Search Results for author:
Found 10 papers, 2 papers with code
On Leveraging Large Language Models for Enhancing Entity Resolution
Entity resolution, the task of identifying and consolidating records that pertain to the same real-world entity, plays a pivotal role in various sectors such as e-commerce, healthcare, and law enforcement.
S$^{2}$-DMs:Skip-Step Diffusion Models
On the CIFAR10 dataset, models trained using our algorithm showed an improvement of 3. 27% to 14. 06% over models trained with traditional methods across various sampling algorithms (DDIMs, PNDMs, DEIS) and different numbers of sampling steps (10, 20, ..., 1000).
Single-Cell RNA-seq Synthesis with Latent Diffusion Model
The single-cell RNA sequencing (scRNA-seq) technology enables researchers to study complex biological systems and diseases with high resolution.
Topic-DPR: Topic-based Prompts for Dense Passage Retrieval
Prompt-based learning's efficacy across numerous natural language processing tasks has led to its integration into dense passage retrieval.
Identical and Fraternal Twins: Fine-Grained Semantic Contrastive Learning of Sentence Representations
While the InfoNCE loss function overlooks the semantic margin and prioritizes similarity maximization between positive pairs during training, leading to the insensitive semantic comprehension ability of the trained model.
An Adversarial Transfer Network for Knowledge Representation Learning
Specifically, we add soft constraints on aligned entity pairs and neighbours to the existing knowledge representation learning methods.
Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning
Meanwhile, a novel mechanism of reinforcement learning is proposed by forcing an agent to walk forward every step to avoid the agent stalling at the same entity node constantly.
Incorporating GAN for Negative Sampling in Knowledge Representation Learning
In this GAN-based framework, we take advantage of a generator to obtain high-quality negative samples.
Personalizing a Dialogue System with Transfer Reinforcement Learning
One way to solve this problem is to consider a collection of multiple users' data as a source domain and an individual user's data as a target domain, and to perform a transfer learning from the source to the target domain.
Tag-Weighted Topic Model For Large-scale Semi-Structured Documents
We propose a novel method to model the SSDs by a so-called Tag-Weighted Topic Model (TWTM).
