspect of neural network) → HasProperty → specific	0.25
number (aspect of neural network) → CapableOf → depend on the number of possible inputs	0.25
number (aspect of neural network) → CapableOf → depend on the number of desired outputs	0.25
number (aspect of neural network) → ReceivesAction → performed	0.25
number (aspect of neural network) → ReceivesAction → checked	0.25
number (aspect of neural network) → ReceivesAction → employed	0.25
number (aspect of neural network) → HasProperty → odd	0.25
number (aspect of neural network) → HasProperty → smaller	0.25
number (aspect of neural network) → ReceivesAction → kept modest	0.25
weight (aspect of neural network) → HasProperty → equal	0.25
prediction → CreatedBy → neural network	0.25
neural network → HasProperty → slow	0.23
neural network → CapableOf → become good	0.23
neural network → CapableOf → use the python programming language	0.23
neural network → HasProperty → successful	0.22
neural network → CapableOf → identify false positives	0.22
neural network → ReceivesAction → responsible for efficiency	0.19
neural network → HasProperty → large	0.19
neural network → CapableOf → mark triumph	0.19
neural network → CapableOf → suffer	0.19
neural network → CapableOf → analyze complex distortions	0.19
neural network → CapableOf → show cardiologist-level performance	0.19
neural network → CapableOf → derive biomarkers of age	0.19
neural network → CapableOf → predict mortality	0.19
neural network → CapableOf → pursue interdisciplinary perspectives	0.19
neural network → HasProperty → chaotic	0.19
neural network → CapableOf → mimic simulated environment	0.19
neural network → CapableOf → enable artificial intelligence	0.19
neural network → CapableOf → mimic signal	0.19
neural network → CapableOf → improve the strength of tree search	0.16
neural network → CapableOf → like beige	0.16
neural network → CapableOf → re over past few years	0.16
neural network → CapableOf → emerged as powerful machine-learning models	0.16
neural network → ReceivesAction → combined	0.16
neural network → CapableOf → outperform random forest	0.16
neural network → ReceivesAction → used in self-driving cars	0.16
neural network → CapableOf → use only 30 watts	0.16
neural network → CapableOf → do in their own tests	0.16
neural network → CapableOf → disclose significant technical knowledge	0.16
neural network → HasProperty → perfect	0.16
neural network → ReceivesAction → based on polymeric memristors	0.16
neural network → ReceivesAction → trained to recognize visual scenes	0.16
neural network → CapableOf → emulate the atari gaming environment	0.16
neural network → CapableOf → avoid obstacle	0.16
artificial neural network (subgroup of neural network) → ReceivesAction → applied to speech recognition	0.16
artificial neural network (subgroup of neural network) → ReceivesAction → applied to image analysis	0.16
artificial neural network (subgroup of neural network) → ReceivesAction → applied to adaptive control	0.16
input (aspect of neural network) → HasProperty → negative	0.16
weight (aspect of neural network) → HasProperty → wrong	0.16