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Found 8 papers, 0 papers with code
Picking watermarks from noise (PWFN): an improved robust watermarking model against intensive distortions
To improve the robustness of the decoder against stronger noise, this paper proposes to introduce a denoise module between the noise layer and the decoder.
DBDH: A Dual-Branch Dual-Head Neural Network for Invisible Embedded Regions Localization
In addition, we introduce an extra segmentation head to segment the mask of the embedding region during training.
The Frontier of Data Erasure: Machine Unlearning for Large Language Models
Large Language Models (LLMs) are foundational to AI advancements, facilitating applications like predictive text generation.
RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RL
To address the challenge, we present RobustAnalog, a robust circuit design framework that involves the variation information in the optimization process.
DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks
The key contributions of this paper are a novel sample-efficient two-stage deep learning optimization framework leveraging RL actor-critic algorithms, and a recipe to extend it on large industrial circuits using critical device identification.
RoDesigner: Variation-Aware Optimization for Robust Analog Design with Multi-Task RL
Specifically, circuit optimizations under different variations are considered as a set of tasks.
GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning
Our transferable optimization method makes transistor sizing and design porting more effective and efficient.
