Search Results for author: Bing Xu

Found 19 papers, 10 papers with code

CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue

no code implementations CCL 2020 Ting Jiang, Bing Xu, Tiejun Zhao, Sheng Li

In the first layer, in order to extract textual features of utterances, we propose a convolutional self-attention network(CAN).

Emotion Recognition Natural Language Processing +1

CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection

1 code implementation12 Apr 2022 Zhen Li, Bing Xu, Conghui Zhu, Tiejun Zhao

Compared with unimodal data, multimodal data can provide more features to help the model analyze the sentiment of data.

Contrastive Learning Sentiment Analysis

Multi-Grained Knowledge Distillation for Named Entity Recognition

1 code implementation NAACL 2021 Xuan Zhou, Xiao Zhang, Chenyang Tao, Junya Chen, Bing Xu, Wei Wang, Jing Xiao

To maximally assimilate knowledge into the student model, we propose a multi-grained distillation scheme, which integrates cross entropy involved in conditional random field (CRF) and fuzzy learning. To validate the effectiveness of our proposal, we conducted a comprehensive evaluation on five NER benchmarks, reporting cross-the-board performance gains relative to competing prior-arts.

Knowledge Distillation named-entity-recognition +2

Biometric Blockchain: A Better Solution for the Security and Trust of Food Logistics

no code implementations21 Jul 2019 Bing Xu, Tobechukwu Agbele, Richard Jiang

The advantage of using BBC in the food logistics is clear: it can not only identify if the data or labels are authentic, but also clearly record who is responsible for the secured data or labels.

Training Deep Nets with Sublinear Memory Cost

6 code implementations21 Apr 2016 Tianqi Chen, Bing Xu, Chiyuan Zhang, Carlos Guestrin

In the extreme case, our analysis also shows that the memory consumption can be reduced to O(log n) with as little as O(n log n) extra cost for forward computation.

Revise Saturated Activation Functions

no code implementations18 Feb 2016 Bing Xu, Ruitong Huang, Mu Li

In this paper, we revise two commonly used saturated functions, the logistic sigmoid and the hyperbolic tangent (tanh).

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

2 code implementations3 Dec 2015 Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, Zheng Zhang

This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion.

Dimensionality Reduction General Classification

Learning with a Strong Adversary

1 code implementation10 Nov 2015 Ruitong Huang, Bing Xu, Dale Schuurmans, Csaba Szepesvari

The robustness of neural networks to intended perturbations has recently attracted significant attention.

General Classification

Empirical Evaluation of Rectified Activations in Convolutional Network

2 code implementations5 May 2015 Bing Xu, Naiyan Wang, Tianqi Chen, Mu Li

In this paper we investigate the performance of different types of rectified activation functions in convolutional neural network: standard rectified linear unit (ReLU), leaky rectified linear unit (Leaky ReLU), parametric rectified linear unit (PReLU) and a new randomized leaky rectified linear units (RReLU).

General Classification Image Classification

Generative Adversarial Nets

1 code implementation NeurIPS 2014 Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio

We propose a new framework for estimating generative models via adversarial nets, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.

Generative Adversarial Networks

178 code implementations Proceedings of the 27th International Conference on Neural Information Processing Systems 2014 Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.

Super-Resolution Time-Series Few-Shot Learning with Heterogeneous Channels

Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches

no code implementations29 Nov 2013 Xudong Liu, Bing Xu, Yuyu Zhang, Qiang Yan, Liang Pang, Qiang Li, Hanxiao Sun, Bin Wang

The ICDM Challenge 2013 is to apply machine learning to the problem of hotel ranking, aiming to maximize purchases according to given hotel characteristics, location attractiveness of hotels, user's aggregated purchase history and competitive online travel agency information for each potential hotel choice.

Feature Engineering

Horizontal and Vertical Ensemble with Deep Representation for Classification

no code implementations12 Jun 2013 Jingjing Xie, Bing Xu, Zhang Chuang

Representation learning, especially which by using deep learning, has been widely applied in classification.

Classification General Classification +1

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