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Found 9 papers, 6 papers with code
Graph Domain Adaptation: Challenges, Progress and Prospects
To the best of our knowledge, this paper is the first survey for graph domain adaptation.
Causality and Independence Enhancement for Biased Node Classification
Most existing methods that address out-of-distribution (OOD) generalization for node classification on graphs primarily focus on a specific type of data biases, such as label selection bias or structural bias.
OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning
Overall, OpenGDA provides a user-friendly, scalable and reproducible benchmark for evaluating graph domain adaptation models.
GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio
This paper introduces GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10, 000 hours of high quality labeled audio suitable for supervised training, and 40, 000 hours of total audio suitable for semi-supervised and unsupervised training.
Ranked #1 on
Speech Recognition
on GigaSpeech
Locate Who You Are: Matching Geo-location to Text for User Identity Linkage
However, encouraged by online services, users would also post asymmetric information across networks, such as geo-locations and texts.
speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment
This paper introduces a new open-source speech corpus named "speechocean762" designed for pronunciation assessment use, consisting of 5000 English utterances from 250 non-native speakers, where half of the speakers are children.
Ranked #7 on
Phone-level pronunciation scoring
on speechocean762
GCN-ALP: Addressing Matching Collisions in Anchor Link Prediction
However, the low quality of observed user data confuses the judgment on anchor links, resulting in the matching collision problem in practice.
Cascade Dynamics Modeling with Attention-based Recurrent Neural Network
However, for cascade prediction, each cascade generally corresponds to a diffusion tree, causing cross-dependence in cascade— one sharing behavior could be triggered by its non-immediate predecessor in the memory chain.
Marked Temporal Dynamics Modeling based on Recurrent Neural Network
Indeed, in marked temporal dynamics, the time and the mark of the next event are highly dependent on each other, requiring a method that could simultaneously predict both of them.
