Search Results for author:
Found 7 papers, 1 papers with code
LoyalDiffusion: A Diffusion Model Guarding Against Data Replication
In this paper, we address this gap by examining and mitigating the impact of the model structure, specifically the skip connections in the diffusion model's U-Net model.
Quantitative causality, causality-guided scientific discovery, and causal machine learning
It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization.
GameGPT: Multi-agent Collaborative Framework for Game Development
The large language model (LLM) based agents have demonstrated their capacity to automate and expedite software development processes.
Mitigate Replication and Copying in Diffusion Models with Generalized Caption and Dual Fusion Enhancement
While diffusion models demonstrate a remarkable capability for generating high-quality images, their tendency to `replicate' training data raises privacy concerns.
Island-based Random Dynamic Voltage Scaling vs ML-Enhanced Power Side-Channel Attacks
In this paper, we describe and analyze an island-based random dynamic voltage scaling (iRDVS) approach to thwart power side-channel attacks.
Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference
Large number of ReLU and MAC operations of Deep neural networks make them ill-suited for latency and compute-efficient private inference.
SAL-ViT: Towards Latency Efficient Private Inference on ViT using Selective Attention Search with a Learnable Softmax Approximation
Then, given our observation that external attention (EA) presents lower PI latency than widely-adopted self-attention (SA) at the cost of accuracy, we present a selective attention search (SAS) method to integrate the strength of EA and SA.
