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Robust Dialogue Utterance Rewriting as Sequence Tagging
The task of dialogue rewriting aims to reconstruct the latest dialogue u...
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Fully Convolutional Networks for Panoptic Segmentation
In this paper, we present a conceptually simple, strong, and efficient f...
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Learnable Boundary Guided Adversarial Training
Previous adversarial training raises model robustness under the compromi...
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LID 2020: The Learning from Imperfect Data Challenge Results
Learning from imperfect data becomes an issue in many industrial applica...
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Improved Analysis of Clipping Algorithms for Non-convex Optimization
Gradient clipping is commonly used in training deep neural networks part...
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Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot
Network pruning is a method for reducing test-time computational resourc...
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GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
Normalization plays an important role in the optimization of deep neural...
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Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL
Reinforcement learning (RL) in episodic, factored Markov decision proces...
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Improving One-stage Visual Grounding by Recursive Sub-query Construction
We improve one-stage visual grounding by addressing current limitations ...
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RANDOM MASK: Towards Robust Convolutional Neural Networks
Robustness of neural networks has recently been highlighted by the adver...
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Transferred Discrepancy: Quantifying the Difference Between Representations
Understanding what information neural networks capture is an essential p...
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Comprehensive Image Captioning via Scene Graph Decomposition
We address the challenging problem of image captioning by revisiting the...
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RepPoints V2: Verification Meets Regression for Object Detection
Verification and regression are two general methodologies for prediction...
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Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation
Weakly supervised phrase grounding aims at learning region-phrase corres...
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Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems
Metamaterials are emerging as a new paradigmatic material system to rend...
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Data-Driven Topology Optimization with Multiclass Microstructures using Latent Variable Gaussian Process
The data-driven approach is emerging as a promising method for the topol...
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MC-BERT: Efficient Language Pre-Training via a Meta Controller
Pre-trained contextual representations (e.g., BERT) have become the foun...
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Multi-modal Feature Fusion with Feature Attention for VATEX Captioning Challenge 2020
This report describes our model for VATEX Captioning Challenge 2020. Fir...
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(Locally) Differentially Private Combinatorial Semi-Bandits
In this paper, we study Combinatorial Semi-Bandits (CSB) that is an exte...
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Locally Differentially Private (Contextual) Bandits Learning
We study locally differentially private (LDP) bandits learning in this p...
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METASET: Exploring Shape and Property Spaces for Data-Driven Metamaterials Design
Data-driven design of mechanical metamaterials is an increasingly popula...
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Boosting Few-Shot Learning With Adaptive Margin Loss
Few-shot learning (FSL) has attracted increasing attention in recent yea...
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Improve bone age assessment by learning from anatomical local regions
Skeletal bone age assessment (BAA), as an essential imaging examination,...
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MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning
Generating multi-sentence descriptions for videos is one of the most cha...
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Memory Enhanced Global-Local Aggregation for Video Object Detection
How do humans recognize an object in a piece of video? Due to the deteri...
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Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories
We address the problem of discovering 3D parts for objects in unseen cat...
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On Layer Normalization in the Transformer Architecture
The Transformer is widely used in natural language processing tasks. To ...
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Combinatorial Semi-Bandit in the Non-Stationary Environment
In this paper, we investigate the non-stationary combinatorial semi-band...
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MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius
Adversarial training is one of the most popular ways to learn robust mod...
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Dense RepPoints: Representing Visual Objects with Dense Point Sets
We present an object representation, called Dense RepPoints, for flexibl...
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Defective Convolutional Layers Learn Robust CNNs
Robustness of convolutional neural networks has recently been highlighte...
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Development of Clinical Concept Extraction Applications: A Methodology Review
Our study provided a review of the development of clinical concept extra...
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A Review of the End-to-End Methodologies for Clinical Concept Extraction
Our study provided a review of the concept extraction literature from Ja...
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On the Anomalous Generalization of GANs
Generative models, especially Generative Adversarial Networks (GANs), ha...
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Robust Local Features for Improving the Generalization of Adversarial Training
Adversarial training has been demonstrated as one of the most effective ...
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Hint-Based Training for Non-Autoregressive Machine Translation
Due to the unparallelizable nature of the autoregressive factorization, ...
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McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds
A crucial assumption in most statistical learning theory is that samples...
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A Fast and Accurate One-Stage Approach to Visual Grounding
We propose a simple, fast, and accurate one-stage approach to visual gro...
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Nesterov Accelerated Gradient and Scale Invariance for Improving Transferability of Adversarial Examples
Recent evidence suggests that deep neural networks (DNNs) are vulnerable...
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Few-Shot Learning with Global Class Representations
In this paper, we propose to tackle the challenging few-shot learning (F...
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Representation Degeneration Problem in Training Natural Language Generation Models
We study an interesting problem in training neural network-based models ...
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Cross-lingual Data Transformation and Combination for Text Classification
Text classification is a fundamental task for text data mining. In order...
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Convergence of Adversarial Training in Overparametrized Networks
Neural networks are vulnerable to adversarial examples, i.e. inputs that...
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Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View
The Transformer architecture is widely used in natural language processi...
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Adversarially Robust Generalization Just Requires More Unlabeled Data
Neural network robustness has recently been highlighted by the existence...
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Equipping Experts/Bandits with Long-term Memory
We propose the first reduction-based approach to obtaining long-term mem...
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A Gram-Gauss-Newton Method Learning Overparameterized Deep Neural Networks for Regression Problems
First-order methods such as stochastic gradient descent (SGD) are curren...
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Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
Previous cross-lingual knowledge graph (KG) alignment studies rely on en...
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RepPoints: Point Set Representation for Object Detection
Modern object detectors rely heavily on rectangular bounding boxes, such...
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Distributed Bandit Learning: How Much Communication is Needed to Achieve (Near) Optimal Regret
We study the communication complexity of distributed multi-armed bandits...
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