Search Results for author:
Found 18 papers, 7 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.
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.
Contrastive Learning
Weakly supervised Semantic Segmentation
+1
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.
