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Found 11 papers, 5 papers with code
Membership Inference on Text-to-Image Diffusion Models via Conditional Likelihood Discrepancy
Text-to-image diffusion models have achieved tremendous success in the field of controllable image generation, while also coming along with issues of privacy leakage and data copyrights.
Optimal Matrix Sketching over Sliding Windows
Matrix sketching, aimed at approximating a matrix $\boldsymbol{A} \in \mathbb{R}^{N\times d}$ consisting of vector streams of length $N$ with a smaller sketching matrix $\boldsymbol{B} \in \mathbb{R}^{\ell\times d}, \ell \ll N$, has garnered increasing attention in fields such as large-scale data analytics and machine learning.
Mozart's Touch: A Lightweight Multi-modal Music Generation Framework Based on Pre-Trained Large Models
In recent years, AI-Generated Content (AIGC) has witnessed rapid advancements, facilitating the generation of music, images, and other forms of artistic expression across various industries.
RecDiffusion: Rectangling for Image Stitching with Diffusion Models
Image stitching from different captures often results in non-rectangular boundaries, which is often considered unappealing.
POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems
We also present a novel approach to propagate TMs more efficiently and precisely across ReLU activation functions.
PathSAGE: Spatial Graph Attention Neural Networks With Random Path Sampling
To address these problems, we propose a model called PathSAGE, which can learn high-order topological information and improve the model's performance by expanding the receptive field.
Integrating Dependency Tree Into Self-attention for Sentence Representation
Recent progress on parse tree encoder for sentence representation learning is notable.
Towards Generalized Models for Task-oriented Dialogue Modeling on Spoken Conversations
Building robust and general dialogue models for spoken conversations is challenging due to the gap in distributions of spoken and written data.
POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems
We present POLAR, a polynomial arithmetic-based framework for efficient bounded-time reachability analysis of neural-network controlled systems (NNCSs).
Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising
To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation.
Personalizing a Dialogue System with Transfer Reinforcement Learning
One way to solve this problem is to consider a collection of multiple users' data as a source domain and an individual user's data as a target domain, and to perform a transfer learning from the source to the target domain.
