Results 91 to 100 of about 3,499,798 (281)

StackingNet: Collective Inference Across Independent AI Foundation Models

Advanced Science, EarlyView.
ABSTRACT Artificial intelligence (AI) built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black‐box foundation models is essential for trustworthy ...
Siyang Li, Chenhao Liu, Dongrui Wu, Zhigang Zeng, Lieyun Ding   +4 more
wiley   +1 more source

MixUp as Directional Adversarial Training

CoRR, 2019
In this work, we explain the working mechanism of MixUp in terms of adversarial training. We introduce a new class of adversarial training schemes, which we refer to as directional adversarial training, or DAT. In a nutshell, a DAT scheme perturbs a training example in the direction of another example but keeps its original label as the training target.
Guillaume P. Archambault, Yongyi Mao, Hongyu Guo, Richong Zhang   +3 more
openaire   +2 more sources

Adversarial Risk Análysis for Counterterrorism Modelling [PDF]

, 2013
Recent large scale terrorist attacks have raised interest in models for resource allocation against terrorist threats. The unifying theme in this area is the need to develop methods for the analysis of allocation decisions when risks stem from the ...
Ríos, Jesús, Ríos Insúa, David
core  

Latent Diffusion Process With Mechanistic Guidance For Designing Functionally Graded Metamaterials With Perfect Connectivity

Advanced Science, EarlyView.
A latent diffusion‐based framework is proposed for designing functionally graded metamaterials with perfect connectivity. By integrating vector‐quantized latent representations with mechanistic guidance, the framework enables accurate inverse design toward target elastic properties.
Jongbin Yu, Dosung Lee, Namjung Kim
wiley   +1 more source

Semantics-Preserving Adversarial Training

CoRR, 2020
Preprint.
Wonseok Lee 0001, Hanbit Lee, Sang-goo Lee   +2 more
openaire   +3 more sources

Effective and Robust Adversarial Training Against Data and Label Corruptions

Corruptions due to data perturbations and label noise are prevalent in the datasets from unreliable sources, which poses significant threats to model training.
Xu, XS, Huang, Z, Zhang, PF, Bai, G
core   +1 more source

SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics

Advanced Science, EarlyView.
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi, Ahmadreza Argha, Nona Farbehi, Ivan Bakhshayeshi, Thantrira Porntaveetus, Youqiong Ye, Nigel H. Lovell, Hamid Alinejad‐Rokny   +7 more
wiley   +1 more source

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, Nicholas Schaffer, Xiao Yang   +2 more
wiley   +1 more source

RazorNet: Adversarial Training and Noise Training on a Deep Neural Network Fooled by a Shallow Neural Network

Big Data and Cognitive Computing, 2019
In this work, we propose ShallowDeepNet, a novel system architecture that includes a shallow and a deep neural network. The shallow neural network has the duty of data preprocessing and generating adversarial samples. The deep neural network has the duty
Shayan Taheri, Milad Salem, Jiann-Shiun Yuan   +2 more
doaj   +1 more source

Molecular Atlas of Key Food Odorants Reveals Mixture‐Level Organization and Enables Generative Aroma Design

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, Huadong Xing, Antonella Di Pizio, Qinfei Ke, Xingran Kou, Dachuan Zhang   +5 more
wiley   +1 more source