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Found 13 papers, 6 papers with code
KAN versus MLP on Irregular or Noisy Functions
For some types of functions, MLP outperforms or performs comparably to KAN.
Reduced Effectiveness of Kolmogorov-Arnold Networks on Functions with Noise
noise with any fixed SNR, when we increase the amount of training data by a factor of $r$, the test-loss (RMSE) of KANs will exhibit a performance trend like $\text{test-loss} \sim \mathcal{O}(r^{-\frac{1}{2}})$ as $r\to +\infty$.
Invisible Watermarking for Audio Generation Diffusion Models
Diffusion models have gained prominence in the image domain for their capabilities in data generation and transformation, achieving state-of-the-art performance in various tasks in both image and audio domains.
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER
The objective of few-shot named entity recognition is to identify named entities with limited labeled instances.
Ranked #1 on
Few-shot NER
on Few-NERD (INTER)
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue
Pre-trained language models based on general text enable huge success in the NLP scenario.
Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery
Previous methods suffer from a coupling of pseudo label disambiguation and representation learning, that is, the reliability of pseudo labels relies on representation learning, and representation learning is restricted by pseudo labels in turn.
A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition
Specifically, we decouple class-specific prototypes and contextual semantic prototypes by two masking strategies to lead the model to focus on two different semantic information for inference.
Revisit Out-Of-Vocabulary Problem for Slot Filling: A Unified Contrastive Frameword with Multi-level Data Augmentations
In real dialogue scenarios, the existing slot filling model, which tends to memorize entity patterns, has a significantly reduced generalization facing Out-of-Vocabulary (OOV) problems.
Knowledge from Large-Scale Protein Contact Prediction Models Can Be Transferred to the Data-Scarce RNA Contact Prediction Task
Our findings indicate that the learned structural patterns of proteins can be transferred to RNAs, opening up potential new avenues for research.
PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling
Most existing slot filling models tend to memorize inherent patterns of entities and corresponding contexts from training data.
Learning Robust Policies for Generalized Debris Capture with an Automated Tether-Net System
This tether-net system is subject to several sources of uncertainty in sensing and actuation that affect the performance of its net launch and closing control.
deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search
With the rapid increase in the amount of public code repositories, developers maintain a great desire to retrieve precise code snippets by using natural language.
Deep Learning based Multi-Modal Sensing for Tracking and State Extraction of Small Quadcopters
The thermal data is processed through a thresholding and Kalman filter approach to detect and track the bounding box.
