Search Results for author:
Found 12 papers, 8 papers with code
SlimSAM: 0.1% Data Makes Segment Anything Slim
To address this challenging trade-off, we introduce SlimSAM, a novel data-efficient SAM compression method that achieves superior performance with extremely less training data.
DeepCache: Accelerating Diffusion Models for Free
Diffusion models have recently gained unprecedented attention in the field of image synthesis due to their remarkable generative capabilities.
LLM-Pruner: On the Structural Pruning of Large Language Models
With LLM being a general-purpose task solver, we explore its compression in a task-agnostic manner, which aims to preserve the multi-task solving and language generation ability of the original LLM.
Structural Pruning for Diffusion Models
Generative modeling has recently undergone remarkable advancements, primarily propelled by the transformative implications of Diffusion Probabilistic Models (DPMs).
DepGraph: Towards Any Structural Pruning
Structural pruning enables model acceleration by removing structurally-grouped parameters from neural networks.
Prompting to Distill: Boosting Data-Free Knowledge Distillation via Reinforced Prompt
Data-free knowledge distillation (DFKD) conducts knowledge distillation via eliminating the dependence of original training data, and has recently achieved impressive results in accelerating pre-trained language models.
MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations
Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing.
Ranked #1 on
Entity Retrieval
on ZESHEL
Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition
Although these methods have the innate ability to handle nested NER, they suffer from high computational cost, ignorance of boundary information, under-utilization of the spans that partially match with entities, and difficulties in long entity recognition.
Ranked #6 on
Nested Named Entity Recognition
on GENIA
Chinese Named Entity Recognition
named-entity-recognition
+3
A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction
Joint entity and relation extraction framework constructs a unified model to perform entity recognition and relation extraction simultaneously, which can exploit the dependency between the two tasks to mitigate the error propagation problem suffered by the pipeline model.
Ranked #1 on
Relation Extraction
on CoNLL04
(NER Micro F1 metric)
Joint Entity and Relation Extraction
Reading Comprehension
+2
SynET: Synonym Expansion using Transitivity
Extensive experiments on a real-world dataset show the effectiveness of our approach.
Adversarial Self-Supervised Data-Free Distillation for Text Classification
To the best of our knowledge, our framework is the first data-free distillation framework designed for NLP tasks.
Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network
Meanwhile, we propose Gated-RGCN to accumulate evidence on the path-based reasoning graph, which contains a new question-aware gating mechanism to regulate the usefulness of information propagating across documents and add question information during reasoning.
