Search Results for author:
Found 34 papers, 13 papers with code
Learning to Rank in the Age of Muppets: Effectiveness–Efficiency Tradeoffs in Multi-Stage Ranking
It is well known that rerankers built on pretrained transformer models such as BERT have dramatically improved retrieval effectiveness in many tasks.
Multimodal large language model for wheat breeding: a new exploration of smart breeding
UAV remote sensing technology has become a key technology in crop breeding, which can achieve high-throughput and non-destructive collection of crop phenotyping data.
Towards Fair and Rigorous Evaluations: Hyperparameter Optimization for Top-N Recommendation Task with Implicit Feedback
This study contributes to algorithmic research in recommender systems based on hyperparameter optimization, providing a fair basis for comparison.
PanicleNeRF: low-cost, high-precision in-field phenotypingof rice panicles with smartphone
The rice panicle traits significantly influence grain yield, making them a primary target for rice phenotyping studies.
Toward Automatic Group Membership Annotation for Group Fairness Evaluation
With the increasing research attention on fairness in information retrieval systems, more and more fairness-aware algorithms have been proposed to ensure fairness for a sustainable and healthy retrieval ecosystem.
Learn to be Fair without Labels: a Distribution-based Learning Framework for Fair Ranking
Learning-based models are more flexible and achieve better performance than traditional methods.
Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommender Systems
To fill this gap, we propose Behavior Alignment, a new evaluation metric to measure how well the recommendation strategies made by a LLM-based CRS are consistent with human recommenders'.
A Simple Yet Effective Approach for Diversified Session-Based Recommendation
It consists of two novel designs: a model-agnostic diversity-oriented loss function, and a non-invasive category-aware attention mechanism.
HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation
In this paper, we propose a novel hybrid pseudo-labeling framework for unsupervised event-based semantic segmentation, HPL-ESS, to alleviate the influence of noisy pseudo labels.
Dynamic In-Context Learning from Nearest Neighbors for Bundle Generation
To enhance reliability and mitigate the hallucination issue, we develop (1) a self-correction strategy to foster mutual improvement in both tasks without supervision signals; and (2) an auto-feedback mechanism to recurrently offer dynamic supervision based on the distinct mistakes made by ChatGPT on various neighbor sessions.
CrossBind: Collaborative Cross-Modal Identification of Protein Nucleic-Acid-Binding Residues
Accurate identification of protein nucleic-acid-binding residues poses a significant challenge with important implications for various biological processes and drug design.
Session-Based Recommendation by Exploiting Substitutable and Complementary Relationships from Multi-behavior Data
Session-based recommendation (SR) aims to dynamically recommend items to a user based on a sequence of the most recent user-item interactions.
X4D-SceneFormer: Enhanced Scene Understanding on 4D Point Cloud Videos through Cross-modal Knowledge Transfer
The field of 4D point cloud understanding is rapidly developing with the goal of analyzing dynamic 3D point cloud sequences.
Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approach
To the best of our knowledge, we are the first to achieve differential privacy in sequential recommendation with dependent interactions.
CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning
To dually improve the performance of temporal states evolution and incremental recommendation, we design a Pseudo-Multi-Task Learning (PMTL) paradigm by stacking the incremental single-target recommendations into one multi-target task for joint optimization.
ZeQR: Zero-shot Query Reformulation for Conversational Search
Nevertheless, existing zero-shot methods face three primary limitations: they are not universally applicable to all retrievers, their effectiveness lacks sufficient explainability, and they struggle to resolve common conversational ambiguities caused by omission.
Mixed-initiative Query Rewriting in Conversational Passage Retrieval
In this work, we aim to reproduce multi-stage retrieval pipelines and explore one of the potential benefits of involving mixed-initiative interaction in conversational passage retrieval scenarios: reformulating raw queries.
Contactless hand tremor amplitude measurement using smartphones: development and pilot evaluation
Objective: to develop a proof of principle method to measure hand tremor amplitude from smartphone videos.
Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task.
Decentralized Matrix Factorization with Heterogeneous Differential Privacy
Existing differentially private matrix factorization methods either assume the recommender is trusted, or can only provide a uniform level of privacy protection for all users and items with untrusted recommender.
Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring
In this paper, we propose a novel client-side FL watermarking scheme to tackle the copyright protection issue in secure FL with HE.
Understanding Diversity in Session-Based Recommendation
Besides the "trade-off" relationship, they might be positively correlated with each other, that is, having a same-trend (win-win or lose-lose) relationship, which varies across different methods and datasets.
DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation
Recently, one critical issue looms large in the field of recommender systems -- there are no effective benchmarks for rigorous evaluation -- which consequently leads to unreproducible evaluation and unfair comparison.
Causality and Correlation Graph Modeling for Effective and Explainable Session-based Recommendation
Considering the varied interpretations and characteristics of causality and correlation relationship between items, in this study, we propose a novel method denoted as CGSR by jointly modeling causality and correlation relationship between items.
MuSCLe: A Multi-Strategy Contrastive Learning Framework for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation (WSSS) has gained significant popularity since it relies only on weak labels such as image level annotations rather than pixel level annotations required by supervised semantic segmentation (SSS) methods.
Image Disentanglement Autoencoder for Steganography Without Embedding
Conventional steganography approaches embed a secret message into a carrier for concealed communication but are prone to attack by recent advanced steganalysis tools.
Analysis of an adaptive lead weighted ResNet for multiclass classification of 12-lead ECGs
Method: We proposed a squeeze and excite ResNet to automatically learn deep features from 12-lead ECGs, in order to identify 24 cardiac conditions.
Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset
We present Covidex, a search engine that exploits the latest neural ranking models to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI.
Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer
Photorealistic style transfer is the task of transferring the artistic style of an image onto a content target, producing a result that is plausibly taken with a camera.
Research Commentary on Recommendations with Side Information: A Survey and Research Directions
This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information.
Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
In the field of sequential recommendation, deep learning (DL)-based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based ones.
Creatism: A deep-learning photographer capable of creating professional work
In our system, we break down aesthetics into multiple aspects, each can be learned individually from a shared dataset of professional examples.
