Based on Definitions 1 and 2, we have the following theorem.

The use of a global population can lead to potentially misleading results when local model behaviour is of interest. Hence we consider the formulation of neighbourhood reference distributions that improve the local interpretability of Shapley values.

V4 has been reported to retain higher-order information (e.g., color and shape) and attention in visual processing (Fries et al., 2001; Orban, 2008), while PFC is considered to

The design and optimization of functional proteins that bind specific ligand molecules is paramount in therapeutics and bio-engineering.

To further improve the performance, we introduce an additional self-modulation component based on cross-attention.

DNNs, such as adversarial training (Madry et al., 2018), provide no provable robustness guarantees, Both IBP and CROWN-IBP with loss fusion (Xu et al., 2020) have a per-batch training time For example, generalized CROWN-IBP in Xu et al. (2020) used 900 epochs for warmup and 2,000 He et al., 2015a), but prior works for certified training generally use weight initialization methods originally designed for standard DNN training, while certified training is essentially optimizing a different type of augmented network defined by robustness verification (Zhang et al., 2020). It can however hamper classification performance if too many neurons are dead.

We study multi-agent reinforcement learning (MARL) in infinite-horizon discounted zero-sum Markov games.

Estimating the data uncertainty in regression tasks is often done by learning a quantile function or a prediction interval of the true label conditioned on the input.

Estimating the data uncertainty in regression tasks is often done by learning a quantile function or a prediction interval of the true label conditioned on the input.