Search Results for author:
Found 30 papers, 19 papers with code
Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recommendations.
CellSeg1: Robust Cell Segmentation with One Training Image
We introduce CellSeg1, a practical solution for segmenting cells of arbitrary morphology and modality with a few dozen cell annotations in 1 image.
Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation
In this paper, we propose a novel Domain-Adaptive Prompt framework for fine-tuning the Segment Anything Model (termed as DAPSAM) to address single-source domain generalization (SDG) in segmenting medical images.
FinTruthQA: A Benchmark Dataset for Evaluating the Quality of Financial Information Disclosure
This paper builds a benchmark FinTruthQA, that can evaluate advanced natural language processing (NLP) techniques for the automatic quality assessment of information disclosure in financial Q&A data.
Vision-Language Models Meet Meteorology: Developing Models for Extreme Weather Events Detection with Heatmaps
Real-time detection and prediction of extreme weather protect human lives and infrastructure.
Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation
Intriguingly, the conclusion drawn from our study is that, certain data augmentation strategies can achieve similar or even superior performance compared with some CL-based methods, demonstrating the potential to significantly alleviate the data sparsity issue with fewer computational overhead.
Large Language Models in Mental Health Care: a Scoping Review
There is a need to systematically review the application outcomes and delineate the advantages and limitations in clinical settings.
Shifted Autoencoders for Point Annotation Restoration in Object Counting
Object counting typically uses 2D point annotations.
A Survey of Large Language Models in Medicine: Progress, Application, and Challenge
Therefore, this review aims to provide a detailed overview of the development and deployment of LLMs in medicine, including the challenges and opportunities they face.
Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case Study
Large Multimodal Models (LMMs) have demonstrated impressive performance across various vision and language tasks, yet their potential applications in recommendation tasks with visual assistance remain unexplored.
Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare
This method enhances the model's ability to generate medical captions and answer complex medical queries.
Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model
In the CPT and SFT phases, Qilin-Med achieved 38. 4% and 40. 0% accuracy on the CMExam test set, respectively.
LLMRec: Benchmarking Large Language Models on Recommendation Task
The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
Attention Calibration for Transformer-based Sequential Recommendation
Our empirical analysis of some representative Transformer-based SR models reveals that it is not uncommon for large attention weights to be assigned to less relevant items, which can result in inaccurate recommendations.
Streamlining Social Media Information Retrieval for COVID-19 Research with Deep Learning
Conclusions: This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data.
Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset
To the best of our knowledge, CMExam is the first Chinese medical exam dataset to provide comprehensive medical annotations.
Is ChatGPT a Good Recommender? A Preliminary Study
We conduct human evaluations on two explainability-oriented tasks to more accurately evaluate the quality of contents generated by different models.
Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems
To address these issues, we propose the REMI framework, consisting of an Interest-aware Hard Negative mining strategy (IHN) and a Routing Regularization (RR) method.
GreenPLM: Cross-Lingual Transfer of Monolingual Pre-Trained Language Models at Almost No Cost
Large pre-trained models have revolutionized natural language processing (NLP) research and applications, but high training costs and limited data resources have prevented their benefits from being shared equally amongst speakers of all the world's languages.
Equivariant Contrastive Learning for Sequential Recommendation
Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e. g., item substitution) and insensitive to mild augmentations (e. g., featurelevel dropout masking).
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets
To further investigate tweet users' attitudes toward specific entities, 4 types of entities (Person, Organization, Drug, and Vaccine) are selected and annotated with user sentiments, resulting in a targeted sentiment dataset with 9, 101 entities (in 5, 278 tweets).
Low-resource Accent Classification in Geographically-proximate Settings: A Forensic and Sociophonetics Perspective
Accented speech recognition and accent classification are relatively under-explored research areas in speech technology.
Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection
The past ten years have witnessed the rapid development of text-based intent detection, whose benchmark performances have already been taken to a remarkable level by deep learning techniques.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Decoupled Side Information Fusion for Sequential Recommendation
Motivated by this, we propose Decoupled Side Information Fusion for Sequential Recommendation (DIF-SR), which moves the side information from the input to the attention layer and decouples the attention calculation of various side information and item representation.
Joint Multiple Intent Detection and Slot Filling via Self-distillation
With the auxiliary knowledge provided by the MIL Intent Decoder, we set Final Slot Decoder as the teacher model that imparts knowledge back to Initial Slot Decoder to complete the loop.
Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension
To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the \textit{coarse-grained} representations of the source sequences, i. e., passage and question.
PIN: A Novel Parallel Interactive Network for Spoken Language Understanding
However, we noted that, so far, the efforts to obtain better performance by supporting bidirectional and explicit information exchange between ID and SF are not well studied. In addition, few studies attempt to capture the local context information to enhance the performance of SF.
Low-supervision urgency detection and transfer in short crisis messages
Humanitarian disasters have been on the rise in recent years due to the effects of climate change and socio-political situations such as the refugee crisis.
Deep Neural Network Based Hyperspectral Pixel Classification With Factorized Spectral-Spatial Feature Representation
Secondly, in order to utilize the local spatial correlation among pixels, we share the previous subnetwork as a spectral feature extractor for each pixel in a patch of image, after which the spectral features of all pixels in a patch are combined and feeded into the subsequent classification subnetwork.
