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Found 9 papers, 3 papers with code
Reliable Model Watermarking: Defending Against Theft without Compromising on Evasion
With the rise of Machine Learning as a Service (MLaaS) platforms, safeguarding the intellectual property of deep learning models is becoming paramount.
SyncTalk: The Devil is in the Synchronization for Talking Head Synthesis
A lifelike talking head requires synchronized coordination of subject identity, lip movements, facial expressions, and head poses.
AllTogether: Investigating the Efficacy of Spliced Prompt for Web Navigation using Large Language Models
Large Language Models (LLMs) have emerged as promising agents for web navigation tasks, interpreting objectives and interacting with web pages.
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.
Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedule
As a modern ensemble technique, Deep Forest (DF) employs a cascading structure to construct deep models, providing stronger representational power compared to traditional decision forests.
New general dependence measures: construction, estimation and application to high-frequency stock returns
To show practical utility, we use these measures on high-frequency stock return data around market distress events such as the 2010 Flash Crash and during the GFC.
PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs
In this paper, we develop a new method to automatically convert 2D line drawings from three orthographic views into 3D CAD models.
Meta-DM: Applications of Diffusion Models on Few-Shot Learning
In the field of few-shot learning (FSL), extensive research has focused on improving network structures and training strategies.
Few-Shot Learning
Unsupervised Few-Shot Image Classification
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.
