If your directional bias is to the downside, selling a call vertical might be your go-to strategy choice. If you keep to out-of-the-money strikes, the odds of finishing with a profitable trade are on your side, but as noted above, the maximum loss is much greater than the maximum ...
In graph-level representation learning tasks, graph neural networks have received much attention for their powerful feature learning capabilities. However, with the increasing scales of graph data, how to efficiently process and extract the key information has become the focus of research. The graph p...
This paper proposes a political bias discrimination method of news based on a heterogeneous neural network, with multiple information related to prejudice in the news as the nodes of a heterogeneous network. By enriching the representation of nodes through a heterogeneous graph neural network and ...
zero-shot performance w.r.t. the amount of training data (right)The outcome outlines the following key observations: (see Sec. 4.3 for details)Generalizability of AnyGraph Follows the Scaling Law. Emergent Abilities of AnyGraph. Insufficient Training Data May Bring Bias.Ablation...
The layer class NodeFormerConv implements one-layer feed-forward of NodeFormer (which contains MP on a latent graph, adding relational bias and computing edge-level reg loss from input graphs if available). The model class NodeFormer implements the model that adopts standard input (node features...
For instance, people get influenced by others, but also tend to search and recall information and facts that align with their already formed belief system (confirmation bias). Furthermore, users interact preferably with people of similar profiles and opinions (homophily), a tendency that greatly ...
This work is supported by the European Union - FSE-REACT-EU, PON Research and Innovation 2014–2020 DM1062/2021 contract number 18-I-15350-2, and was partially supported by the Ministry of University and Research, PRIN research project “BRIO – BIAS, RISK, OPACITY in AI: design, verificat...
In the third graph, I propose a causal graph indicating the frequency bias in the next-basket recommendation and a model based on machine learning is derived to solve the problem. In the last work, I propose a model based on the graph attention network to learn the item graph in session-...
In the third graph, I propose a causal graph indicating the frequency bias in the next-basket recommendation and a model based on machine learning is derived to solve the problem. In the last work, I propose a model based on the graph attention network to learn the item graph in session-...
(0\right)}\) is X, that is, the feature matrix is the input of the first layer of GCN, \({W}^{\left(l-1\right)}\) and \({b}^{\left(l-1\right)}\) are the weight matrix and bias in the \(\left(l-1\right)\)-th GCN layer and \({\upsigma }\) is an activation ...