MUlTI-LABEL-ClASSIFICATION

A Graph Attention Network (GAT) is a neural network architecture that operates on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods’ features, a GAT enables (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. See here for an explanation by DGL.

node2vec is a framework for learning graph embeddings for nodes in graphs. Node2vec maximizes a likelihood objective over mappings which preserve neighbourhood distances in higher dimensional spaces. From an algorithm design perspective, node2vec exploits the freedom to define neighbourhoods for nodes and provide an explanation for the effect of the choice of neighborhood on the learned representations. For each node, node2vec simulates biased random walks based on an efficient network-aware search strategy and the nodes appearing in the random walk define neighbourhoods. The search strategy accounts for the relative influence nodes exert in a network. It also generalizes prior work alluding to naive search strategies by providing flexibility in exploring neighborhoods.

Sparsemax is a type of activation/output function similar to the traditional softmax, but able to output sparse probabilities. $$ \text{sparsemax}\left(z\right) = \arg\_{p∈\Delta^{K−1}}\min||\mathbf{p} - \mathbf{z}||^{2} $$

End-to-End Neural Diarization is a neural network for speaker diarization in which a neural network directly outputs speaker diarization results given a multi-speaker recording. To realize such an end-to-end model, the speaker diarization problem is formulated as a multi-label classification problem and a permutation-free objective function is introduced to directly minimize diarization errors. The EEND method can explicitly handle speaker overlaps during training and inference. Just by feeding multi-speaker recordings with corresponding speaker segment labels, the model can be adapted to real conversations.

DenseNet-Elastic is a convolutional neural network that is a modification of a DenseNet with elastic blocks (extra upsampling and downsampling).

Elastic Dense Block is a skip connection block that modifies the Dense Block with downsamplings and upsamplings in parallel branches at each layer to let the network learn from a data scaling policy in which inputs are processed at different resolutions in each layer. It is called "elastic" because each layer in the network is flexible in terms of choosing the best scale by a soft policy.

An Elastic ResNeXt Block is a modification of the ResNeXt Block that adds downsamplings and upsamplings in parallel branches at each layer. It is called "elastic" because each layer in the network is flexible in terms of choosing the best scale by a soft policy.

ResNeXt-Elastic is a convolutional neural network that is a modification of a ResNeXt with elastic blocks (extra upsampling and downsampling).

SKEP is a self-supervised pre-training method for sentiment analysis. With the help of automatically-mined knowledge, SKEP conducts sentiment masking and constructs three sentiment knowledge prediction objectives, so as to embed sentiment information at the word, polarity and aspect level into pre-trained sentiment representation. In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair. SKEP contains two parts: (1) Sentiment masking recognizes the sentiment information of an input sequence based on automatically-mined sentiment knowledge, and produces a corrupted version by removing these informations. (2) Sentiment pre-training objectives require the transformer to recover the removed information from the corrupted version. The three prediction objectives on top are jointly optimized: Sentiment Word (SW) prediction (on $\left.\mathrm{x}\_{9}\right)$, Word Polarity (SP) prediction (on $\mathrm{x}\_{6}$ and $\mathbf{x}\_{9}$ ), Aspect-Sentiment pairs (AP) prediction (on $\mathbf{x}\_{1}$ ). Here, the smiley denotes positive polarity. Notably, on $\mathrm{x}\_{6}$, only SP is calculated without SW, as its original word has been predicted in the pair prediction on $\mathbf{x}\_{1}$.

A TResNet is a variant on a ResNet that aim to boost accuracy while maintaining GPU training and inference efficiency. They contain several design tricks including a SpaceToDepth stem, Anti-Alias downsampling, In-Place Activated BatchNorm, Blocks selection and squeeze-and-excitation layers.