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Found 30 papers, 14 papers with code
Shimo Lab at "Discharge Me!": Discharge Summarization by Prompt-Driven Concatenation of Electronic Health Record Sections
Our approach involves a first step of extracting the relevant sections from the EHR.
Revisiting Cosine Similarity via Normalized ICA-transformed Embeddings
Cosine similarity is widely used to measure the similarity between two embeddings, while interpretations based on angle and correlation coefficient are common.
Predicting Drug-Gene Relations via Analogy Tasks with Word Embeddings
In this study, we demonstrate that BioConceptVec embeddings, along with our own embeddings trained on PubMed abstracts, contain information about drug-gene relations and can predict target genes from a given drug through analogy computations.
Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding
The primary aim of Knowledge Graph embeddings (KGE) is to learn low-dimensional representations of entities and relations for predicting missing facts.
Axis Tour: Word Tour Determines the Order of Axes in ICA-transformed Embeddings
Inspired by Word Tour, a one-dimensional word embedding method, we aim to improve the clarity of the word embedding space by maximizing the semantic continuity of the axes.
Knowledge Sanitization of Large Language Models
We explore a knowledge sanitization approach to mitigate the privacy concerns associated with large language models (LLMs).
Discovering Universal Geometry in Embeddings with ICA
This study utilizes Independent Component Analysis (ICA) to unveil a consistent semantic structure within embeddings of words or images.
3D Rotation and Translation for Hyperbolic Knowledge Graph Embedding
The main objective of Knowledge Graph (KG) embeddings is to learn low-dimensional representations of entities and relations, enabling the prediction of missing facts.
Norm of Word Embedding Encodes Information Gain
Distributed representations of words encode lexical semantic information, but what type of information is encoded and how?
Improving word mover's distance by leveraging self-attention matrix
The proposed method is based on the Fused Gromov-Wasserstein distance, which simultaneously considers the similarity of the word embedding and the SAM for calculating the optimal transport between two sentences.
Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction
For correcting the asymptotic bias with fewer observations, this paper proposes a \emph{local radial regression (LRR)} and its logistic regression variant called \emph{local radial logistic regression~(LRLR)}, by combining the advantages of LPoR and MS-$k$-NN.
Revisiting Additive Compositionality: AND, OR and NOT Operations with Word Embeddings
(Q2) Ordinary additive compositionality can be seen as an AND operation of word meanings, but it is not well understood how other operations, such as OR and NOT, can be computed by the embeddings.
Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate
The weights and the parameter $k \in \mathbb{N}$ regulate its bias-variance trade-off, and the trade-off implicitly affects the convergence rate of the excess risk for the $k$-NN classifier; several existing studies considered selecting optimal $k$ and weights to obtain faster convergence rate.
Stochastic Neighbor Embedding of Multimodal Relational Data for Image-Text Simultaneous Visualization
Multimodal relational data analysis has become of increasing importance in recent years, for exploring across different domains of data, such as images and their text tags obtained from social networking services (e. g., Flickr).
Extrapolation Towards Imaginary $0$-Nearest Neighbour and Its Improved Convergence Rate
The weights and the parameter $k \in \mathbb{N}$ regulate its bias-variance trade-off, and the trade-off implicitly affects the convergence rate of the excess risk for the $k$-NN classifier; several existing studies considered selecting optimal $k$ and weights to obtain faster convergence rate.
More Powerful Selective Kernel Tests for Feature Selection
An approach for addressing this is via conditioning on the selection procedure to account for how we have used the data to generate our hypotheses, and prevent information to be used again after selection.
Hyperlink Regression via Bregman Divergence
A collection of $U \: (\in \mathbb{N})$ data vectors is called a $U$-tuple, and the association strength among the vectors of a tuple is termed as the \emph{hyperlink weight}, that is assumed to be symmetric with respect to permutation of the entries in the index.
Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities
In addition to the parameters of neural networks, we optimize the weights of the inner product by allowing positive and negative values.
Robust Graph Embedding with Noisy Link Weights
We propose $\beta$-graph embedding for robustly learning feature vectors from data vectors and noisy link weights.
An information criterion for auxiliary variable selection in incomplete data analysis
Utilizing a parametric model of joint distribution of primary and auxiliary variables, it is possible to improve the estimation of parametric model for the primary variables when the auxiliary variables are closely related to the primary variables.
Word-like character n-gram embedding
We propose a new word embedding method called \textit{word-like character} n\textit{-gram embedding}, which learns distributed representations of words by embedding word-like character n-grams.
Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability
We propose shifted inner-product similarity (SIPS), which is a novel yet very simple extension of the ordinary inner-product similarity (IPS) for neural-network based graph embedding (GE).
Segmentation-free Compositional $n$-gram Embedding
We propose a new type of representation learning method that models words, phrases and sentences seamlessly.
On representation power of neural network-based graph embedding and beyond
We consider the representation power of siamese-style similarity functions used in neural network-based graph embedding.
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
PMvGE is a probabilistic model for predicting new associations via graph embedding of the nodes of data vectors with links of their associations.
Spectral Graph-Based Method of Multimodal Word Embedding
In this paper, we propose a novel method for multimodal word embedding, which exploit a generalized framework of multi-view spectral graph embedding to take into account visual appearances or scenes denoted by words in a corpus.
PAFit: an R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks
This paper introduces the R package PAFit, which implements non-parametric procedures for estimating the preferential attachment function and node fitnesses in a growing network, as well as a number of functions for generating complex networks from these two mechanisms.
Data Analysis, Statistics and Probability Social and Information Networks Physics and Society Computation
Cross-validation of matching correlation analysis by resampling matching weights
For dimensionality reduction, we consider a linear transformation of data vectors, and define a matching error as the weighted sum of squared distances between transformed vectors with respect to the matching weights.
A simple coding for cross-domain matching with dimension reduction via spectral graph embedding
These data vectors from multiple domains are projected to a common space by linear transformations in order to search closely related vectors across domains.
