First, a network was trained with Gaussian noise injection (Sec. 5.3) and subsequently tested using the delta network GRU formulation given in Sec. 3. A second network was trained …

This paper presents OptNet, a network architec-ture that integrates optimization problems (here, specifically in the form of quadratic programs) as individual layers in larger end-to-end train …

For prediction transparency, we propose the Shallow-Deep Network (SDN), a generic modification to off-the-shelf DNNs that introduces internal classifiers. We apply SDN to four modern …

Here, we propose a methodology such that training a prototype neural network will evolve into training an SNN gradually by fusing the learnable relaxation degree into the network with …

One of the main appeals of neural network-based models is that a single model architecture can often be used to solve a variety of related tasks. However, many recent advances are based …

Analogously, to mitigate network overthinking, we propose two SDN-based heuristics: the confidence-based early exits (Section 5.1) and network confusion analysis (Section 5.2).

We proved that any target network with width n, depth L and inputs in Rd can be approximated by a network with width O(d), where the number of parameters increases by only a factor of L …

We present a systematic study on how to morph a well-trained neural network to a new one so that its network function can be completely preserved. We define this as network morphism in …

For a single network (depth 4, width 16), Figure 7 indicates that this distribution does not significantly change during training, although there appears to be a slight skew towards larger …

Our empirical results show that IPM-MPNNs can lead to reduced solving times compared to a state-of-the-art LP solver with time constraints and competing neu-ral network-based …