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Showing 726–750 of 1626 papers

TitleStatusHype
DTeam @ VarDial 2019: Ensemble based on skip-gram and triplet loss neural networks for Moldavian vs. Romanian cross-dialect topic identification—0
An Interpretable Representation Learning Approach for Diffusion Tensor Imaging—0
Adversarial Attack on Deep Cross-Modal Hamming Retrieval—0
Acoustic Scene Classification Based on a Large-margin Factorized CNN—0
6MapNet: Representing soccer players from tracking data by a triplet network—0
Explainable High-order Visual Question Reasoning: A New Benchmark and Knowledge-routed Network—0
Driver2vec: Driver Identification from Automotive Data—0
Cat, Rat, Meow: On the Alignment of Language Model and Human Term-Similarity Judgments—0
DreamRelation: Relation-Centric Video Customization—0
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders—0
An Improved Deep Learning Approach For Product Recognition on Racks in Retail Stores—0
Do not trust the neighbors! Adversarial Metric Learning for Self-Supervised Scene Flow Estimation—0
DIRL: Domain-Invariant Representation Learning for Sim-to-Real Transfer—0
Camera-Invariant Meta-Learning Network for Single-Camera-Training Person Re-identification—0
Angular Triplet-Center Loss for Multi-view 3D Shape Retrieval—0
Domain Alignment with Triplets—0
Angular triangle distance for ordinal metric learning—0
Does Knowledge Localization Hold True? Surprising Differences Between Entity and Relation Perspectives in Language Models—0
Dividing and Conquering Cross-Modal Recipe Retrieval: from Nearest Neighbours Baselines to SoTA—0
Bures Joint Distribution Alignment with Dynamic Margin for Unsupervised Domain Adaptation—0
An example of prediction which complies with Demographic Parity and equalizes group-wise risks in the context of regression—0
Adopting the Word-Pair-Dependency-Triplets with Individual Comparison for Natural Language Inference—0
Acoustic Neighbor Embeddings—0
Diversified Mutual Learning for Deep Metric Learning—0
Diversified and Adaptive Negative Sampling on Knowledge Graphs—0
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