Multiple Instance Learning (mil) Is A Variation Of Supervised Learning Where A Single Class Label Is Assigned To A Bag Of Instances. In This Paper, We State The Mil Problem As Learning The Bernoulli Distribution Of The Bag Label Where The Bag Label Probability Is Fully Parameterized By Neural Networks. Furthermore, We Propose A Neural Network-based Permutation-invariant Aggregation Operator That Corresponds To The Attention Mechanism. Notably, An Application Of The Proposed Attention-based Operator Provides Insight Into The Contribution Of Each Instance To The Bag Label. We Show Empirically That Our Approach Achieves Comparable Performance To The Best Mil Methods On Benchmark Mil Datasets And It Outperforms Other Methods On A Mnist-based Mil Dataset And Two Real-life Histopathology Datasets Without Sacrificing Interpretability.
A new system for creating code that manipulates tensors yields programs that are 100 times as efficient as those produced by existing software packages, with ramifications for big-data analysis and machine learning.