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Found 16 papers, 9 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.
FloE: On-the-Fly MoE Inference on Memory-constrained GPU
With the widespread adoption of Mixture-of-Experts (MoE) models, there is a growing demand for efficient inference on memory-constrained devices.
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning
Active learning (AL) reduces human annotation costs for machine learning systems by strategically selecting the most informative unlabeled data for annotation, but performing it individually may still be insufficient due to restricted data diversity and annotation budget.
NLCTables: A Dataset for Marrying Natural Language Conditions with Table Discovery
With the growing abundance of repositories containing tabular data, discovering relevant tables for in-depth analysis remains a challenging task.
Train Small, Infer Large: Memory-Efficient LoRA Training for Large Language Models
For a model with 70 billion parameters, LoRAM enables training on a GPU with only 20G HBM, replacing an A100-80G GPU for LoRA training and 15 GPUs for full fine-tuning.
Preventing the Popular Item Embedding Based Attack in Federated Recommendations
The PIECKUEA further enhances the robustness of the attack by using a user embedding approximation module, which approximates private user embeddings using mined popular items.
A Comprehensive Study of Shapley Value in Data Analytics
Over the recent years, Shapley value (SV), a solution concept from cooperative game theory, has found numerous applications in data analytics (DA).
KcMF: A Knowledge-compliant Framework for Schema and Entity Matching with Fine-tuning-free LLMs
This study presents the Knowledge-Compliant Matching Framework (KcMF), an LLM-based approach that addresses these issues without the need for domain-specific fine-tuning.
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.
