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Found 13 papers, 6 papers with code
LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns.
RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates
We propose RoCoFT, a parameter-efficient fine-tuning method for large-scale language models (LMs) based on updating only a few rows and columns of the weight matrices in transformers.
Parameter-Efficient Fine-Tuning of Large Language Models using Semantic Knowledge Tuning
This method involves using a fixed LLM to understand and process the semantic content of the prompt through zero-shot capabilities.
FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation
Drug-Target Interaction (DTI) prediction is vital for drug discovery, yet challenges persist in achieving model interpretability and optimizing performance.
AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification
In this study, we introduce an interpretable graph-based deep learning prediction model, AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism to address the problem of drug–target interaction prediction.
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Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model
Our topic model identifies topics' recurrence over time with a varying time resolution.
Facial Expression Phoenix (FePh): An Annotated Sequenced Dataset for Facial and Emotion-Specified Expressions in Sign Language
Facial expressions are important parts of both gesture and sign language recognition systems.
A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detection
Hence, the challenge to develop fraud detection that are accurate and efficient is substantially intensified and, as a consequence, credit card fraud detection has lately become a very active area of research.
DeepFork: Supervised Prediction of Information Diffusion in GitHub
To understand human influence, information spread and evolution of transmitted information among assorted users in GitHub, we developed a deep neural network model: DeepFork, a supervised machine learning based approach that aims to predict information diffusion in complex social networks; considering node as well as topological features.
Appointment scheduling model in healthcare using clustering algorithms
Determining the priority of outpatients and allocating the capacity based on the priority classes are important concepts that have to be considered in the scheduling of outpatients.
Multi-Task Learning Using Neighborhood Kernels
As shown by our empirical results, our algorithm consistently outperforms the traditional kernel learning algorithms such as uniform combination solution, convex combinations of base kernels as well as some kernel alignment-based models, which have been proven to give promising results in the past.
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning
We show a Talagrand-type concentration inequality for Multi-Task Learning (MTL), using which we establish sharp excess risk bounds for MTL in terms of distribution- and data-dependent versions of the Local Rademacher Complexity (LRC).
Multi-Task Learning with Group-Specific Feature Space Sharing
When faced with learning a set of inter-related tasks from a limited amount of usable data, learning each task independently may lead to poor generalization performance.
