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Found 10 papers, 2 papers with code
Pruning Adatperfusion with Lottery Ticket Hypothesis
Pre-trained language models have shown great success in multiple downstream tasks.
Sequential Convolution and Runge-Kutta Residual Architecture for Image Compressed Sensing
In recent years, Deep Neural Networks (DNN) have empowered Compressed Sensing (CS) substantially and have achieved high reconstruction quality and speed far exceeding traditional CS methods.
Ranked #1 on
Image Compression
on BSDS500
Glass Segmentation with Multi Scales and Primary Prediction Guiding
Glass-like objects can be seen everywhere in our daily life which are very hard for existing methods to segment them.
TARGET: Template-Transferable Backdoor Attack Against Prompt-based NLP Models via GPT4
Most of the existing attack methods focus on inserting manually predefined templates as triggers in the pre-training phase to train the victim model and utilize the same triggers in the downstream task to perform inference, which tends to ignore the transferability and stealthiness of the templates.
SdCT-GAN: Reconstructing CT from Biplanar X-Rays with Self-driven Generative Adversarial Networks
Computed Tomography (CT) is a medical imaging modality that can generate more informative 3D images than 2D X-rays.
COVER: A Heuristic Greedy Adversarial Attack on Prompt-based Learning in Language Models
Prompt-based learning has been proved to be an effective way in pre-trained language models (PLMs), especially in low-resource scenarios like few-shot settings.
Progressive Multi-stage Interactive Training in Mobile Network for Fine-grained Recognition
Finally, using the progressive training (P), the features extracted by the model in different stages can be fully utilized and fused with each other.
BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification
Graph-based Aspect-based Sentiment Classification (ABSC) approaches have yielded state-of-the-art results, expecially when equipped with contextual word embedding from pre-training language models (PLMs).
Dropout with Tabu Strategy for Regularizing Deep Neural Networks
Tabu Dropout has no extra parameters compared with the standard Dropout and also it is computationally cheap.
Normative Multiagent Systems: A Dynamic Generalization
Social norms are powerful formalism in coordinating autonomous agents' behaviour to achieve certain objectives.
