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Found 28 papers, 12 papers with code
ConsistencyDet: A Robust Object Detector with a Denoising Paradigm of Consistency Model
In the present study, we introduce a novel framework designed to articulate object detection as a denoising diffusion process, which operates on the perturbed bounding boxes of annotated entities.
Detection of ChatGPT Fake Science with the xFakeSci Learning Algorithm
In this study, we show how articles can be generated using means of prompt engineering for various diseases and conditions.
Give Us the Facts: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling
Recently, ChatGPT, a representative large language model (LLM), has gained considerable attention due to its powerful emergent abilities.
Fair Causal Feature Selection
Fair feature selection for classification decision tasks has recently garnered significant attention from researchers.
Unifying Large Language Models and Knowledge Graphs: A Roadmap
In this article, we present a forward-looking roadmap for the unification of LLMs and KGs.
A Comprehensive Survey on Automatic Knowledge Graph Construction
A knowledge graph is built in three steps: knowledge acquisition, knowledge refinement, and knowledge evolution.
Block-Structured Optimization for Subgraph Detection in Interdependent Networks
We propose a generalized framework for block-structured nonconvex optimization, which can be applied to structured subgraph detection in interdependent networks, such as multi-layer networks, temporal networks, networks of networks, and many others.
Deep Forest with Hashing Screening and Window Screening
To screen out redundant feature vectors, we introduce a hashing screening mechanism for multi-grained scanning and propose a model called HW-Forest which adopts two strategies, hashing screening and window screening.
Chinese Idiom Paraphrasing
Idioms, are a kind of idiomatic expression in Chinese, most of which consist of four Chinese characters.
Learning Relation-Specific Representations for Few-shot Knowledge Graph Completion
To this end, these methods learn entity-pair representations from the direct neighbors of head and tail entities, and then aggregate the representations of reference entity pairs.
Prompt-Learning for Short Text Classification
In the short text, the extremely short length, feature sparsity, and high ambiguity pose huge challenges to classification tasks.
OPP-Miner: Order-preserving sequential pattern mining
To discover patterns, existing methods often convert time series data into another form, such as nominal/symbolic format, to reduce dimensionality, which inevitably deviates the data values.
Towards Efficient Local Causal Structure Learning
Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data.
Trustworthy Preference Completion in Social Choice
As from time to time it is impractical to ask agents to provide linear orders over all alternatives, for these partial rankings it is necessary to conduct preference completion.
Chinese Lexical Simplification
Lexical simplification has attracted much attention in many languages, which is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning.
A Survey on Large-scale Machine Learning
Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining, visual classification, and recommender systems.
LSBert: A Simple Framework for Lexical Simplification
Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning, to simplify the sentence.
Causality-based Feature Selection: Methods and Evaluations
It has been shown that the knowledge about the causal relationships between features and the class variable has potential benefits for building interpretable and robust prediction models, since causal relationships imply the underlying mechanism of a system.
Towards Efficient Local Causal Structure Learning
To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS.
Lexical Simplification with Pretrained Encoders
Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning.
STTM: A Tool for Short Text Topic Modeling
Along with the emergence and popularity of social communications on the Internet, topic discovery from short texts becomes fundamental to many applications that require semantic understanding of textual content.
Information Retrieval
Object Detection with Deep Learning: A Review
Traditional object detection methods are built on handcrafted features and shallow trainable architectures.
AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding
Existing methods usually adopt a "one-size-fits-all" approach when concerning multi-scale structure information, such as first- and second-order proximity of nodes, ignoring the fact that different scales play different roles in the embedding learning.
Camera Fingerprint: A New Perspective for Identifying User's Identity
The underlying assumption is that multiple accounts belonging to the same person contain the same or similar camera fingerprint information.
Topic Modeling over Short Texts by Incorporating Word Embeddings
Inferring topics from the overwhelming amount of short texts becomes a critical but challenging task for many content analysis tasks, such as content charactering, user interest profiling, and emerging topic detecting.
Online Feature Selection with Group Structure Analysis
The problem assumes that features are generated individually but there are group structure in the feature stream.
LOFS: Library of Online Streaming Feature Selection
As an emerging research direction, online streaming feature selection deals with sequentially added dimensions in a feature space while the number of data instances is fixed.
Scalable and Accurate Online Feature Selection for Big Data
Feature selection is important in many big data applications.
