Results 131 to 140 of about 3,240,231 (313)
Robust Adversarial Reinforcement Learning
CoRR, 2017 Deep neural networks coupled with fast simulation and improved computation have led to recent successes in the field of reinforcement learning (RL). However, most current RL-based approaches fail to generalize since: (a) the gap between simulation and real world is so large that policy-learning approaches fail to transfer; (b) even if policy learning ...
Lerrel Pinto +3 more
openaire +3 more sources
Neuromorphic Devices and Computing for Sensing, Memory, and Control
Advanced Science, EarlyView.This review introduces neuromorphic devices made from diverse materials. These devices mimic neuronal functions and architectures and, when integrated with artificial or biological computing, can form closed loops with neurons for pressure, optical, acoustic, and biochemical sensing and modulation.
Zhengguang Zhu +2 more
wiley +1 more source
Alexandria Engineering JournalExtractive Question Answering (EQA) models aim to locate accurate answers from passages given a question but are highly susceptible to adversarial attacks.
Gang Huang, Lu Zhang, Hailun Wang
doaj +1 more source
Structural Divergence Between the Moltbook AI‐Agent Network and Human Social Networks
Advanced Science, EarlyView.Analysis of the Moltbook AI‐agent network reveals a striking combination of familiar global scaling and distinct internal organization. Attention is highly concentrated, reciprocity is limited, connected triads are suppressed, and communities are strongly modular.
Wenpin Hou, Zhicheng Ji
wiley +1 more source
Robustness Tokens: Towards Adversarial Robustness of Transformers
Recently, large pre-trained foundation models have become widely adopted by machine learning practitioners for a multitude of tasks. Given that such models are publicly available, relying on their use as backbone models for downstream tasks might result in high vulnerability to adversarial attacks crafted with the same public model.
Brian Pulfer +2 more
openaire +2 more sources
Regularization for Adversarial Robust Learning
CoRR51 pages, 5 ...
Jie Wang, Rui Gao, Yao Xie
openaire +3 more sources
Adversarial scheduling analysis of Game-Theoretic Models of Norm Diffusion. [PDF]
In (Istrate et al. SODA 2001) we advocated the investigation of robustness of results in the theory of learning in games under adversarial scheduling models.
Istrate, Gabriel +2 more
core
Advanced Science, EarlyView.KFO‐Atlas reveals how real‐world aromas are organized as structured mixtures rather than individual molecules. Building on these principles, KFO‐Gen, a generative AI framework, designs perceptually valid aroma formulations and reconstructs meat‐like aromas exclusively from plant‐derived odorants, providing a foundation for mixture‐level studies and AI ...
Jingzhi Zhang +5 more
wiley +1 more source
IEEE AccessDeep Neural Networks (DNNs) have achieved tremendous success in various computer vision tasks but remain highly vulnerable to adversarial examples. To address this limitation, we investigate the inherent robustness of hand-crafted features and validate ...
Shuohan Xue +2 more
doaj +1 more source
Advanced Science, EarlyView.Recent advances in metasurface‐enabled low‐observable technologies are reviewed from the perspective of cross‐scale material–structure synergy. Electromagnetic, thermal, optical, and acoustic stealth are highlighted together with dynamic tuning, programmable coding, data‐driven inverse design, artificial intelligence, multispectral compatibility, and ...
Shuhao Wang +5 more
wiley +1 more source