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Found 13 papers, 4 papers with code
AISFG: Abundant Information Slot Filling Generator
As an essential component of task-oriented dialogue systems, slot filling requires enormous labeled training data in a certain domain.
Enhancing Chest X-ray Classification through Knowledge Injection in Cross-Modality Learning
We evaluate the model's performance through zero-shot classification on the CheXpert dataset, a benchmark for CXR classification.
An Oversampling-enhanced Multi-class Imbalanced Classification Framework for Patient Health Status Prediction Using Patient-reported Outcomes
Patient-reported outcomes (PROs) directly collected from cancer patients being treated with radiation therapy play a vital role in assisting clinicians in counseling patients regarding likely toxicities.
LLM4PR: Improving Post-Ranking in Search Engine with Large Language Models
In this study, we introduce a novel paradigm named Large Language Models for Post-Ranking in search engine (LLM4PR), which leverages the capabilities of LLMs to accomplish the post-ranking task in SE.
Predicting the Big Five Personality Traits in Chinese Counselling Dialogues Using Large Language Models
This study exams whether Large Language Models (LLMs) can predict the Big Five personality traits directly from counseling dialogues and introduces an innovative framework to perform the task.
LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment
We thus propose VIDAL-10M with Video, Infrared, Depth, Audio and their corresponding Language, naming as VIDAL-10M.
Ranked #1 on
Zero-shot Audio Classification
on VGG-Sound
(using extra training data)
Chatlaw: A Multi-Agent Collaborative Legal Assistant with Knowledge Graph Enhanced Mixture-of-Experts Large Language Model
AI legal assistants based on Large Language Models (LLMs) can provide accessible legal consulting services, but the hallucination problem poses potential legal risks.
SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds
To explore these issues for sequential interaction networks, we propose SINCERE, a novel method representing Sequential Interaction Networks on Co-Evolving RiEmannian manifolds.
Prediction of superconducting properties of materials based on machine learning models
Based on this, this manuscript proposes the use of XGBoost model to identify superconductors; the first application of deep forest model to predict the critical temperature of superconductors; the first application of deep forest to predict the band gap of materials; and application of a new sub-network model to predict the Fermi energy level of materials.
Hardness prediction of age-hardening aluminum alloy based on ensemble learning
Because aluminum alloy is widely used in many fields, so it is significant to predict the properties of aluminum alloy.
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER
Recently, prompt-based methods have achieved significant performance in few-shot learning scenarios by bridging the gap between language model pre-training and fine-tuning for downstream tasks.
Jointly Adversarial Network to Wavelength Compensation and Dehazing of Underwater Images
Different from most of previous underwater image enhancement methods that compute light attenuation along object-camera path through hazy image formation model, we propose a novel jointly wavelength compensation and dehazing network (JWCDN) that takes into account the wavelength attenuation along surface-object path and the scattering along object-camera path simultaneously.
Purifying Real Images with an Attention-guided Style Transfer Network for Gaze Estimation
Different from the previous methods, this paper try to purify real image by extracting discriminative and robust features to convert outdoor real images to indoor synthetic images.
