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Found 21 papers, 4 papers with code
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs
LLMs fine-tuned with this data have improved planning capabilities, better equipping them to handle complex QA tasks that involve retrieval.
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
In recent years, large language models (LLMs) have made remarkable achievements in various domains.
Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs
To stimulate the LLMs' reasoning ability, the chain-of-thought (CoT) prompting method is widely used, but existing methods still have some limitations in our scenario: (1) Previous methods either use simple "Let's think step by step" spells or provide fixed examples in demonstrations without considering compatibility between prompts and concrete questions, making LLMs ineffective when the marketers' demands are abstract and diverse.
Temporal-Spatial Entropy Balancing for Causal Continuous Treatment-Effect Estimation
However, temporal and spatial dimensions are extremely critical in the logistics field, and this limitation may directly affect the precision of subsidy and pricing strategies.
Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation
In this paper, we seek to carefully prompt a Large Language Model (LLM) with domain-level knowledge as a better marketing-oriented knowledge miner for marketing-oriented knowledge graph construction, which is however non-trivial, suffering from several inevitable issues in real-world marketing scenarios, i. e., uncontrollable relation generation of LLMs, insufficient prompting ability of a single prompt, the unaffordable deployment cost of LLMs.
Deformable Convolutions and LSTM-based Flexible Event Frame Fusion Network for Motion Deblurring
It is also important to note that recent modern cameras (e. g., cameras in mobile phones) dynamically set the exposure time of the image, which presents an additional problem for networks developed for a fixed number of event frames.
Ranked #3 on
Deblurring
on GoPro
(using extra training data)
Who Would be Interested in Services? An Entity Graph Learning System for User Targeting
At the online stage, the system offers the ability of user targeting in real-time based on the entity graph from the offline stage.
Exploring Multimodal Sentiment Analysis via CBAM Attention and Double-layer BiLSTM Architecture
To remove redundant information and make the network pay more attention to the correlation between image and text features, CNN and CBAM attention are added after splicing text features and picture features, to improve the feature representation ability.
Weakly Supervised Patch Label Inference Networks for Efficient Pavement Distress Detection and Recognition in the Wild
Specifically, WSPLIN first divides the pavement image under different scales into patches with different collection strategies and then employs a Patch Label Inference Network (PLIN) to infer the labels of these patches to fully exploit the resolution and scale information.
Network regression and supervised centrality estimation
The centrality in a network is a popular metric for agents' network positions and is often used in regression models to model the network effect on an outcome variable of interest.
Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning
DCVAE conducts feature synthesis via pairing two Conditional Variational AutoEncoders (CVAEs) with the same seed but different modality conditions in a dizygotic symbiosis manner.
Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network
Considering the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution.
Representation Evaluation Block-based Teacher-Student Network for the Industrial Quality-relevant Performance Modeling and Monitoring
Quality-relevant fault detection plays an important role in industrial processes, while the current quality-related fault detection methods based on neural networks main concentrate on process-relevant variables and ignore quality-relevant variables, which restrict the application of process monitoring.
Motion Aware Double Attention Network for Dynamic Scene Deblurring
As part of the network, event data is first used by the high blur region segmentation module that creates a probability-like score for areas exhibiting high relative motion to the camera.
Ranked #10 on
Image Deblurring
on GoPro
(using extra training data)
Regression-based Hypergraph Learning for Image Clustering and Classification
Moreover, we plug RH into two conventional hypergraph learning frameworks, namely hypergraph spectral clustering and hypergraph transduction, to present Regression-based Hypergraph Spectral Clustering (RHSC) and Regression-based Hypergraph Transduction (RHT) models for addressing the image clustering and classification issues.
Learning Hypergraph-regularized Attribute Predictors
Then the attribute prediction problem is casted as a regularized hypergraph cut problem in which HAP jointly learns a collection of attribute projections from the feature space to a hypergraph embedding space aligned with the attribute space.
On The Effect of Hyperedge Weights On Hypergraph Learning
However, many studies on pairwise graphs show that the choice of edge weight can significantly influence the performances of such graph algorithms.
Sparse Graph-based Transduction for Image Classification
Then the Laplacian eigenmapping is adopted for deriving the graph Laplacian of the graph.
Shape Primitive Histogram: A Novel Low-Level Face Representation for Face Recognition
We further exploit the representational power of Haar wavelet and present a novel low-level face representation named Shape Primitives Histogram (SPH) for face recognition.
Collaborative Discriminant Locality Preserving Projections With its Application to Face Recognition
In our algorithm, the discriminating power of DLPP are further exploited from two aspects.
Face Recognition via Globality-Locality Preserving Projections
We present an improved Locality Preserving Projections (LPP) method, named Gloablity-Locality Preserving Projections (GLPP), to preserve both the global and local geometric structures of data.
