Search Results for author:
Found 12 papers, 5 papers with code
Aiming at the Target: Filter Collaborative Information for Cross-Domain Recommendation
In CUT, user similarity in the target domain is adopted as a constraint for user transformation learning to filter the user collaborative information from the source domain.
TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned Decision
Among these works, many of them utilize in-context examples to achieve generalization without the need for fine-tuning, while few of them have considered the problem of how to select and effectively utilize these examples.
Fine-grainedly Synthesize Streaming Data Based On Large Language Models With Graph Structure Understanding For Data Sparsity
Due to the sparsity of user data, sentiment analysis on user reviews in e-commerce platforms often suffers from poor performance, especially when faced with extremely sparse user data or long-tail labels.
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment
In specific, causal intervention is implemented by designing the prompts without accessing the parameters and logits of LLMs. The chain-of-thoughts generated by LLMs are employed as the mediator variable and the causal effect between the input prompt and the output answers is calculated through front-door adjustment to mitigate model biases.
DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal Inference
However, most of the present debiasing methods focus on single-variable causal inference, which is not suitable for ABSA with two input variables (the target aspect and the review).
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models
For poor model calibration, we incorporate the regularization method during LoRA training to keep the model from being over-confident, and the Monte-Carlo dropout mechanism is employed to enhance the uncertainty estimation.
Modeling User Repeat Consumption Behavior for Online Novel Recommendation
In this paper, we concentrate on recommending online novels to new users of an online novel reading platform, whose first visits to the platform occurred in the last seven days.
A Multi-modal and Multi-task Learning Method for Action Unit and Expression Recognition
Analyzing human affect is vital for human-computer interaction systems.
Curriculum-Meta Learning for Order-Robust Continual Relation Extraction
We propose a novel curriculum-meta learning method to tackle the above two challenges in continual relation extraction.
Learning to Augment Expressions for Few-shot Fine-grained Facial Expression Recognition
Considering the phenomenon of uneven data distribution and lack of samples is common in real-world scenarios, we further evaluate several tasks of few-shot expression learning by virtue of our F2ED, which are to recognize the facial expressions given only few training instances.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
A Fine-Grained Facial Expression Database for End-to-End Multi-Pose Facial Expression Recognition
First, we create a new facial expression dataset of more than 200k images with 119 persons, 4 poses and 54 expressions.
Facial Expression Recognition
Facial Expression Recognition (FER)
+2
Multimodal Emotion Recognition for One-Minute-Gradual Emotion Challenge
The continuous dimensional emotion modelled by arousal and valence can depict complex changes of emotions.
