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Found 16 papers, 2 papers with code
Automated Generation of Multiple-Choice Cloze Questions for Assessing English Vocabulary Using GPT-turbo 3.5
The VocaTT (vocabulary teaching and training) engine is written in Python and comprises three basic steps: pre-processing target word lists, generating sentences and candidate word options using GPT, and finally selecting suitable word options.
Assessing the Efficacy of Grammar Error Correction: A Human Evaluation Approach in the Japanese Context
In this study, we evaluated the performance of the state-of-the-art sequence tagging grammar error detection and correction model (SeqTagger) using Japanese university students' writing samples.
A Unified Framework for Fair Spectral Clustering With Effective Graph Learning
Traditional fair spectral clustering (FSC) methods consist of two consecutive stages, i. e., performing fair spectral embedding on a given graph and conducting $k$means to obtain discrete cluster labels.
Equal Incremental Cost-Based Optimization Method to Enhance Efficiency for IPOP-Type Converters
This paper addresses the issue of enhancing the efficiency of a multiple module system connected in parallel during operation and proposes an algorithm based on equal incremental cost for dynamic load allocation.
Graph Learning Across Data Silos
We consider the problem of inferring graph topology from smooth graph signals in a novel but practical scenario where data are located in distributed clients and prohibited from leaving local clients due to factors such as privacy concerns.
Yuille-Poggio's Flow and Global Minimizer of polynomials through convexification by Heat Evolution
On the other hand, we propose the "seesaw" polynomials $p(x|s)$ and we find a seesaw differential equation $\frac{\partial p(x|s)}{\, ds}=-\frac{1}{p''(x)}$ to characterize the evolution of global minimizer $x^*(s)$ of $p(x|s)$ while varying $s$.
Time-varying Graph Learning Under Structured Temporal Priors
Different from many existing chain structure based methods in which the priors like temporal homogeneity can only describe the variations of two consecutive graphs, we propose a structure named \emph{temporal graph} to characterize the underlying real temporal relations.
A Semantic Indexing Structure for Image Retrieval
In large-scale image retrieval, many indexing methods have been proposed to narrow down the searching scope of retrieval.
Robust Graph Learning Under Wasserstein Uncertainty
To this end, we propose a graph learning framework using Wasserstein distributionally robust optimization (WDRO) which handles uncertainty in data by defining an uncertainty set on distributions of the observed data.
Learning Geo-Contextual Embeddings for Commuting Flow Prediction
Then, an attention mechanism is proposed based on the framework of graph attention network (GAT) to capture the spatial correlations and encode geographic contextual information to embedding space.
Zero-Shot Feature Selection via Transferring Supervised Knowledge
Feature selection, an effective technique for dimensionality reduction, plays an important role in many machine learning systems.
Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping
We call this a temporal transformer network (TTN).
Unsupervised Classification of Street Architectures Based on InfoGAN
Furthermore, we draw a series of heuristic conclusions from the intrinsic information hidden in true images.
A Deep, Information-theoretic Framework for Robust Biometric Recognition
Deep neural networks (DNN) have been a de facto standard for nowadays biometric recognition solutions.
Equivalence between LINE and Matrix Factorization
LINE [1], as an efficient network embedding method, has shown its effectiveness in dealing with large-scale undirected, directed, and/or weighted networks.
Virtual Worlds as Proxy for Multi-Object Tracking Analysis
We provide quantitative experimental evidence suggesting that (i) modern deep learning algorithms pre-trained on real data behave similarly in real and virtual worlds, and (ii) pre-training on virtual data improves performance.
