I suppose an image is worth too many words, so here is the image:

As you can see, in the middle where there are voxels to be segmented, no artifacts are present. Whereas on the top and bott...

My task consists of forecasting number of upvotes for Reddit posts at time t after posting (how many hours t it was posted ago) based on text/title/time t, current architecture is basically transformer's encoders taking text as input after which is placed a linear network taking 'how long ago was posted' and encoder's outputs as input and outputting the regression value.

I've read somewhere (I forgot the source) that we perform 2 types of EDA:

I am clustering time-series datasets which are not labeled (No Ground truth) and I want to measure the quality of the clusters. Could you please suggest any Clustering performance evaluation methods that can be used in time-series clustering ??

I am working on a multi-class text classification problem with the following constraints:

A relatively small number of target classes (~20) Very limited labeled data per class (single-digit examples) Significant feature overlap across classes (shared vocabulary and descriptors) Problem

Standard approaches based on semantic similarity or embedding proximity perfo...