Search Results for author:
Found 27 papers, 10 papers with code
BSDP: Brain-inspired Streaming Dual-level Perturbations for Online Open World Object Detection
Humans can easily distinguish the known and unknown categories and can recognize the unknown object by learning it once instead of repeating it many times without forgetting the learned object.
Retrieval Augmented Cross-Modal Tag Recommendation in Software Q&A Sites
For the retrieval-augmented representations, we employ a cross-modal context-aware attention to leverage the main modality description for targeted feature extraction across the submodalities title and code.
Parallax-Tolerant Image Stitching with Epipolar Displacement Field
Large parallax image stitching is a challenging task.
Windformer:Bi-Directional Long-Distance Spatio-Temporal Network For Wind Speed Prediction
History and future time information includes the trend of airflow changes, whether this dynamic information can be utilized will also affect the prediction effect.
A meta learning scheme for fast accent domain expansion in Mandarin speech recognition
We combine the methods of meta learning and freeze of model parameters, which makes the recognition performance more stable in different cases and the training faster about 20%.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
SST-ReversibleNet: Reversible-prior-based Spectral-Spatial Transformer for Efficient Hyperspectral Image Reconstruction
The reconstruction subnet in the network then learns the mapping of the residuals to the true values to improve reconstruction accuracy.
Deep Generative Mixture Model for Robust Imbalance Classification
In this paper, a deep generative classifier is proposed to mitigate this issue via both model perturbation and data perturbation.
Knowledge Graph Completion based on Tensor Decomposition for Disease Gene Prediction
In this paper, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end Knowledge graph completion model for Disease Gene Prediction using interactional tensor decomposition (called KDGene).
A Pre-training Framework for Knowledge Graph Completion
Knowledge graph completion (KGC) is one of the effective methods to identify new facts in knowledge graph.
IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels
Secondly, the subdomain classifier module based on instance confidence is constructed, which can dynamically divide the target domain into easy and difficult subdomains according to the relative proportion of easy and difficult instances.
Nextformer: A ConvNeXt Augmented Conformer For End-To-End Speech Recognition
To the best of our knowledge, the proposed Nextformer model achieves SOTA results on AISHELL-1(CER 4. 06%) and WenetSpeech(CER 7. 56%/11. 29%).
Deep Embedded Clustering with Distribution Consistency Preservation for Attributed Networks
Under the assumption of consistency for data in different views, the cluster structure of network topology and that of node attributes should be consistent for an attributed network.
Non-generative Generalized Zero-shot Learning via Task-correlated Disentanglement and Controllable Samples Synthesis
Synthesizing pseudo samples is currently the most effective way to solve the Generalized Zero-Shot Learning (GZSL) problem.
Learning to Learn a Cold-start Sequential Recommender
The cold-start recommendation is an urgent problem in contemporary online applications.
Knowledge Graph-enhanced Sampling for Conversational Recommender System
Then, two samplers are designed to enhance knowledge by sampling fuzzy samples with high uncertainty for obtaining user preferences and reliable negative samples for updating recommender to achieve efficient acquisition of user preferences and model updating, and thus provide a powerful solution for CRS to deal with E&E problem.
Homogeneous and Heterogeneous Relational Graph for Visible-infrared Person Re-identification
Existing VI Re-ID methods mainly focus on extracting homogeneous structural relationships in an image, i. e. the relations between local features, while ignoring the heterogeneous correlation of local features in different modalities.
ImageNet Pre-training also Transfers Non-Robustness
ImageNet pre-training has enabled state-of-the-art results on many tasks.
Comparative Analysis of Machine Learning Approaches to Analyze and Predict the Covid-19 Outbreak
Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic.
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation
Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios.
Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation
In this paper, we propose a new framework to model cross-sentence dependencies by training neural machine translation (NMT) to predict both the target translation and surrounding sentences of a source sentence.
A Generalization Theory based on Independent and Task-Identically Distributed Assumption
The derived generalization bound based on the ITID assumption identifies the significance of hypothesis invariance in guaranteeing generalization performance.
blessing in disguise: Designing Robust Turing Test by Employing Algorithm Unrobustness
Turing test was originally proposed to examine whether machine's behavior is indistinguishable from a human.
Attention, Please! Adversarial Defense via Activation Rectification and Preservation
This study provides a new understanding of the adversarial attack problem by examining the correlation between adversarial attack and visual attention change.
Communication: Words and Conceptual Systems
Word (phrase or symbol) representation is the fundamental problem for knowledge representation and understanding.
Semi-Supervised Low-Rank Mapping Learning for Multi-Label Classification
SLRM model takes advantage of the nuclear norm regularization on mapping to effectively capture the label correlations.
Generalized Categorization Axioms
Categorization axioms have been proposed to axiomatizing clustering results, which offers a hint of bridging the difference between human recognition system and machine learning through an intuitive observation: an object should be assigned to its most similar category.
Categorization Axioms for Clustering Results
Cluster analysis has attracted more and more attention in the field of machine learning and data mining.
