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@@ -124,7 +124,7 @@ class TRAModel(Model):
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loss = (pred - label).pow(2).mean()
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L = (all_preds.detach() - label[:, None]).pow(2)
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L -= L.min(dim=-1, keepdim=True).values # normalize & ensure postive input
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L -= L.min(dim=-1, keepdim=True).values # normalize & ensure positive input
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data_set.assign_data(index, L) # save loss to memory
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@@ -165,7 +165,7 @@ class TRAModel(Model):
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L = (all_preds - label[:, None]).pow(2)
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L -= L.min(dim=-1, keepdim=True).values # normalize & ensure postive input
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L -= L.min(dim=-1, keepdim=True).values # normalize & ensure positive input
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data_set.assign_data(index, L) # save loss to memory
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@@ -484,7 +484,7 @@ class TRA(nn.Module):
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"""Temporal Routing Adaptor (TRA)
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TRA takes historical prediction erros & latent representation as inputs,
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TRA takes historical prediction errors & latent representation as inputs,
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then routes the input sample to a specific predictor for training & inference.
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Args:
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