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Found 9 papers, 6 papers with code
SkipBERT: Efficient Inference with Shallow Layer Skipping
In this paper, we propose SkipBERT to accelerate BERT inference by skipping the computation of shallow layers.
FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning
Federated learning (FL) is a promising approach for learning a model from data distributed on massive clients without exposing data privacy.
CARAT: Contrastive Feature Reconstruction and Aggregation for Multi-Modal Multi-Label Emotion Recognition
The challenge of MMER is how to effectively capture discriminative features for multiple labels from heterogeneous data.
Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding
This approach is characterized by a two-stage process: drafting and verification.
Effective Slot Filling via Weakly-Supervised Dual-Model Learning
By using some particular weakly-labeled data, namely the plain phrases included in sentences, we propose a weaklysupervised slot filling approach.
LINDT: Tackling Negative Federated Learning with Local Adaptation
On occasion of NFL recovery, the framework makes adaptation to the federated model on each client's local data by learning a Layer-wise Intertwined Dual-model.
An Experimental Analysis of Indoor Spatial Queries: Modeling, Indexing, and Processing
Indoor location-based services (LBS), such as POI search and routing, are often built on top of typical indoor spatial queries.
Databases Data Structures and Algorithms
Pyramid: A Layered Model for Nested Named Entity Recognition
Its hidden state at layer l represents an l-gram in the input text, which is labeled only if its corresponding text region represents a complete entity mention.
Ranked #1 on
Nested Named Entity Recognition
on NNE
Semi-Supervised Few-Shot Learning for Dual Question-Answer Extraction
In this paper, we redefine the problem as question-answer extraction, and present SAMIE: Self-Asking Model for Information Ixtraction, a semi-supervised model which dually learns to ask and to answer questions by itself.
