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cs.LG2023★ 2 cited
Training trajectories, mini-batch losses and the curious role of the learning rate
Mark Sandler, Andrey Zhmoginov, Max Vladymyrov +1
Stochastic gradient descent plays a fundamental role in nearly all applications of deep learning. However its ability to converge to a global minimum remains shrouded in mystery. I…
cs.LG2022
Anomalous behaviour in loss-gradient based interpretability methods
Vinod Subramanian, Siddharth Gururani, Emmanouil Benetos +1
Loss-gradients are used to interpret the decision making process of deep learning models. In this work, we evaluate loss-gradient based attribution methods by occluding parts of th…