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Found 9 papers, 3 papers with code
AV-GAN: Attention-Based Varifocal Generative Adversarial Network for Uneven Medical Image Translation
In this paper, we propose the Attention-Based Varifocal Generative Adversarial Network (AV-GAN), which solves multiple problems in pathologic image translation tasks, such as uneven translation difficulty in different regions, mutual interference of multiple resolution information, and nuclear deformation.
RT-LM: Uncertainty-Aware Resource Management for Real-Time Inference of Language Models
Recent advancements in language models (LMs) have gained substantial attentions on their capability to generate human-like responses.
RED: A Systematic Real-Time Scheduling Approach for Robotic Environmental Dynamics
This process ensures the scheduling framework's compatibility with MIMONet and maximizes efficiency.
R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics
These components collaboratively tackle the trade-offs in on-device DRL training, improving timing and algorithm performance while minimizing the risk of out-of-memory (OOM) errors.
PIMbot: Policy and Incentive Manipulation for Multi-Robot Reinforcement Learning in Social Dilemmas
Our work introduces a new angle for manipulation in recent multi-agent RL social dilemmas that utilize a unique reward function for incentivization.
MIMONet: Multi-Input Multi-Output On-Device Deep Learning
In this paper, we propose MIMONet, a novel on-device multi-input multi-output (MIMO) DNN framework that achieves high accuracy and on-device efficiency in terms of critical performance metrics such as latency, energy, and memory usage.
Dynamic Transformers Provide a False Sense of Efficiency
Despite much success in natural language processing (NLP), pre-trained language models typically lead to a high computational cost during inference.
White-Box Multi-Objective Adversarial Attack on Dialogue Generation
Such language models are, however, vulnerable to various adversarial samples as studied in traditional tasks such as text classification, which inspires our curiosity about their robustness in DG systems.
Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition
A hard challenge in developing practical face recognition (FR) attacks is due to the black-box nature of the target FR model, i. e., inaccessible gradient and parameter information to attackers.
