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Found 11 papers, 3 papers with code
Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases
We proposed an end-to-end system design towards utilizing Retrieval Augmented Generation (RAG) to improve the factual accuracy of Large Language Models (LLMs) for domain-specific and time-sensitive queries related to private knowledge-bases.
Pretraining Strategy for Neural Potentials
We propose a mask pretraining method for Graph Neural Networks (GNNs) to improve their performance on fitting potential energy surfaces, particularly in water systems.
Line Graph Contrastive Learning for Link Prediction
Link prediction tasks focus on predicting possible future connections.
FB-MSTCN: A Full-Band Single-Channel Speech Enhancement Method Based on Multi-Scale Temporal Convolutional Network
In recent years, deep learning-based approaches have significantly improved the performance of single-channel speech enhancement.
Incorporating Multi-Target in Multi-Stage Speech Enhancement Model for Better Generalization
In this study, we make full use of the contribution of multi-target joint learning to the model generalization capability, and propose a lightweight and low-computing dilated convolutional network (DCN) model for a more robust speech denoising task.
Deep Interaction between Masking and Mapping Targets for Single-Channel Speech Enhancement
In this paper, we propose a multi-branch dilated convolutional network (DCN) to simultaneously enhance the magnitude and phase of noisy speech.
Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning
We present a novel technique for self-supervised video representation learning by: (a) decoupling the learning objective into two contrastive subtasks respectively emphasizing spatial and temporal features, and (b) performing it hierarchically to encourage multi-scale understanding.
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
First, conventional attentions mostly limit the attention field only to a single user's behaviors, which is not suitable in e-commerce where users often hunt for new demands that are irrelevant to any historical behaviors.
Category-Specific CNN for Visual-aware CTR Prediction at JD.com
Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR.
Interaction Graphs for Object Importance Estimation in On-road Driving Videos
A vehicle driving along the road is surrounded by many objects, but only a small subset of them influence the driver's decisions and actions.
A Self Validation Network for Object-Level Human Attention Estimation
Due to the foveated nature of the human vision system, people can focus their visual attention on a small region of their visual field at a time, which usually contains only a single object.
