Training Machine-Learned Models with Temporal Conditioning for Time-Aware Inference

A computer-implemented method of training machine-learned models, the method comprising:

• inputting, to a machine-learned model, a respective training input based on a respective training example;
• generating, using the machine-learned model, a first respective training output based on the respective training input, wherein the first respective training output comprises a respective temporal feature prediction;
• generating, using the machine-learned model, a second respective training output based on the respective training input, wherein the second respective training output comprises a content prediction;
• computing, using the first respective training output and a respective temporal evaluation signal for the respective training example, a temporal feature prediction loss;
• computing, using the second respective training output and a respective content evaluation signal for the respective training example, a content prediction loss;
• and
• generating, using the content prediction loss and the temporal feature prediction loss, a parameter update for one or more parameters of the machine-learned model.

A computer-implemented method of training machine-learned models, the method comprising:

• generating a temporally indexed training dataset that comprises a plurality of training examples that are respectively associated with a plurality of temporal feature values, wherein generating the temporally indexed training dataset comprises:
• for each respective training example of the plurality of training examples:
• computing a respective temporal feature value associated with the respective training example;
• and
• storing the respective temporal feature in a data structure that associates the respective temporal feature value with the respective training example;
• and
• training a machine-learned model using the temporally indexed training dataset, wherein successive parameter updates are computed based on chronologically ordered batches of training examples, wherein the chronologically ordered batches of training examples are populated with training examples based on the plurality of temporal feature values.