**CSPPeleeNet** is a convolutional neural network and object detection backbone  where we apply the Cross Stage Partial Network (CSPNet) approach to [PeleeNet](https://paperswithcode.com/method/peleenet). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network.

Hyperboloid Embeddings (HypE) is a novel self-supervised dynamic reasoning framework, that utilizes positive first-order existential queries on a KG to learn representations of its entities and relations as hyperboloids in a Poincaré ball. HypE models the positive first-order queries as geometrical translation (t), intersection ($\cap$), and union ($\cup$). For the problem of KG reasoning in real-world datasets, the proposed HypE model significantly outperforms the state-of-the art results. HypE is also applied to an anomaly detection task on a popular e-commerce website product taxonomy as well as hierarchically organized web articles and demonstrate significant performance improvements compared to existing baseline methods. Finally, HypE embeddings can also be visualized in a Poincaré ball to clearly interpret and comprehend the representation space.

HypE

Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs

CSPPeleeNet

CSPNet: A New Backbone that can Enhance Learning Capability of CNN

# [Spectral Clustering](https://paperswithcode.com/method/spectral-clustering)

Spectral clustering aims to partition the data points into $k$ clusters using the spectrum of the graph Laplacians 
Given a dataset $X$ with $N$ data points, spectral clustering algorithm first constructs similarity matrix ${W}$, where ${w_{ij}}$ indicates the similarity between data points $x_i$ and $x_j$ via a similarity measure metric.

Let $L=D-W$, where $L$ is called graph Laplacian and ${D}$ is a diagonal matrix with $d_{ii} = \sum_ {j=1}^n w_{ij}$.
The objective function of spectral clustering can be formulated based on the graph Laplacian as follow:
\begin{equation}
  \label{eq:SC_obj}
  {\max_{{U}}  \operatorname{tr}\left({U}^{T} {L} {U}\right)}, \\ {\text { s.t. } \quad {U}^{T} {{U}={I}}},
\end{equation}
where $\operatorname{tr(\cdot)}$ denotes the trace norm of a matrix.
The rows of matrix ${U}$ are the low dimensional embedding of the original data points.
Generally, spectral clustering computes ${U}$ as the bottom $k$ eigenvectors of ${L}$, and finally applies $k$-means on ${U}$ to obtain the clustering results.


# Large-scale Spectral Clustering

To capture the relationship between all data points in $X$, an $N\times N$ similarity matrix is needed to be constructed in conventional spectral clustering, which costs $O(N^2d)$ time and $O(N^2)$ memory and is not feasible for large-scale clustering tasks.
Instead of a full similarity matrix, many accelerated spectral clustering methods are using a similarity sub-matrix to represent each data points by the cross-similarity between data points and a set of representative data points (i.e., landmarks) via some similarity measures, as
\begin{equation}
    \label{eq: cross-similarity}
    B = \Phi(X,R),
\end{equation}
where $R = \{r_1,r_2,\dots, r_p \}$ ($p \ll N$) is a set of landmarks with the same dimension to $X$, $\Phi(\cdot)$ indicate a similarity measure metric, and $B\in \mathbb{R}^{N\times p}$ is the similarity sub-matrix to represent the $X \in \mathbb{R}^{N\times d}$ with respect to the $R\in \mathbb{R}^{p\times d}$.

For large-scale spectral clustering using such similarity matrix,
a symmetric similarity matrix $W$ can be designed as 
\begin{equation}
  \label{eq: WusedB }
  W=\left[\begin{array}{ll}
      \mathbf{0} & B         ; \\
      B^{T}      & \mathbf{0}
    \end{array}\right].
\end{equation}
The size of matrix $W$ is $(N+p)\times (N+p)$. 
Taking the advantage of the bipartite structure, some fast eigen-decomposition methods can then  be used to obtain the spectral embedding.
Finally, $k$-means is conducted on the embedding to obtain clustering results.

The clustering result is directly related to the quality of $B$ that consists of the similarities between data points and landmarks.
Thus, the performance of landmark selection is crucial to the clustering result.

Source	CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com