**Vulnerability-constrained Decoding**, is a sequence decoding approach that aims to avoid generating vulnerabilities in generated code.

**TabNet** is a deep tabular data learning architecture that uses sequential attention to choose which features to reason from at each decision step.

The TabNet encoder is composed of a feature transformer, an attentive transformer and feature masking. A split block
divides the processed representation to be used by the attentive transformer of the subsequent step as well as for the overall output. For each step, the feature selection mask provides interpretable information about the model’s functionality, and the masks can be aggregated to obtain global feature important attribution. The TabNet decoder is composed of a feature transformer block at each step. 

In the feature transformer block, a 4-layer network is used, where 2 are shared across all decision steps and 2 are decision step-dependent. Each layer is composed of a fully-connected (FC) layer, BN and GLU nonlinearity. An attentive transformer block example – a single layer mapping is modulated with a prior scale information which aggregates how much each feature has been used before the current decision step. sparsemax is used for normalization of the coefficients, resulting in sparse selection of the salient features.

TabNet

TabNet: Attentive Interpretable Tabular Learning

Vulnerability-constrained Decoding

Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding

A **Hopfield Layer** is a module that enables a network to associate two sets of vectors. This general functionality allows for [transformer](https://paperswithcode.com/method/transformer)-like self-attention, for decoder-encoder attention, for time series prediction (maybe with positional encoding), for sequence analysis, for multiple instance learning, for learning with point sets, for combining data sources by associations, for constructing a memory, for averaging and pooling operations, and for many more. 

In particular, the Hopfield layer can readily be used as plug-in replacement for existing layers like pooling layers ([max-pooling](https://paperswithcode.com/method/max-pooling) or [average pooling](https://paperswithcode.com/method/average-pooling), permutation equivariant layers, [GRU](https://paperswithcode.com/method/gru) & [LSTM](https://paperswithcode.com/method/lstm) layers, and attention layers. The Hopfield layer is based on modern Hopfield networks with continuous states that have very high storage capacity and converge after one update.

Source	Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com