Search Results for author:
Found 23 papers, 9 papers with code
MuLan: Multimodal-LLM Agent for Progressive Multi-Object Diffusion
Moreover, MuLan adopts a vision-language model (VLM) to provide feedback to the image generated in each sub-task and control the diffusion model to re-generate the image if it violates the original prompt.
Advancing Attack-Resilient Scheduling of Integrated Energy Systems with Demand Response via Deep Reinforcement Learning
Simulation results demonstrate that our method is capable of adequately addressing the uncertainties resulting from RES and loads, mitigating the impact of cyber-attacks on the scheduling strategy, and ensuring a stable demand supply for various energy sources.
SeisT: A foundational deep learning model for earthquake monitoring tasks
The most significant improvements, in comparison to existing models, are observed in phase-P picking, phase-S picking, and magnitude estimation, with gains of 1. 7%, 9. 5%, and 8. 0%, respectively.
Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search
Graph-based CF methods improve personalization by modeling collaborative signal within the user click graph.
Emotional Talking Head Generation based on Memory-Sharing and Attention-Augmented Networks
Given an audio clip and a reference face image, the goal of the talking head generation is to generate a high-fidelity talking head video.
MAVD: The First Open Large-Scale Mandarin Audio-Visual Dataset with Depth Information
Audio-visual speech recognition (AVSR) gains increasing attention from researchers as an important part of human-computer interaction.
Rethinking the Role of Pre-ranking in Large-scale E-Commerce Searching System
As such, the metric of a pre-ranking model follows the ranking model using Area Under ROC (AUC) for offline evaluation.
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning
Federated learning (FL) attempts to train a global model by aggregating local models from distributed devices under the coordination of a central server.
Piggyback on Idle Ride-Sourcing Drivers for Integrated On-Demand and Flexible Intracity Parcel Delivery Services
This paper investigates the spatial pricing and fleet management strategies for an integrated platform that provides both ride-sourcing services and intracity parcel delivery services over a transportation network utilizing the idle time of ride-sourcing drivers.
MAKE: Vision-Language Pre-training based Product Retrieval in Taobao Search
Given a user query, the retrieval phase returns a subset of candidate products for the following ranking phase.
Multi-Objective Personalized Product Retrieval in Taobao Search
These problems promote us to further strengthen the capabilities of our EBR model in both relevance estimation and personalized retrieval.
TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving
The next-generation high-resolution automotive radar (4D radar) can provide additional elevation measurement and denser point clouds, which has great potential for 3D sensing in autonomous driving.
Modeling User Behavior with Graph Convolution for Personalized Product Search
Our approach can be seamlessly integrated with existing latent space based methods and be potentially applied in any product retrieval method that uses purchase history to model user preferences.
Adversarial Learning for Incentive Optimization in Mobile Payment Marketing
This bias deteriorates the performance of the response model and misleads the linear programming process, dramatically degrading the performance of the resulting allocation policy.
Research on Event Accumulator Settings for Event-Based SLAM
The experiment results show that our method can achieve better performance in most sequences compared with the state-of-the-art event frame based SLAM algorithm.
Embedding-based Product Retrieval in Taobao Search
We evaluate MGDSPR on Taobao Product Search with significant metrics gains observed in offline experiments and online A/B tests.
Multi-stage malaria parasite recognition by deep learning
The proposed method showed higher accuracy and effectiveness in publicly available microscopic images of multi-stage malaria parasites compared to a wide range of state-of-the-art approaches.
Learning Representations for Incomplete Time Series Clustering
Also, to reduce the error propagation from imputation to clustering, we introduce a discriminator to make the distribution of imputation values close to the true one and train CRLI in an alternating train- ing manner.
Towards Understanding and Improving Dropout in Game Theory
Experimental results on various DNNs and datasets have shown that the interaction loss can effectively improve the utility of dropout and boost the performance of DNNs.
Interpreting and Boosting Dropout from a Game-Theoretic View
This paper aims to understand and improve the utility of the dropout operation from the perspective of game-theoretic interactions.
Impact of Congestion Charge and Minimum Wage on TNCs: A Case Study for San Francisco
Both charges reduce the number of TNC trips, but this reduction is limited by the wage floor, and the number of TNC vehicles reduced is not significant.
Learning Representations for Time Series Clustering
When applying seq2seq to time series clustering, obtaining a representation that effectively represents the temporal dynamics of the sequence, multi-scale features, and good clustering properties remains a challenge.
DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark
Driver attention prediction is currently becoming the focus in safe driving research community, such as the DR(eye)VE project and newly emerged Berkeley DeepDrive Attention (BDD-A) database in critical situations.
