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Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search
Designing proper loss functions for vision tasks has been a long-standin...
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FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques
It is extensively studied that Deep Neural Networks (DNNs) are vulnerabl...
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An Investigation of Potential Function Designs for Neural CRF
The neural linear-chain CRF model is one of the most widely-used approac...
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Neural Latent Dependency Model for Sequence Labeling
Sequence labeling is a fundamental problem in machine learning, natural ...
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Reducing the Annotation Effort for Video Object Segmentation Datasets
For further progress in video object segmentation (VOS), larger, more di...
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Second-Order Unsupervised Neural Dependency Parsing
Most of the unsupervised dependency parsers are based on first-order pro...
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When Machine Learning Meets Congestion Control: A Survey and Comparison
Machine learning (ML) has seen a significant surge and uptake across man...
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Structural Knowledge Distillation
Knowledge distillation is a critical technique to transfer knowledge bet...
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Automated Concatenation of Embeddings for Structured Prediction
Pretrained contextualized embeddings are powerful word representations f...
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Adversarial Attack and Defense of Structured Prediction Models
Building an effective adversarial attacker and elaborating on countermea...
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A Survey of Unsupervised Dependency Parsing
Syntactic dependency parsing is an important task in natural language pr...
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Improving Query Efficiency of Black-box Adversarial Attack
Deep neural networks (DNNs) have demonstrated excellent performance on v...
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More Embeddings, Better Sequence Labelers?
Recent work proposes a family of contextual embeddings that significantl...
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Fast and Accurate Sequence Labeling with Approximate Inference Network
The linear-chain Conditional Random Field (CRF) model is one of the most...
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Rectified Decision Trees: Exploring the Landscape of Interpretable and Effective Machine Learning
Interpretability and effectiveness are two essential and indispensable r...
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Neural Network-based Automatic Factor Construction
Instead of conducting manual factor construction based on traditional an...
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A Large-Scale Chinese Short-Text Conversation Dataset
The advancements of neural dialogue generation models show promising res...
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Backdoor Learning: A Survey
Deep neural networks (DNNs) have demonstrated their power on many widely...
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Stochastic Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization
In this paper, we introduce a simplified and unified method for finite-s...
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Enhanced Universal Dependency Parsing with Second-Order Inference and Mixture of Training Data
This paper presents the system used in our submission to the IWPT 2020 S...
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Rethinking the Trigger of Backdoor Attack
In this work, we study the problem of backdoor attacks, which add a spec...
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Structure-Level Knowledge Distillation For Multilingual Sequence Labeling
Multilingual sequence labeling is a task of predicting label sequences u...
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Detecting Suspected Epidemic Cases Using Trajectory Big Data
Emerging infectious diseases are crucial threats to human health and glo...
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Toward Adversarial Robustness via Semi-supervised Robust Training
Adversarial examples have been shown to be the severe threat to deep neu...
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Alpha Discovery Neural Network based on Prior Knowledge
In financial automatic feature construction task, genetic programming is...
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Deep Flow Collaborative Network for Online Visual Tracking
The deep learning-based visual tracking algorithms such as MDNet achieve...
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Visual Privacy Protection via Mapping Distortion
Data privacy protection is an important research area, which is especial...
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Adversarial Defense Via Local Flatness Regularization
Adversarial defense is a popular and important research area. Due to its...
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DAL: Dual Adversarial Learning for Dialogue Generation
In open-domain dialogue systems, generative approaches have attracted mu...
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Rectified Decision Trees: Towards Interpretability, Compression and Empirical Soundness
How to obtain a model with good interpretability and performance has alw...
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Multinomial Random Forests: Fill the Gap between Theoretical Consistency and Empirical Soundness
Random forests (RF) are one of the most widely used ensemble learning me...
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Interface-Based Side Channel Attack Against Intel SGX
Intel has introduced a trusted computing technology, Intel Software Guar...
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Fully Implicit Online Learning
Regularized online learning is widely used in machine learning. In this ...
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Maximum A Posteriori Inference in Sum-Product Networks
Sum-product networks (SPNs) are a class of probabilistic graphical model...
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CRF Autoencoder for Unsupervised Dependency Parsing
Unsupervised dependency parsing, which tries to discover linguistic depe...
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Dependency Grammar Induction with Neural Lexicalization and Big Training Data
We study the impact of big models (in terms of the degree of lexicalizat...
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Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition
Unsupervised dependency parsing aims to learn a dependency parser from u...
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Latent Dependency Forest Models
Probabilistic modeling is one of the foundations of modern machine learn...
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