Architectures

An Efficient Transformer Decoder with Compressed Sub-layers AAAI 2021 An Empirical Investigation of Word Alignment Supervision for Zero-Shot Multilingual Neural Machine Translation EMNLP 2021 An Empirical Study of Adder Neural Networks for Object Detection NeurIPS 2021 An Empirical Study of Training Self-Supervised Vision Transformers ICCV 2021 An Empirical Study on Adversarial Attack on NMT: Languages and Positions Matter ACL 2021 An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction EMNLP 2021 An End-to-End Approach for Full Bridging Resolution EMNLP 2021 An End-to-End Transformer Model for 3D Object Detection ICCV 2021 A Neural Graph-based Local Coherence Model EMNLP 2021 A Neural-Network-Based Approach to Identifying Speakers in Novels Interspeech 2021 An Examination of Fairness of AI Models for Deepfake Detection IJCAI 2021 An Exploratory Study on Long Dialogue Summarization: What Works and What’s Next EMNLP 2021 An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks NeurIPS 2021 An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale ICLR 2021 An Inertial Newton Algorithm for Deep Learning JMLR 2021 An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence NeurIPS 2021 An Interpretable Framework for Drug-Target Interaction with Gated Cross Attention MLHC 2021 An Investigation into the Contribution of Locally Aggregated Descriptors to Figurative Language Identification EMNLP 2021 An Investigation of Language Model Interpretability via Sentence Editing EMNLP 2021 Anlirika: An LSTM–CNN Flow Twister for Spoken Language Identification NAACL 2021 An MRF-UNet Product of Experts for Image Segmentation MIDL 2021 An optimization and generalization analysis for max-pooling networks UAI 2021 A Novel Framework for Detecting Important Subevents from Crisis Events via Dynamic Semantic Graphs EMNLP 2021 A Novel Sequence-to-Subgraph Framework for Diagnosis Classification IJCAI 2021 A Novel Visual Interpretability for Deep Neural Networks by Optimizing Activation Maps with Perturbation AAAI 2021