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Found 10 papers, 2 papers with code
LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views
By combining two complementing models, LEVI effectively suppresses problematic features in both the fine-tuning data and pre-trained model and preserves useful features for new tasks.
Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems
We identify two key challenges for applying the vanilla Transformer architecture to web-scale recommender systems: (1) Transformer architecture fails to capture the heterogeneous feature interactions in the self-attention layer; (2) The serving latency of Transformer architecture might be too high to be deployed in web-scale recommender systems.
Talking Models: Distill Pre-trained Knowledge to Downstream Models via Interactive Communication
In this paper, we extend KD with an interactive communication process to help students of downstream tasks learn effectively from pre-trained foundation models.
Expert Finding in Heterogeneous Bibliographic Networks with Locally-trained Embeddings
We study the task of expert finding in heterogeneous bibliographical networks based on two aspects: textual content analysis and authority ranking.
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks
Therefore, we are motivated to propose a novel embedding learning framework---AspEm---to preserve the semantic information in HINs based on multiple aspects.
Empower Sequence Labeling with Task-Aware Neural Language Model
In this study, we develop a novel neural framework to extract abundant knowledge hidden in raw texts to empower the sequence labeling task.
Ranked #13 on
Part-Of-Speech Tagging
on Penn Treebank
Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach
These annotations, referred as heterogeneous supervision, often conflict with each other, which brings a new challenge to the original relation extraction task: how to infer the true label from noisy labels for a given instance.
PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks
We also identify, from real-world data, and propose to model cross-meta-path synergy, which is a characteristic important for defining path-based HIN relevance and has not been modeled by existing methods.
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation
Moreover, we rigorously show that under a certain condition on the magnitude of the nonzero singular values, the proposed estimator enjoys oracle property (i. e., exactly recovers the true rank of the matrix), besides attaining a faster rate.
Robust Tensor Decomposition with Gross Corruption
In this paper, we study the statistical performance of robust tensor decomposition with gross corruption.
