Clustering

GPT-4 is a transformer based model pre-trained to predict the next token in a document.

Feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction.

Spectral clustering has attracted increasing attention due to the promising ability in dealing with nonlinearly separable datasets [15], [16]. In spectral clustering, the spectrum of the graph Laplacian is used to reveal the cluster structure. The spectral clustering algorithm mainly consists of two steps: 1) constructs the low dimensional embedded representation of the data based on the eigenvectors of the graph Laplacian, 2) applies k-means on the constructed low dimensional data to obtain the clustering result. Thus,

Embeddings from Language Models, or ELMo, is a type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. A biLM combines both a forward and backward LM. ELMo jointly maximizes the log likelihood of the forward and backward directions. To add ELMo to a supervised model, we freeze the weights of the biLM and then concatenate the ELMo vector $\textbf{ELMO}^{task}_k$ with $\textbf{x}_k$ and pass the ELMO enhanced representation $[\textbf{x}_k; \textbf{ELMO}^{task}_k]$ into the task RNN. Here $\textbf{x}_k$ is a context-independent token representation for each token position. Image Source: here

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.

MPNet is a pre-training method for language models that combines masked language modeling (MLM) and permuted language modeling (PLM) in one view. It takes the dependency among the predicted tokens into consideration through permuted language modeling and thus avoids the issue of BERT. On the other hand, it takes position information of all tokens as input to make the model see the position information of all the tokens and thus alleviates the position discrepancy of XLNet. The training objective of MPNet is: $$ \mathbb{E}\_{z\in{\mathcal{Z}\_{n}}} \sum^{n}\_{t=c+1}\log{P}\left(x\_{z\_{t}}\mid{x\_{z\_{<t}}}, M\_{z\_{{>}{c}}}; \theta\right) $$ As can be seen, MPNet conditions on ${x\_{z\_{<t}}}$ (the tokens preceding the current predicted token $x\_{z\_{t}}$) rather than only the non-predicted tokens ${x\_{z\_{<=c}}}$ in MLM; comparing with PLM, MPNet takes more information (i.e., the mask symbol $[M]$ in position $z\_{>c}$) as inputs. Although the objective seems simple, it is challenging to implement the model efficiently. For details, see the paper.

In relation-aware global attention (RGA) stresses the importance of global structural information provided by pairwise relations, and uses it to produce attention maps. RGA comes in two forms, spatial RGA (RGA-S) and channel RGA (RGA-C). RGA-S first reshapes the input feature map $X$ to $C\times (H\times W)$ and the pairwise relation matrix $R \in \mathbb{R}^{(H\times W)\times (H\times W)}$ is computed using \begin{align} Q &= \delta(W^QX) \end{align} \begin{align} K &= \delta(W^KX) \end{align} \begin{align} R &= Q^TK \end{align} The relation vector $r_i$ at position $i$ is defined by stacking pairwise relations at all positions: \begin{align} r_i = [R(i, :); R(:,i)] \end{align} and the spatial relation-aware feature $y_i$ can be written as \begin{align} Y_i = [g^c_\text{avg}(\delta(W^\varphi x_i)); \delta(W^\phi r_i)] \end{align} where $g^c_\text{avg}$ denotes global average pooling in the channel domain. Finally, the spatial attention score at position $i$ is given by \begin{align} a_i = \sigma(W_2\delta(W_1y_i)) \end{align} RGA-C has the same form as RGA-S, except for taking the input feature map as a set of $H\times W$-dimensional features. RGA uses global relations to generate the attention score for each feature node, so provides valuable structural information and significantly enhances the representational power. RGA-S and RGA-C are flexible enough to be used in any CNN network; Zhang et al. propose using them jointly in sequence to better capture both spatial and cross-channel relationships.

Domain Adaptive Neighborhood Clustering via Entropy Optimization (DANCE) is a self-supervised clustering method that harnesses the cluster structure of the target domain using self-supervision. This is done with a neighborhood clustering technique that self-supervises feature learning in the target. At the same time, useful source features and class boundaries are preserved and adapted with a partial domain alignment loss that the authors refer to as entropy separation loss. This loss allows the model to either match each target example with the source, or reject it as unknown.

DeepCluster is a self-supervision approach for learning image representations. DeepCluster iteratively groups the features with a standard clustering algorithm, k-means, and uses the subsequent assignments as supervision to update the weights of the network

MagFace is a category of losses for face recognition that learn a universal feature embedding whose magnitude can measure the quality of a given face. Under the new loss, it can be proven that the magnitude of the feature embedding monotonically increases if the subject is more likely to be recognized. In addition, MagFace introduces an adaptive mechanism to learn a well-structured within-class feature distributions by pulling easy samples to class centers while pushing hard samples away. For face recognition, MagFace helps prevent model overfitting on noisy and low-quality samples by an adaptive mechanism to learn well-structured within-class feature distributions -- by pulling easy samples to class centers while pushing hard samples away.

SCCL, or Supporting Clustering with Contrastive Learning, is a framework to leverage contrastive learning to promote better separation in unsupervised clustering. It combines the top-down clustering with the bottom-up instance-wise contrastive learning to achieve better inter-cluster distance and intra-cluster distance. During training, we jointly optimize a clustering loss over the original data instances and an instance-wise contrastive loss over the associated augmented pairs.

Symbolic rule learning methods find regularities in data that can be expressed in the form of 'if-then' rules based on symbolic representations of the data.

Cluster-GCN is a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure. Cluster-GCN works as the following: at each step, it samples a block of nodes that associate with a dense subgraph identified by a graph clustering algorithm, and restricts the neighborhood search within this subgraph. This simple but effective strategy leads to significantly improved memory and computational efficiency while being able to achieve comparable test accuracy with previous algorithms. Description and image from: Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

DeCLUTR is an approach for learning universal sentence embeddings that utilizes a self-supervised objective that does not require labelled training data. The objective learns universal sentence embeddings by training an encoder to minimize the distance between the embeddings of textual segments randomly sampled from nearby in the same document.

SCAN automatically groups images into semantically meaningful clusters when ground-truth annotations are absent. SCAN is a two-step approach where feature learning and clustering are decoupled. First, a self-supervised task is employed to obtain semantically meaningful features. Second, the obtained features are used as a prior in a learnable clustering approach. Image source: Gansbeke et al.

MinCutPool is a trainable pooling operator for graphs that learns to map nodes into clusters. The method is trained to approximate the minimum K-cut of the graph to ensure that the clusters are balanced, while also jointly optimizing the objective of the task at hand.

SAFRAN is a rule application framework which aggregates rules through a scalable clustering algorithm.

Agglomerative Contextual Decomposition (ACD) is an interpretability method that produces hierarchical interpretations for a single prediction made by a neural network, by scoring interactions and building them into a tree. Given a prediction from a trained neural network, ACD produces a hierarchical clustering of the input features, along with the contribution of each cluster to the final prediction. This hierarchy is optimized to identify clusters of features that the DNN learned are predictive.

SMITH, or Siamese Multi-depth Transformer-based Hierarchical Encoder, is a Transformer-based model for document representation learning and matching. It contains several design choices to adapt self-attention models for long text inputs. For the model pre-training, a masked sentence block language modeling task is used in addition to the original masked word language model task used in BERT, to capture sentence block relations within a document. Given a sequence of sentence block representation, the document level Transformers learn the contextual representation for each sentence block and the final document representation.

Structurally Regularized Deep Clustering, or SRDC, is a deep network based discriminative clustering method for domain adaptation that minimizes the KL divergence between predictive label distribution of the network and an introduced auxiliary one. Replacing the auxiliary distribution with that formed by ground-truth labels of source data implements the structural source regularization via a simple strategy of joint network training.