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Found 23 papers, 4 papers with code
Don’t Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention Pooling
Recent pre-trained language models (PLMs) achieved great success on many natural language processing tasks through learning linguistic features and contextualized sentence representation.
Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Diversification-Enhancing Contrastive Learning
Hence, the tricky point in stock recommendation is that recommendations should give good investment performance but also should not ignore individual preferences.
Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users
It is essential to provide recommendations that are both personalized and diverse, rather than solely relying on achieving high rank-based performance, such as Click-through Rate, Recall, etc.
Temporal Graph Networks for Graph Anomaly Detection in Financial Networks
This paper explores the utilization of Temporal Graph Networks (TGN) for financial anomaly detection, a pressing need in the era of fintech and digitized financial transactions.
A Recommender System for NFT Collectibles with Item Feature
Recommender systems have been actively studied and applied in various domains to deal with information overload.
A Temporal Graph Network Framework for Dynamic Recommendation
Recommender systems, crucial for user engagement on platforms like e-commerce and streaming services, often lag behind users' evolving preferences due to static data reliance.
ERD: A Framework for Improving LLM Reasoning for Cognitive Distortion Classification
Improving the accessibility of psychotherapy with the aid of Large Language Models (LLMs) is garnering a significant attention in recent years.
Federated Learning for Estimating Heterogeneous Treatment Effects
To overcome this obstacle, in this work, we propose a novel framework for collaborative learning of HTE estimators across institutions via Federated Learning.
Harmonic Mobile Manipulation
Recent advancements in robotics have enabled robots to navigate complex scenes or manipulate diverse objects independently.
Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World
Reinforcement learning (RL) with dense rewards and imitation learning (IL) with human-generated trajectories are the most widely used approaches for training modern embodied agents.
PROPANE: Prompt design as an inverse problem
Carefully-designed prompts are key to inducing desired behavior in Large Language Models (LLMs).
NFTs to MARS: Multi-Attention Recommender System for NFTs
Recommender systems have become essential tools for enhancing user experiences across various domains.
CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models
Large pre-trained language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data.
Don't Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention Pooling
Recent pre-trained language models (PLMs) achieved great success on many natural language processing tasks through learning linguistic features and contextualized sentence representation.
Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine
Causal structure learning refers to a process of identifying causal structures from observational data, and it can have multiple applications in biomedicine and health care.
Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark
We also expect to provide the feasibility of HTE for personalized drug effectiveness.
Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules
The RGCN model achieved an overall accuracy of 0. 872, an AUROC of 0. 919 and an AUPRC of 0. 838 for the testing dataset with the drug-protein interactions and the Mordred descriptors as the input.
Texture Transform Attention for Realistic Image Inpainting
Texture Transform Attention is used to create a new reassembled texture map using fine textures and coarse semantics that can efficiently transfer texture information as a result.
Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence
In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment.
Combining Representation Learning with Tensor Factorization for Risk Factor Analysis - an application to Epilepsy and Alzheimer's disease
Existing studies consider Alzheimer's disease (AD) a comorbidity of epilepsy, but also recognize epilepsy to occur more frequently in patients with AD than those without.
Discriminative Sleep Patterns of Alzheimer's Disease via Tensor Factorization
Sleep change is commonly reported in Alzheimer's disease (AD) patients and their brain wave studies show decrease in dreaming and non-dreaming stages.
From Brain Imaging to Graph Analysis: a study on ADNI's patient cohort
In this paper, we studied the association between the change of structural brain volumes to the potential development of Alzheimer's disease (AD).
Federated Tensor Factorization for Computational Phenotyping
In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data.
