Search Results for author:
Found 25 papers, 9 papers with code
Polynomial Semantic Indexing
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.
Bayesian models for Large-scale Hierarchical Classification
A challenging problem in hierarchical classification is to leverage the hierarchical relations among classes for improving classification performance.
Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets
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.
Mitigating Annotation Artifacts in Natural Language Inference Datasets to Improve Cross-dataset Generalization Ability
This irregularity makes the evaluation results over-estimated and affects models' generalization ability.
Generating Followup Questions for Interpretable Multi-hop Question Answering
As a first instantiation of this framework, we train a pointer-generator network to predict followup questions based on the question and partial information.
CSRN: Collaborative Sequential Recommendation Networks for News Retrieval
Recurrent Neural Network (RNN)-based sequential recommendation is a popular approach that utilizes users' recent browsing history to predict future items.
Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting
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.
General-Purpose User Embeddings based on Mobile App Usage
In this paper, we report our recent practice at Tencent for user modeling based on mobile app usage.
Adversarial Infidelity Learning for Model Interpretation
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.
Why Attentions May Not Be Interpretable?
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.
A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations
Federated Learning(FL) is a newly developed privacy-preserving machine learning paradigm to bridge data repositories without compromising data security and privacy.
Hybrid Differentially Private Federated Learning on Vertically Partitioned Data
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.
Domain Agnostic Learning for Unbiased Authentication
We further extend our method to a meta-learning framework to pursue more thorough domain-difference elimination.
Reliable Evaluations for Natural Language Inference based on a Unified Cross-dataset Benchmark
Recent studies show that crowd-sourced Natural Language Inference (NLI) datasets may suffer from significant biases like annotation artifacts.
2nd Place Solution for Waymo Open Dataset Challenge -- Real-time 2D Object Detection
In an autonomous driving system, it is essential to recognize vehicles, pedestrians and cyclists from images.
2nd Place Solution for Waymo Open Dataset Challenge - Real-time 2D Object Detection
In an autonomous driving system, it is essential to recognize vehicles, pedestrians and cyclists from images.
Ranked #1 on
Object Detection
on Waymo Open Dataset
Capture Uncertainties in Deep Neural Networks for Safe Operation of Autonomous Driving Vehicles
Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles.
Contrastive Multi-view Hyperbolic Hierarchical Clustering
Then, we embed the representations into a hyperbolic space and optimize the hyperbolic embeddings via a continuous relaxation of hierarchical clustering loss.
Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets
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.
DartBlur: Privacy Preservation With Detection Artifact Suppression
Facial information is particularly sensitive in this regard.
MHCN: A Hyperbolic Neural Network Model for Multi-view Hierarchical Clustering
Multi-view hierarchical clustering (MCHC) plays a pivotal role in comprehending the structures within multi-view data, which hinges on the skillful interaction between hierarchical feature learning and comprehensive representation learning across multiple views.
RealGraph: A Multiview Dataset for 4D Real-world Context Graph Generation
The CGG task capitalizes on the calibrated multiview videos of a dynamic scene, and targets at recovering semantic information (coordination, trajectories and relationships) of the presented objects in the form of spatio-temporal context graph in 4D space.
A Novel Convolutional Neural Network Architecture with a Continuous Symmetry
This paper introduces a new Convolutional Neural Network (ConvNet) architecture inspired by a class of partial differential equations (PDEs) called quasi-linear hyperbolic systems.
A Virtual Reality Training System for Automotive Engines Assembly and Disassembly
A free-to-use executable file (Microsoft Windows) and open-source code are available at https://github. com/LadissonLai/SUSTech_VREngine for facilitating the development of VR systems in the automotive industry.
