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Found 40 papers, 10 papers with code
Disjunctive Logic Programs versus Normal Logic Programs
This paper focuses on the expressive power of disjunctive and normal logic programs under the stable model semantics over finite, infinite, or arbitrary structures.
Existential Rule Languages with Finite Chase: Complexity and Expressiveness
Finite chase, or alternatively chase termination, is an important condition to ensure the decidability of existential rule languages.
Expressiveness of Logic Programs under General Stable Model Semantics
The stable model semantics had been recently generalized to non-Herbrand structures by several works, which provides a unified framework and solid logical foundations for answer set programming.
Query Answering with Inconsistent Existential Rules under Stable Model Semantics
Surprisingly, for R-acyclic existential rules with R-stratified or guarded existential rules with stratified negations, both the data complexity and combined complexity of query answering under the rule {repair semantics} remain the same as that under the conventional query answering semantics.
Expressive Completeness of Existential Rule Languages for Ontology-based Query Answering
In this paper, we prove that disjunctive embedded dependencies exactly capture the class of recursively enumerable ontologies in Ontology-based Conjunctive Query Answering (OCQA).
Hierarchical Multimodal Metric Learning for Multimodal Classification
The learned metrics can improve multimodal classification accuracy and experimental results on four datasets show that the proposed algorithm outperforms existing learning algorithms based on multiple metrics as well as other approaches tested on these datasets.
Loop Restricted Existential Rules and First-order Rewritability for Query Answering
In particular, we prove that this language satisfies the so-called bounded derivation-depth prop- erty (BDDP), which implies that the CQA is first-order rewritable, and its data complexity is in AC0 .
A Data-Center FPGA Acceleration Platform for Convolutional Neural Networks
Intensive computation is entering data centers with multiple workloads of deep learning.
Towards Universal Languages for Tractable Ontology Mediated Query Answering
On the negative side, we prove that there is, in general, no universal language for each of these families of languages.
Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks
Multispectral images (e. g. visible and infrared) may be particularly useful when detecting objects with the same model in different environments (e. g. day/night outdoor scenes).
Localize to Classify and Classify to Localize: Mutual Guidance in Object Detection
Most deep learning object detectors are based on the anchor mechanism and resort to the Intersection over Union (IoU) between predefined anchor boxes and ground truth boxes to evaluate the matching quality between anchors and objects.
Ranked #9 on
Object Detection
on PASCAL VOC 2007
Guided Attentive Feature Fusion for Multispectral Pedestrian Detection
Multispectral image pairs can provide complementary visual information, making pedestrian detection systems more robust and reliable.
Chronological Citation Recommendation with Time Preference
Citation recommendation is an important task to assist scholars in finding candidate literature to cite.
Using Full-text Content of Academic Articles to Build a Methodology Taxonomy of Information Science in China
Accordingly, the research methodology entities were clustered and the basic methodology taxonomy was expanded using the clustering results to obtain a methodology taxonomy with more levels.
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model.
Ranked #1 on
Image Retrieval
on RUC-CAS-WenLan
DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension
Multiparty Dialogue Machine Reading Comprehension (MRC) differs from traditional MRC as models must handle the complex dialogue discourse structure, previously unconsidered in traditional MRC.
Ranked #4 on
Question Answering
on Molweni
Interest-based Item Representation Framework for Recommendation with Multi-Interests Capsule Network
To make the framework model-agnostic, user Multi Interests Capsule Network is designed as an auxiliary task to jointly learn item-based item representations and interest-based item representations.
Characterizing the Program Expressive Power of Existential Rule Languages
Existential rule languages are a family of ontology languages that have been widely used in ontology-mediated query answering (OMQA).
Joint Optimization of DNN Inference Delay and Energy under Accuracy Constraints for AR Applications
The high computational complexity and high energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems.
Prediction-based Hybrid Slicing Framework for Service Level Agreement Guarantee in Mobility Scenarios: A Deep Learning Approach
In the proposal, LSTM networks are employed to predict traffic demand and the location of each user in a slicing window level.
Adaptive incomplete multi-view learning via tensor graph completion
With the advancement of the data acquisition techniques, multi-view learning has become a hot topic.
Exploring Temporal Concurrency for Video-Language Representation Learning
Paired video and language data is naturally temporal concurrency, which requires the modeling of the temporal dynamics within each modality and the temporal alignment across modalities simultaneously.
Dancing in the Dark: A Benchmark towards General Low-light Video Enhancement
To address this issue, we design a camera system and collect a high-quality low-light video dataset with multiple exposures and cameras.
Modeling Video As Stochastic Processes for Fine-Grained Video Representation Learning
Specifically, we enforce the embeddings of the frame sequence of interest to approximate a goal-oriented stochastic process, i. e., Brownian bridge, in the latent space via a process-based contrastive loss.
Dynamical Equations With Bottom-up Self-Organizing Properties Learn Accurate Dynamical Hierarchies Without Any Loss Function
The hurdle is that general patterns are difficult to define in terms of dynamical equations and designing a system that could learn by reordering itself is still to be seen.
Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention
We evaluate our model on various dialogue understanding tasks including dialogue relation extraction, dialogue emotion recognition, and dialogue act classification.
Ranked #1 on
Dialog Relation Extraction
on DialogRE
One for All: Unified Workload Prediction for Dynamic Multi-tenant Edge Cloud Platforms
In this paper, we propose an end-to-end framework with global pooling and static content awareness, DynEformer, to provide a unified workload prediction scheme for dynamic MT-ECP.
A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning
Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected.
Decompose Semantic Shifts for Composed Image Retrieval
Composed image retrieval is a type of image retrieval task where the user provides a reference image as a starting point and specifies a text on how to shift from the starting point to the desired target image.
Symmetrical SyncMap for Imbalanced General Chunking Problems
The main idea is to apply equal updates from negative and positive feedback loops by symmetrical activation.
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning
It assigns a shared homogeneous small feature extractor and a local gating network for each client's local heterogeneous large model.
Improvement of Frequency Source Phase Noise Reduction Design under Vibration Condition
Reasonable vibration reduction design is an important way to achieve low phase noise index of airborne frequency source output signal.
Augmented Labeled Random Finite Sets and Its Application to Group Target Tracking
Furthermore, by means of the labeled multi-Bernoulli (LMB) filter with the proposed augmented LRFSs, the group structure is iteratively propagated and updated during the tracking process, which achieves the simultaneously estimation of the kinetic states, track label, and the corresponding group information of multiple group targets, and further improves the GTT tracking performance.
Adapting to Covariate Shift in Real-time by Encoding Trees with Motion Equations
In this paper, we demonstrated how a neural network integrated with Xenovert achieved better results in 4 out of 5 shifted datasets, saving the hurdle of retraining a machine learning model.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
IR&TM-NJUST@CLSciSumm 20
Unlike the methods used in CL-SciSumm 2019, we construct inputs of models based on word vectors and add deep learning models for classification this year.
Less Is More: Domain Adaptation with Lottery Ticket for Reading Comprehension
Then, we only fine-tune the lottery subnetwork, a small fraction of the whole parameters, on the annotated target domain data for adaptation.
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning
Document-level Event Factuality Identification (DEFI) predicts the factuality of a specific event based on a document from which the event can be derived, which is a fundamental and crucial task in Natural Language Processing (NLP).
