SmoothHess is applied post-hoc, requires no modifications to the ReLU network architecture, and the extent of smoothing can be controlled explicitly.

Please note that an "interaction" in this paper is a statistical interaction that describes a non-additive

Existing deep learning based solutions typically restore the target layers individually, or with some concerns at the end of the output, barely taking into account the interaction across thetwostreams/branches. Inorder toutilize information more efficiently, this work presents a general yet simple interactive strategy, namely your trash is my treasure(YTMT), for constructing dual-stream decomposition networks.

Most recent research hasaddressed thisby focusing onlocal interpretability, which explains a model's individual predictions (e.g., the role of each feature in a patient's diagnosis) [25, 30, 34, 38]. Twospecial cases areS = andS = D, which respectively correspond to the mean prediction f (x ) = E[f(X)] and the full model predictionfD(x) = f(x).

Previous works attempting to close this gap have failed to fully investigate the exponentially growing number of feature combinations which deep networks consider automatically during training. In this work, we develop a tractable selection algorithm to efficiently identify the necessary feature combinations byleveraging techniques infeature interaction detection. Our proposed Sparse Interaction AdditiveNetworks (SIAN) construct abridge from thesesimple andinterpretable models tofullyconnected neuralnetworks.

In contrast, linear RNNs have low computational complexity and are suitable for long-range modeling.