Search Results for author: Bing Bai

Found 20 papers, 6 papers with code

Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets

no code implementations12 Jul 2022 Yingsong Huang, Bing Bai, Shengwei Zhao, Kun Bai, Fei Wang

The second issue refers to that models may output misleading predictions due to epistemic uncertainty and aleatoric uncertainty, thus existing methods that rely solely on the output probabilities may fail to distinguish confident samples.

Contrastive Multi-view Hyperbolic Hierarchical Clustering

no code implementations5 May 2022 Fangfei Lin, Bing Bai, Kun Bai, Yazhou Ren, Peng Zhao, Zenglin Xu

Then, we embed the representations into a hyperbolic space and optimize the hyperbolic embeddings via a continuous relaxation of hierarchical clustering loss.

Capture Uncertainties in Deep Neural Networks for Safe Operation of Autonomous Driving Vehicles

no code implementations11 Aug 2021 Liuhui Ding, Dachuan Li, Bowen Liu, Wenxing Lan, Bing Bai, Qi Hao, Weipeng Cao, Ke Pei

Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles.

Autonomous Driving Motion Planning +2

Reliable Evaluations for Natural Language Inference based on a Unified Cross-dataset Benchmark

no code implementations15 Oct 2020 Guanhua Zhang, Bing Bai, Jian Liang, Kun Bai, Conghui Zhu, Tiejun Zhao

Recent studies show that crowd-sourced Natural Language Inference (NLI) datasets may suffer from significant biases like annotation artifacts.

Natural Language Inference

Domain Agnostic Learning for Unbiased Authentication

no code implementations11 Oct 2020 Jian Liang, Yuren Cao, Shuang Li, Bing Bai, Hao Li, Fei Wang, Kun Bai

We further extend our method to a meta-learning framework to pursue more thorough domain-difference elimination.

Face Recognition Meta-Learning +1

Hybrid Differentially Private Federated Learning on Vertically Partitioned Data

no code implementations6 Sep 2020 Chang Wang, Jian Liang, Mingkai Huang, Bing Bai, Kun Bai, Hao Li

We present HDP-VFL, the first hybrid differentially private (DP) framework for vertical federated learning (VFL) to demonstrate that it is possible to jointly learn a generalized linear model (GLM) from vertically partitioned data with only a negligible cost, w. r. t.

Federated Learning Privacy Preserving

A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations

no code implementations25 Aug 2020 Mingkai Huang, Hao Li, Bing Bai, Chang Wang, Kun Bai, Fei Wang

Federated Learning(FL) is a newly developed privacy-preserving machine learning paradigm to bridge data repositories without compromising data security and privacy.

Collaborative Filtering Federated Learning +1

Why Attentions May Not Be Interpretable?

no code implementations10 Jun 2020 Bing Bai, Jian Liang, Guanhua Zhang, Hao Li, Kun Bai, Fei Wang

In this paper, we demonstrate that one root cause of this phenomenon is the combinatorial shortcuts, which means that, in addition to the highlighted parts, the attention weights themselves may carry extra information that could be utilized by downstream models after attention layers.

Feature Importance

Adversarial Infidelity Learning for Model Interpretation

1 code implementation9 Jun 2020 Jian Liang, Bing Bai, Yuren Cao, Kun Bai, Fei Wang

A popular way of performing model interpretation is Instance-wise Feature Selection (IFS), which provides an importance score of each feature representing the data samples to explain how the model generates the specific output.

General-Purpose User Embeddings based on Mobile App Usage

1 code implementation27 May 2020 Junqi Zhang, Bing Bai, Ye Lin, Jian Liang, Kun Bai, Fei Wang

In this paper, we report our recent practice at Tencent for user modeling based on mobile app usage.

Feature Engineering

Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting

1 code implementation ACL 2020 Guanhua Zhang, Bing Bai, Junqi Zhang, Kun Bai, Conghui Zhu, Tiejun Zhao

In this paper, we formalize the unintended biases in text classification datasets as a kind of selection bias from the non-discrimination distribution to the discrimination distribution.

Abusive Language General Classification +3

CSRN: Collaborative Sequential Recommendation Networks for News Retrieval

no code implementations7 Apr 2020 Bing Bai, Guanhua Zhang, Ye Lin, Hao Li, Kun Bai, Bo Luo

Recurrent Neural Network (RNN)-based sequential recommendation is a popular approach that utilizes users' recent browsing history to predict future items.

Collaborative Filtering News Retrieval +2

Generating Followup Questions for Interpretable Multi-hop Question Answering

no code implementations27 Feb 2020 Christopher Malon, Bing Bai

As a first instantiation of this framework, we train a pointer-generator network to predict followup questions based on the question and partial information.

Multi-hop Question Answering Question Answering +4

Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets

2 code implementations ACL 2019 Guanhua Zhang, Bing Bai, Jian Liang, Kun Bai, Shiyu Chang, Mo Yu, Conghui Zhu, Tiejun Zhao

Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process.

Selection bias

Bayesian models for Large-scale Hierarchical Classification

no code implementations NeurIPS 2012 Siddharth Gopal, Yiming Yang, Bing Bai, Alexandru Niculescu-Mizil

A challenging problem in hierarchical classification is to leverage the hierarchical relations among classes for improving classification performance.

Classification General Classification

Polynomial Semantic Indexing

no code implementations NeurIPS 2009 Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Kunihiko Sadamasa, Yanjun Qi, Corinna Cortes, Mehryar Mohri

We present a class of nonlinear (polynomial) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score.


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