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Papers
Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders
ACL 2020
Pre-training Entity Relation Encoder with Intra-span and Inter-span Information
EMNLP 2020
Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information
EMNLP 2020
Pre-Training Transformers as Energy-Based Cloze Models
EMNLP 2020
Probing Emergent Semantics in Predictive Agents via Question Answering
ICML 2020
Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction
ACL 2020
Probing Multimodal Embeddings for Linguistic Properties: the Visual-Semantic Case
COLING 2020
Probing Neural Dialog Models for Conversational Understanding
ACL 2020
Probing Pretrained Language Models for Lexical Semantics
EMNLP 2020
Probing Task-Oriented Dialogue Representation from Language Models
EMNLP 2020
Provable Representation Learning for Imitation Learning via Bi-level Optimization
ICML 2020
Proxy Anchor Loss for Deep Metric Learning
CVPR 2020
Pseudo-Bidirectional Decoding for Local Sequence Transduction
EMNLP 2020
PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision
EMNLP 2020
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning
NeurIPS 2020
Quantifying the Contextualization of Word Representations with Semantic Class Probing
EMNLP 2020
Quantitative stability of optimal transport maps and linearization of the 2-Wasserstein space
AISTATS 2020
Quaternion Product Units for Deep Learning on 3D Rotation Groups
CVPR 2020
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures
ICML 2020
RankMI: A Mutual Information Maximizing Ranking Loss
CVPR 2020
RD$^2$: Reward Decomposition with Representation Decomposition
NeurIPS 2020
Real-World Person Re-Identification via Degradation Invariance Learning
CVPR 2020
Reflection-based Word Attribute Transfer
ACL 2020
Region Pooling with Adaptive Feature Fusion for End-to-End Person Recognition
WACV 2020
Regularized linear autoencoders recover the principal components, eventually
NeurIPS 2020