Search Results for author:
Found 12 papers, 5 papers with code
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction
Information extraction, e. g., attribute value extraction, has been extensively studied and formulated based only on text.
Federated Pruning: Improving Neural Network Efficiency with Federated Learning
Automatic Speech Recognition models require large amount of speech data for training, and the collection of such data often leads to privacy concerns.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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PAM: Understanding Product Images in Cross Product Category Attribute Extraction
Understanding product attributes plays an important role in improving online shopping experience for customers and serves as an integral part for constructing a product knowledge graph.
Learning with Hyperspherical Uniformity
Due to the over-parameterization nature, neural networks are a powerful tool for nonlinear function approximation.
Generative Fairness Teaching
We propose a generative fairness teaching framework that provides a model with not only real samples but also synthesized samples to compensate the data biases during training.
Orthogonal Over-Parameterized Training
The inductive bias of a neural network is largely determined by the architecture and the training algorithm.
Regularizing Neural Networks via Minimizing Hyperspherical Energy
Inspired by the Thomson problem in physics where the distribution of multiple propelling electrons on a unit sphere can be modeled via minimizing some potential energy, hyperspherical energy minimization has demonstrated its potential in regularizing neural networks and improving their generalization power.
Learning towards Minimum Hyperspherical Energy
In light of this intuition, we reduce the redundancy regularization problem to generic energy minimization, and propose a minimum hyperspherical energy (MHE) objective as generic regularization for neural networks.
Deformable Part Networks
In this paper we propose novel Deformable Part Networks (DPNs) to learn {\em pose-invariant} representations for 2D object recognition.
Decoupled Networks
Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations.
Robust Elastic Net Regression
We propose a robust elastic net (REN) model for high-dimensional sparse regression and give its performance guarantees (both the statistical error bound and the optimization bound).
Jointly Learning Non-negative Projection and Dictionary with Discriminative Graph Constraints for Classification
Sparse coding with dictionary learning (DL) has shown excellent classification performance.
