It learns to partition and map together using a single neural network via an alternative training and re-partitioning.

IRLI index creation takes 3 steps. First(left)(initialization step)- the labels are pooled randomly into B buckets using a 2-universal hash function. The figure shows only five buckets (while we have a few thousands in practice). Second(middle)- We train R fully-connected networks on N data points, where any bucket containing at-least one true labels is positive. Third(right): After training for a few epochs, the labels are re-assigned to the buckets. For each label, we provide a representative input to the R networks. We selectthe top-K buckets and assign the label to the least occupied bucket (K=2 in the figure yields 2nd and 3rd buckets as the top-scored ones. Light-green bucket is the lesser occupied one, and hence we assign the label to the 3rd bucket). A larger K ensures perfect load balance, while a smaller K ensures higher precision and recall.

IRLI query process. Here the query vector is passed through R trained models, and each one gives the probability scores over the corresponding buckets. Figure shows m= 1 for illustration purpose. The top candidates are sorted based on the aggregated scores of each label.

Prerequisites:

1. Tensorflow- 1.8 - 1.14 should work
2. Minimum 2 GPU, preferred >4 GPUs with 32GB memory
3. Memory required - Around 200 GB

Cython is also required for importing a C++ function gather_batch during evaluation. Although the version requirements for cython is non that stringent as Tensorflow, use Cython-0.29.14 in case you run into any issues.

Instructions to run:

1. Edit the config.py for required parameters.
2. Run preprocess.py
3. Run train_script.py
4. For inference, run approx_eval.py