One of the most popular approaches is voting by the weak learning algorithms, which may complement the weakness of each other. Standard learning algorithms consider vectorial data x. However, if data have a two-
Linear Relationships: Since Word2vec preserves linear relationships, computing linear combinations of the vectorial representations results in semantically meaningful results. Below are a few interesting relationships we found: high – $false + $true ≈’ low ‘-eq’ – $false + $true ...
Instead we envision to train specialized modules that project the vectorial representations into new representations more appropriate to the completion of semantic tasks of interest. The topology of the vectorial representation space only serves as an inductive bias that transfers some of the knowledge ...
In this work, knowledge-based, deep, ad-hoc and general-purpose representations are combined together to improve the accuracy of a BNER system. The combination has been carried out by using two representation learning paradigms. The first is the Multiple Kernel Learning [11], whose purpose is ...
Once trained, the vectorial representations that emerge from pretext tasks capture the important features of the images, and can be used for comparison and categorization. Here, we present the development, validation and use of cytoself, a deep learning-based approach for fully self-supervised ...
as letters, words, or other substring pieces of equal or unequal length. (C) Bag-of-words representation can be used to count unique tokens in a text, turning every input text into a fixed-size vector. Subsequently, these vector representations can be analyzed through any machine-learning ...
For any network, graph representation learning transforms the network to extract patterns, make predictions or gain insights, and leverages these to produce compact vectorial representations (denoted by the tube-like shapes) that can be optimized for the downstream task. The right-most schematic shows...
Ideally, we would like to map each message to a point in a vectorial subspace (embedding) where "similar" messages are close to each other, so that they can be subsequently grouped based on their location. Although more recent and powerful alternatives are avail- able for this purpose [9,...
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In the AR-enhanced version of the experiment, vectorial representations are used to vi- sualize the polarization state of a single photon. QR codes are attached to the experimental setup to ensure that the AR glasses display the visualizations in the correct locations of the setup. As soon as...