similarlysome tasks need to predict multiple target attributes of input data given the text of novel or short storyyou might want to automatically classify it by genre (such as romance or thrillerbut also predict the approximate date it was written of courseyou could train two separate modelsone for the genre and one for the date but because these attributes aren' statistically independentyou could build better model by learning to jointly predict both genre and date at the same time such joint model would then have two outputsor heads (see figure due to correlations between genre and dateknowing the date of novel would help the model learn richaccurate representations of the space of novel genresand vice versa genre date genre classifier date regressor text-processing module novel text figure mult -out put (or multiheadmodel additionallymany recently developed neural architectures require nonlinear network topologynetworks structured as directed acyclic graphs the inception family of networks (developed by szegedy et al at google), for instancerelies on inception moduleswhere the input is processed by several parallel convolutional branches whose outputs are then merged back into single tensor (see figure there' also the recent trend of adding residual connections to modelwhich started with the resnet family of networks (developed by he et al at microsoft residual connection consists of reinjecting previous representations into the downstream flow of data by adding past output tensor to later output tensor (see figure )which helps prevent information loss along the data-processing flow there are many other examples of such graph-like networks christian szegedy et al "going deeper with convolutions,conference on computer vision and pattern recognition ( )kaiming he et al "deep residual learning for image recognition,conference on computer vision and pattern recognition ( )licensed to