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EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets
Deep, heavily overparameterized language models such as BERT, XLNet and ...
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Fair for All: Best-effort Fairness Guarantees for Classification
Standard approaches to group-based notions of fairness, such as parity a...
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DSAM: A Distance Shrinking with Angular Marginalizing Loss for High Performance Vehicle Re-identificatio
Vehicle Re-identification (ReID) is an important yet challenging problem...
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Object Tracking Using Spatio-Temporal Future Prediction
Occlusion is a long-standing problem that causes many modern tracking me...
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Cross-Thought for Sentence Encoder Pre-training
In this paper, we propose Cross-Thought, a novel approach to pre-trainin...
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Multi-Fact Correction in Abstractive Text Summarization
Pre-trained neural abstractive summarization systems have dominated extr...
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InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective
Large-scale language models such as BERT have achieved state-of-the-art ...
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Efficient Robust Training via Backward Smoothing
Adversarial training is so far the most effective strategy in defending ...
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Contrastive Distillation on Intermediate Representations for Language Model Compression
Existing language model compression methods mostly use a simple L2 loss ...
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Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding
Transformer has become ubiquitous in the deep learning field. One of the...
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Fine-grained Iterative Attention Network for TemporalLanguage Localization in Videos
Temporal language localization in videos aims to ground one video segmen...
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Graph Optimal Transport for Cross-Domain Alignment
Cross-domain alignment between two sets of entities (e.g., objects in an...
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Adaptive Learning Rates with Maximum Variation Averaging
Adaptive gradient methods such as RMSProp and Adam use exponential movin...
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Large-Scale Adversarial Training for Vision-and-Language Representation Learning
We present VILLA, the first known effort on large-scale adversarial trai...
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Sparsification of Balanced Directed Graphs
Sparsification, where the cut values of an input graph are approximately...
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Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models
Recent Transformer-based large-scale pre-trained models have revolutioni...
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Low-shot Object Detection via Classification Refinement
This work aims to address the problem of low-shot object detection, wher...
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High-Dimensional Robust Mean Estimation via Gradient Descent
We study the problem of high-dimensional robust mean estimation in the p...
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HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
We present HERO, a Hierarchical EncodeR for Omni-representation learning...
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Contextual Text Style Transfer
We introduce a new task, Contextual Text Style Transfer - translating a ...
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APo-VAE: Text Generation in Hyperbolic Space
Natural language often exhibits inherent hierarchical structure ingraine...
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Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
Pretrained models from self-supervision are prevalently used in fine-tun...
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BachGAN: High-Resolution Image Synthesis from Salient Object Layout
We propose a new task towards more practical application for image gener...
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VIOLIN: A Large-Scale Dataset for Video-and-Language Inference
We introduce a new task, Video-and-Language Inference, for joint multimo...
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Optimizing Non-Orthogonal Multiple Access in Random Access Networks
Non-orthogonal multiple access (NOMA) has been considered as a promising...
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Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV based Random Access IoT Networks with NOMA
In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) techni...
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Investigation of Numerical Dispersion with Time Step of The FDTD Methods: Avoiding Erroneous Conclusions
It is widely thought that small time steps lead to small numerical error...
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Topology Aware Deep Learning for Wireless Network Optimization
Data-driven machine learning approaches have recently been proposed to f...
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Bridging Disentanglement with Independence and Conditional Independence via Mutual Information for Representation Learning
Existing works on disentangled representation learning usually lie on a ...
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INSET: Sentence Infilling with Inter-sentential Generative Pre-training
Missing sentence generation (or sentence infilling) fosters a wide range...
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Distilling the Knowledge of BERT for Text Generation
Large-scale pre-trained language model, such as BERT, has recently achie...
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Discourse-Aware Neural Extractive Model for Text Summarization
Recently BERT has been adopted in state-of-the-art text summarization mo...
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Tell-the-difference: Fine-grained Visual Descriptor via a Discriminating Referee
In this paper, we investigate a novel problem of telling the difference ...
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Meta Module Network for Compositional Visual Reasoning
There are two main lines of research on visual reasoning: neural module ...
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FreeLB: Enhanced Adversarial Training for Language Understanding
Adversarial training, which minimizes the maximal risk for label-preserv...
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UNITER: Learning UNiversal Image-TExt Representations
Joint image-text embedding is the bedrock for most Vision-and-Language (...
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What Makes A Good Story? Designing Composite Rewards for Visual Storytelling
Previous storytelling approaches mostly focused on optimizing traditiona...
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Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation
Recent unsupervised approaches to domain adaptation primarily focus on m...
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Domain Adaptive Text Style Transfer
Text style transfer without parallel data has achieved some practical su...
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Patient Knowledge Distillation for BERT Model Compression
Pre-trained language models such as BERT have proven to be highly effect...
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Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs
Crowd counting is a challenging task due to the large variations in crow...
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Mixed-Supervised Dual-Network for Medical Image Segmentation
Deep learning-based medical image segmentation models usually require la...
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EnlightenGAN: Deep Light Enhancement without Paired Supervision
Deep learning-based methods have achieved remarkable success in image re...
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Faster Algorithms for High-Dimensional Robust Covariance Estimation
We study the problem of estimating the covariance matrix of a high-dimen...
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Group Fairness in Committee Selection
In this paper, we study fairness in committee selection problems. We con...
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A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning
Few-shot learning aims to learn classifiers for new classes with only a ...
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Relation-aware Graph Attention Network for Visual Question Answering
In order to answer semantically-complicated questions about an image, a ...
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POP-CNN: Predicting Odor's Pleasantness with Convolutional Neural Network
Predicting odor's pleasantness simplifies the evaluation of odors and ha...
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Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding
Evaluating the clinical similarities between pairwise patients is a fund...
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Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog
This paper presents Recurrent Dual Attention Network (ReDAN) for visual ...
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