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Found 11 papers, 4 papers with code
Wav2SQL: Direct Generalizable Speech-To-SQL Parsing
Speech-to-SQL (S2SQL) aims to convert spoken questions into SQL queries given relational databases, which has been traditionally implemented in a cascaded manner while facing the following challenges: 1) model training is faced with the major issue of data scarcity, where limited parallel data is available; and 2) the systems should be robust enough to handle diverse out-of-domain speech samples that differ from the source data.
SPSQL: Step-by-step Parsing Based Framework for Text-to-SQL Generation
Then, we construct data formats of different subtasks based on existing data and improve the accuracy of the overall model by improving the accuracy of each submodel.
HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification
A parallel hierarchy of local and global feature blocks is designed to efficiently extract local features and global representations at various semantic scales, with the flexibility to model at different scales and linear computational complexity relevant to image size.
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks
Due to the differences of clients, a single global model may not perform well on all clients, so the personalized federated learning method, which trains a personalized model for each client that better suits its individual needs, becomes a research hotspot.
Cross-Silo Heterogeneous Model Federated Multitask Learning
CoFED is a federated learning method that is compatible with heterogeneous models, tasks, and training processes.
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks
We also propose a parametric gather-excite operator pair which yields further performance gains, relate it to the recently-introduced Squeeze-and-Excitation Networks, and analyse the effects of these changes to the CNN feature activation statistics.
Squeeze-and-Excitation Networks
Squeeze-and-Excitation Networks formed the foundation of our ILSVRC 2017 classification submission which won first place and reduced the top-5 error to 2. 251%, surpassing the winning entry of 2016 by a relative improvement of ~25%.
Ranked #59 on
Image Classification
on CIFAR-10
A Key Volume Mining Deep Framework for Action Recognition
Training with a large proportion of irrelevant volumes will hurt performance.
Deep Image: Scaling up Image Recognition
We present a state-of-the-art image recognition system, Deep Image, developed using end-to-end deep learning.
Multi-level Discriminative Dictionary Learning towards Hierarchical Visual Categorization
For each internode of the hierarchical category structure, a discriminative dictionary and a set of classification models are learnt for visual categorization, and the dictionaries in different layers are learnt to exploit the discriminative visual properties of different granularity.
