Update README.md

parent 1f731e67
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* **EpisodicMemory(replayMemoryStoreProb=1, maxStoredSamples=-1, memoryReplacementStrategy="replace_oldest", useReplay=true, replayInterval, replayBatchSize=-1, replaySteps, replayGradientSteps=1, useLocalAdaption=true, localAdaptionGradientSteps=1, localAdaptionK=1, queryNetDir=-1, queryNetPrefix=-1, queryNetNumInputs=1)**
!!! Important: Needs mxnet version 1.7.0, see installation instructions at: [installation scripts](https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMADL2CPP)!!!
!!! Important: Needs mxnet version 1.7.0, see installation instructions at: [EMADL2CPP](https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMADL2CPP)!!!
Episodic Memory as described in [1], although we implemented it as a layer which can also be used inside of a network. Works with multiple inputs. Not learned. Inputs are stored and are repleayed on part of the network following this layer. Local adaption retrives samples to the sample for which inference should be done from memory and performs finetuning learning with these on the part of the network following this layer.
......@@ -641,7 +641,7 @@ All predefined methods start with a capital letter and all constructed methods h
* **DotProductSelfAttention(scaleFactor=-1, numHeads=1, dimKeys=-1, dimValues=-1, useProjBias=true, useMask=false)**
!!! Important: Needs mxnet version 1.7.0, see installation instructions at: [installation scripts](https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMADL2CPP)!!!
!!! Important: Needs mxnet version 1.7.0, see installation instructions at: [EMADL2CPP](https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMADL2CPP)!!!
Calculates the DotProductSelfAttention. As described in [3]. Takes three inputs: queries, keys, values and optionally a mask for masked Self Attention as used in Bert.
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