Search Results for author:
Found 19 papers, 7 papers with code
A Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao
In light of this, we propose a novel end-to-end generative re-ranking model named Sequential Ordered Regression Transformer-Generator (SORT-Gen) for the less-studied list-level multi-objective optimization problem.
GraphATC: advancing multilevel and multi-label anatomical therapeutic chemical classification via atom-level graph learning
The accurate categorization of compounds within the anatomical therapeutic chemical (ATC) system is fundamental for drug development and fundamental research.
Ranked #1 on
Drug ATC Classification
on ATC-SMILES
PIP: Perturbation-based Iterative Pruning for Large Language Models
The rapid increase in the parameter counts of Large Language Models (LLMs), reaching billions or even trillions, presents significant challenges for their practical deployment, particularly in resource-constrained environments.
HAIR: Hypernetworks-based All-in-One Image Restoration
To alleviate this issue, we propose HAIR, a Hypernetworks-based All-in-One Image Restoration plug-and-play method that generates parameters based on the input image and thus makes the model to adapt to specific degradation dynamically.
Ranked #3 on
Blind All-in-One Image Restoration
on 3-Degradations
SparseSSP: 3D Subcellular Structure Prediction from Sparse-View Transmitted Light Images
Traditional fluorescence staining is phototoxic to live cells, slow, and expensive; thus, the subcellular structure prediction (SSP) from transmitted light (TL) images is emerging as a label-free, faster, low-cost alternative.
ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation
Second, we propose an adaptive knowledge distillation method for continually transferring the representations from a model that is well-trained on the entire space dataset.
InfRS: Incremental Few-Shot Object Detection in Remote Sensing Images
In this paper, we explore the intricate task of incremental few-shot object detection in remote sensing images.
Few-shot Oriented Object Detection with Memorable Contrastive Learning in Remote Sensing Images
Few-shot object detection (FSOD) has garnered significant research attention in the field of remote sensing due to its ability to reduce the dependency on large amounts of annotated data.
Temporal Embeddings: Scalable Self-Supervised Temporal Representation Learning from Spatiotemporal Data for Multimodal Computer Vision
There exists a correlation between geospatial activity temporal patterns and type of land use.
An Empirical Analysis on Financial Markets: Insights from the Application of Statistical Physics
In this study, we introduce a physical model inspired by statistical physics for predicting price volatility and expected returns by leveraging Level 3 order book data.
PaniniQA: Enhancing Patient Education Through Interactive Question Answering
Patient portal allows discharged patients to access their personalized discharge instructions in electronic health records (EHRs).
Self-Supervised Temporal Analysis of Spatiotemporal Data
There exists a correlation between geospatial activity temporal patterns and type of land use.
Continual Graph Convolutional Network for Text Classification
Graph convolutional network (GCN) has been successfully applied to capture global non-consecutive and long-distance semantic information for text classification.
Context Variance Evaluation of Pretrained Language Models for Prompt-based Biomedical Knowledge Probing
Different from the previous known-unknown evaluation criteria, we propose the concept of "Misunderstand" in LAMA for the first time.
Deep Intention-Aware Network for Click-Through Rate Prediction
We identify that the key to TIRA is to extract customers' personalized entering intention and weigh the impact of triggers based on this intention.
Identifying the kind behind SMILES—anatomical therapeutic chemical classification using structure-only representations
The model is with better explainability in the sense that it is consists of a straightforward tokenization that extracts and embeds statistically and physicochemically meaningful tokens, and a deep network backed by a set of pyramid kernels to capture multi-resolution chemical structural characteristics.
Ranked #2 on
Drug ATC Classification
on ATC-SMILES
Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context
In order to make LMs as KBs more in line with the actual application scenarios of the biomedical domain, we specifically add EHR notes as context to the prompt to improve the low bound in the biomedical domain.
GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction
Specifically, we establish a heterogeneous graph that contains physical and semantic linkages to guide the feature transfer process from warmed-up video to cold-start videos.
The new methods for equity fund selection and optimal portfolio construction
We relook at the classic equity fund selection and portfolio construction problems from a new perspective and propose an easy-to-implement framework to tackle the problem in practical investment.
