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Found 18 papers, 5 papers with code
Hawkes based Representation Learning for Reasoning over Scale-free Community-structured Temporal Knowledge Graphs
In this paper, we propose a novel TKG reasoning model called Hawkes process-based Evolutional Representation Learning Network (HERLN), which learns structural information and evolutional patterns of a TKG simultaneously, considering the characteristics of real-world networks: community structure, scale-free and temporal decaying.
Multimodal Industrial Anomaly Detection by Crossmodal Reverse Distillation
Anomalies in one modality may not be effectively captured in the fused teacher features, leading to detection failures.
Unlocking the Potential of Reverse Distillation for Anomaly Detection
Knowledge Distillation (KD) is a promising approach for unsupervised Anomaly Detection (AD).
Ranked #4 on
Anomaly Detection
on BTAD
(using extra training data)
ONER: Online Experience Replay for Incremental Anomaly Detection
Specifically, our framework utilizes two types of experiences from past tasks: decomposed prompts and semantic prototypes, addressing both model parameter updates and feature optimization.
Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Tasks
Meta-learning has emerged as a prominent technology for few-shot text classification and has achieved promising performance.
Customizing Large Language Model Generation Style using Parameter-Efficient Finetuning
One-size-fits-all large language models (LLMs) are increasingly being used to help people with their writing.
GNSS Interference Classification Using Federated Reservoir Computing
The expanding use of Unmanned Aerial Vehicles (UAVs) in vital areas like traffic management, surveillance, and environmental monitoring highlights the need for robust communication and navigation systems.
Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection
In DMDD, a Decouple Student-Teacher Network is proposed to decouple the initial student features into normality and abnormality features.
Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality
To tackle the problems, we propose a novel multi-modal federated learning method, Federated Multi-modal contrastiVe training with Pre-trained completion (FedMVP), which integrates the large-scale pre-trained models to enhance the federated training.
QIENet: Quantitative irradiance estimation network using recurrent neural network based on satellite remote sensing data
Specifically, the temporal and spatial characteristics of remote sensing data of the satellite Himawari-8 are extracted and fused by recurrent neural network (RNN) and convolution operation, respectively.
Multi-State Brain Network Discovery
We refer to such brain networks as multi-state, and this mixture can help us understand human behavior.
Gaussian Mixture Graphical Lasso with Application to Edge Detection in Brain Networks
Existing works mainly focus on unimodal distributions, where it is usually assumed that the observed activities aregenerated from asingleGaussian distribution (i. e., one graph). However, this assumption is too strong for many real-worldapplications.
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention
The comparison shows that our model could recover the transcription by integrating the fragmented information among hypotheses and identifying the frequent error patterns of the ASR module, and even rewrite the query for a better understanding, which reveals the characteristic of multi-task learning of broadcasting knowledge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Enhance Robustness of Sequence Labelling with Masked Adversarial Training
Adversarial training (AT) has shown strong regularization effects on deep learning algorithms by introducing small input perturbations to improve model robustness.
Ranked #3 on
Chunking
on CoNLL 2000
(using extra training data)
SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling
Virtual adversarial training (VAT) is a powerful technique to improve model robustness in both supervised and semi-supervised settings.
Ranked #7 on
Chunking
on CoNLL 2000
Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses
The NLU module usually uses the first best interpretation of a given speech in downstream tasks such as domain and intent classification.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Signed Distance-based Deep Memory Recommender
Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them.
TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks
To address these issues, we study the problem of syntax-aware sequence generation with GANs, in which a collection of real sequences and a set of pre-defined grammatical rules are given to both discriminator and generator.
