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Found 56 papers, 21 papers with code
A Transformational Biencoder with In-Domain Negative Sampling for Zero-Shot Entity Linking
As like previous work, we rely on negative entities to encourage our model to discriminate the golden entities during training.
Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval
Recent work has shown that dense passage retrieval techniques achieve better ranking accuracy in open-domain question answering compared to sparse retrieval techniques such as BM25, but at the cost of large space and memory requirements.
Advancing Geometric Problem Solving: A Comprehensive Benchmark for Multimodal Model Evaluation
In this work, we present the MM-MATH dataset, a novel benchmark developed to rigorously evaluate the performance of advanced large language and multimodal models - including but not limited to GPT-4, GPT-4V, and Claude - within the domain of geometric computation.
A Semi-Analytical Approach for State-Space Electromagnetic Transient Simulation Using the Differential Transformation
Case studies are conducted on EMT models of the IEEE 39-bus system and a synthetic 390-bus system to demonstrate the merits of the new simulation approach against traditional methods.
AnimatableDreamer: Text-Guided Non-rigid 3D Model Generation and Reconstruction with Canonical Score Distillation
Extensive experiments demonstrate the capability of our method in generating high-flexibility text-guided 3D models from the monocular video, while also showing improved reconstruction performance over existing non-rigid reconstruction methods.
Anaphor Assisted Document-Level Relation Extraction
Existing methods focus on building a heterogeneous document graph to model the internal structure of an entity and the external interaction between entities.
Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?
Since the recent prosperity of Large Language Models (LLMs), there have been interleaved discussions regarding how to reduce hallucinations from LLM responses, how to increase the factuality of LLMs, and whether Knowledge Graphs (KGs), which store the world knowledge in a symbolic form, will be replaced with LLMs.
The Potential and Pitfalls of using a Large Language Model such as ChatGPT or GPT-4 as a Clinical Assistant
Recent studies have demonstrated promising performance of ChatGPT and GPT-4 on several medical domain tasks.
Trading Syntax Trees for Wordpieces: Target-oriented Opinion Words Extraction with Wordpieces and Aspect Enhancement
State-of-the-art target-oriented opinion word extraction (TOWE) models typically use BERT-based text encoders that operate on the word level, along with graph convolutional networks (GCNs) that incorporate syntactic information extracted from syntax trees.
Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction
Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance.
Semi-Analytical Electromagnetic Transient Simulation Using Differential Transformation
For electromagnetic transient (EMT) simulation of a power system, a state-space-based approach needs to solve state-space EMT equations by using numerical integration methods, e. g., the Euler method, Runge-Kutta methods, and trapezoidal-rule method, at small time steps.
Digital Asset Valuation: A Study on Domain Names, Email Addresses, and NFTs
We further propose improvements to make the model more aware of the time sensitivity of transactions and the popularity of assets.
Preparing data for pathological artificial intelligence with clinical-grade performance
The digital pathology is fundamental of clinical-grade PAI, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction.
Machine Learning based Optimal Feedback Control for Microgrid Stabilization
A case study is carried out for a microgrid model based on a modified Kundur two-area system to test the real-time performance of the proposed control scheme.
Data-Driven Computational Methods for the Domain of Attraction and Zubov's Equation
We prove that a neural network approximation exists for the Lyapunov function of power systems such that the approximation error is a cubic polynomial of the number of generators.
PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving
The accelerating development of autonomous driving technology has placed greater demands on obtaining large amounts of high-quality data.
An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction
Target-oriented opinion words extraction (TOWE) (Fan et al., 2019b) is a new subtask of target-oriented sentiment analysis that aims to extract opinion words for a given aspect in text.
XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data
To the best of our knowledge, XOmiVAE is one of the first activation level-based interpretable deep learning models explaining novel clusters generated by VAE.
Multi-scale super-resolution generation of low-resolution scanned pathological images
We design a strategy to scan slides with low resolution (5X) and a super-resolution method is proposed to restore the image details when in diagnosis.
A Replication Study of Dense Passage Retriever
Text retrieval using learned dense representations has recently emerged as a promising alternative to "traditional" text retrieval using sparse bag-of-words representations.
Deep Convolutional Sparse Coding Network for Pansharpening with Guidance of Side Information
Pansharpening is a fundamental issue in remote sensing field.
Deep Gradient Projection Networks for Pan-sharpening
Specifically, two optimization problems regularized by the deep prior are formulated, and they are separately responsible for the generative models for panchromatic images and low resolution multispectral images.
OmiEmbed: a unified multi-task deep learning framework for multi-omics data
To tackle this problem and pave the way for machine learning aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules.
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data
In spite of much recent research in the area, it is still unclear whether subject-area question-answering data is useful for machine reading comprehension (MRC) tasks.
A Blockchain-based Trust System for Decentralised Applications: When trustless needs trust
Blockchain technology has been envisaged to commence an era of decentralised applications and services (DApps) without the need for a trusted intermediary.
Cryptography and Security Distributed, Parallel, and Cluster Computing
FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter
In addition, the latent information of features can be preserved effectively through adversarial training.
Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy
According to the idea that each local region in the fused image should be similar to the sharpest one among source images, this paper presents an optimization-based approach to reduce defocus spread effects.
Aligning geographic entities from historical maps for building knowledge graphs
Despite their valuable content, it is often challenging to access and use the information in historical maps, due to their forms of paper-based maps or scanned images.
Privacy Preservation in Federated Learning: An insightful survey from the GDPR Perspective
Furthermore, in the era of the Internet of Things and big data in which data is essentially distributed, transferring a vast amount of data to a data centre for processing seems to be a cumbersome solution.
Adding Chit-Chat to Enhance Task-Oriented Dialogues
Existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e. g., booking hotels), open-domain chatbots aim at making socially engaging conversations.
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge
In this paper, we aim to extract commonsense knowledge to improve machine reading comprehension.
When Image Decomposition Meets Deep Learning: A Novel Infrared and Visible Image Fusion Method
The core idea is that the encoder decomposes an image into base and detail feature maps with low- and high-frequency information, respectively, and that the decoder is responsible for the original image reconstruction.
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations
The idea of using multi-task learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the relation classification task.
Ranked #8 on
Relation Extraction
on WebNLG
Recurrent Interaction Network for Jointly Extracting Entities andClassifying Relations
In this study, we argue that an explicit interaction between the NER model and the RE model will better guide the training of both models.
Dialogue-Based Relation Extraction
We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue.
Ranked #6 on
Dialog Relation Extraction
on DialogRE
(F1c (v1) metric)
CLUE: A Chinese Language Understanding Evaluation Benchmark
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships
Monocular 3D object detection is an essential component in autonomous driving while challenging to solve, especially for those occluded samples which are only partially visible.
Ranked #11 on
Vehicle Pose Estimation
on KITTI Cars Hard
Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records
The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis.
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree
We propose a method based on neural networks to identify the sentiment polarity of opinion words expressed on a specific aspect of a sentence.
Improving Pre-Trained Multilingual Model with Vocabulary Expansion
However, in multilingual setting, it is extremely resource-consuming to pre-train a deep language model over large-scale corpora for each language.
Improving Pre-Trained Multilingual Models with Vocabulary Expansion
However, in multilingual setting, it is extremely resource-consuming to pre-train a deep language model over large-scale corpora for each language.
Sem-LSD: A Learning-based Semantic Line Segment Detector
Combined with high-level semantics, Sem-LS is more robust under cluttered environment compared with existing line-shaped representations.
Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification
The training procedure of OmiVAE is comprised of an unsupervised phase without the classifier and a supervised phase with the classifier.
AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data
Anomaly detection is facing with emerging challenges in many important industry domains, such as cyber security and online recommendation and advertising.
Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension
Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document.
DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension
We present DREAM, the first dialogue-based multiple-choice reading comprehension data set.
Evidence Sentence Extraction for Machine Reading Comprehension
Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks.
Improving Question Answering with External Knowledge
We focus on multiple-choice question answering (QA) tasks in subject areas such as science, where we require both broad background knowledge and the facts from the given subject-area reference corpus.
DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension
DREAM is likely to present significant challenges for existing reading comprehension systems: 84% of answers are non-extractive, 85% of questions require reasoning beyond a single sentence, and 34% of questions also involve commonsense knowledge.
Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification
This paper presents a study on power grid disturbance classification by Deep Learning (DL).
Improving Machine Reading Comprehension with General Reading Strategies
Reading strategies have been shown to improve comprehension levels, especially for readers lacking adequate prior knowledge.
Ranked #7 on
Question Answering
on StoryCloze
Visualizing and Understanding Deep Neural Networks in CTR Prediction
Recently, many works have been done on visualizing and analyzing the mechanism of deep neural networks in the areas of image processing and natural language processing.
A vision based system for underwater docking
In the experiments, we first evaluate performance of the proposed detection module on UDID and its deformed variations.
Recurrent Polynomial Network for Dialogue State Tracking
Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states as a dialogue progresses.
