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Found 41 papers, 26 papers with code
Learning Strong Graph Neural Networks with Weak Information
Accordingly, we propose D$^2$PT, a dual-channel GNN framework that performs long-range information propagation not only on the input graph with incomplete structure, but also on a global graph that encodes global semantic similarities.
BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks
In this paper, we introduce a unified and generalist Biomedical Generative Pre-trained Transformer (BiomedGPT) model, which leverages self-supervision on large and diverse datasets to accept multi-modal inputs and perform a range of downstream tasks.
On Learning to Summarize with Large Language Models as References
Therefore, we investigate a new learning paradigm of text summarization models that considers the LLMs as the reference or the gold-standard oracle on commonly used summarization datasets such as the CNN/DailyMail dataset.
QTSumm: A New Benchmark for Query-Focused Table Summarization
People primarily consult tables to conduct data analysis or answer specific questions.
A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT
The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace.
Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation
Interpretability and efficiency are two important considerations for the adoption of neural automatic metrics.
Securing Biomedical Images from Unauthorized Training with Anti-Learning Perturbation
The volume of open-source biomedical data has been essential to the development of various spheres of the healthcare community since more `free' data can provide individual researchers more chances to contribute.
Unlearnable Graph: Protecting Graphs from Unauthorized Exploitation
While the use of graph-structured data in various fields is becoming increasingly popular, it also raises concerns about the potential unauthorized exploitation of personal data for training commercial graph neural network (GNN) models, which can compromise privacy.
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.
On Improving Summarization Factual Consistency from Natural Language Feedback
In this work, we study whether informational feedback in natural language can be leveraged to improve generation quality and user preference alignment.
Needle in a Haystack: An Analysis of Finding Qualified Workers on MTurk for Summarization
The acquisition of high-quality human annotations through crowdsourcing platforms like Amazon Mechanical Turk (MTurk) is more challenging than expected.
Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation
4) We evaluate existing automatic metrics using the collected human annotations across evaluation protocols and demonstrate how our benchmark leads to more statistically stable and significant results.
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating
Node representations are learned through contrasting the dual-channel encodings obtained from the discriminated homophilic and heterophilic edges.
SEAT: Stable and Explainable Attention
Results show that SEAT is more stable against different perturbations and randomness while also keeps the explainability of attention, which indicates it is a more faithful explanation.
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
Inspired by this, we propose FedStar, an FGL framework that extracts and shares the common underlying structure information for inter-graph federated learning tasks.
GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection
As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods.
FOLIO: Natural Language Reasoning with First-Order Logic
We present FOLIO, a human-annotated, open-domain, and logically complex and diverse dataset for reasoning in natural language (NL), equipped with first order logic (FOL) annotations.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
Leveraging Locality in Abstractive Text Summarization
Our experimental results show that our model has a better performance compared with strong baselines with efficient attention modules, and our analysis provides further insights into our locality-aware modeling strategy.
R2D2: Robust Data-to-Text with Replacement Detection
Unfaithful text generation is a common problem for text generation systems.
BRIO: Bringing Order to Abstractive Summarization
Abstractive summarization models are commonly trained using maximum likelihood estimation, which assumes a deterministic (one-point) target distribution in which an ideal model will assign all the probability mass to the reference summary.
Ranked #2 on
Text Summarization
on X-Sum
DataLab: A Platform for Data Analysis and Intervention
Despite data's crucial role in machine learning, most existing tools and research tend to focus on systems on top of existing data rather than how to interpret and manipulate data.
Graph Neural Networks for Graphs with Heterophily: A Survey
Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriads of graph analytic tasks and applications.
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach
Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce.
Towards Unsupervised Deep Graph Structure Learning
To solve the unsupervised GSL problem, we propose a novel StrUcture Bootstrapping contrastive LearnIng fraMEwork (SUBLIME for abbreviation) with the aid of self-supervised contrastive learning.
CLICKER: A Computational LInguistics Classification Scheme for Educational Resources
We propose a classification scheme -- CLICKER for CL/NLP based on the analysis of online lectures from 77 university courses on this subject.
Surfer100: Generating Surveys From Web Resources, Wikipedia-style
Fast-developing fields such as Artificial Intelligence (AI) often outpace the efforts of encyclopedic sources such as Wikipedia, which either do not completely cover recently-introduced topics or lack such content entirely.
Distribution Locational Marginal Pricing Under Uncertainty Considering Coordination of Distribution and Wholesale Markets
An effective distribution electricity market (DEM) is required to manage the rapidly growing small-scale distributed energy resources (DERs) in distribution systems (DSs).
Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection
While the generative attribute regression module allows us to capture the anomalies in the attribute space, the multi-view contrastive learning module can exploit richer structure information from multiple subgraphs, thus abling to capture the anomalies in the structure space, mixing of structure, and attribute information.
Priority prediction of Asian Hornet sighting report using machine learning methods
In this paper, we propose a method to predict the priority of sighting reports based on machine learning.
Anomaly Detection in Dynamic Graphs via Transformer
Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity.
SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
In this paper, we present a conceptually simple while empirically powerful framework for abstractive summarization, SimCLS, which can bridge the gap between the learning objective and evaluation metrics resulting from the currently dominated sequence-to-sequence learning framework by formulating text generation as a reference-free evaluation problem (i. e., quality estimation) assisted by contrastive learning.
Ranked #4 on
Text Summarization
on X-Sum
RefSum: Refactoring Neural Summarization
Although some recent works show potential complementarity among different state-of-the-art systems, few works try to investigate this problem in text summarization.
ExplainaBoard: An Explainable Leaderboard for NLP
In this paper, we present a new conceptualization and implementation of NLP evaluation: the ExplainaBoard, which in addition to inheriting the functionality of the standard leaderboard, also allows researchers to (i) diagnose strengths and weaknesses of a single system (e. g.~what is the best-performing system bad at?)
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way.
Graph Self-Supervised Learning: A Survey
Deep learning on graphs has attracted significant interests recently.
Cyclic Label Propagation for Graph Semi-supervised Learning
To overcome these limitations, we introduce a novel framework for graph semi-supervised learning termed as Cyclic Label Propagation (CycProp for abbreviation), which integrates GNNs into the process of label propagation in a cyclic and mutually reinforcing manner to exploit the advantages of both GNNs and LPA.
On Learning Text Style Transfer with Direct Rewards
In most cases, the lack of parallel corpora makes it impossible to directly train supervised models for the text style transfer task.
Conditional Automated Channel Pruning for Deep Neural Networks
To address this issue, given a target compression rate for the whole model, one can search for the optimal compression rate for each layer.
Training and Inference Methods for High-Coverage Neural Machine Translation
In this paper, we introduce a system built for the Duolingo Simultaneous Translation And Paraphrase for Language Education (STAPLE) shared task at the 4th Workshop on Neural Generation and Translation (WNGT 2020).
Adversarial Attack and Defense on Graph Data: A Survey
Therefore, this review is intended to provide an overall landscape of more than 100 papers on adversarial attack and defense strategies for graph data, and establish a unified formulation encompassing most graph adversarial learning models.
