Results 131 to 140 of about 107,198 (316)
Adversarial Learning in Accelerometer Based Transportation and Locomotion Mode Recognition
, 2022 This chapter demonstrates how adversarial learning can be used in the mobile computing domain. Specifically, we address the problem of improving the recognition of human activities from smartphone sensors, when limited training data is available ...
Lukas Kornelius Gunthermann (7523153) +9 more
core
Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
Advanced Intelligent Discovery, EarlyView.Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang +4 more
wiley +1 more source
On the capacity of memoryless adversary [PDF]
2014 IEEE International Symposium on Information Theory, 2014 In this paper, we study a model of communication under adversarial noise. In this model, the adversary makes online decisions on whether to corrupt a transmitted bit based on only the value of that bit. Like the usual binary symmetric channel of information theory or the fully adversarial channel of combinatorial coding theory, the adversary can, with ...
openaire +2 more sources
Advanced Intelligent Discovery, EarlyView.This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee +3 more
wiley +1 more source
Manifold-driven decomposition for adversarial robustness
Frontiers in Computer ScienceThe adversarial risk of a machine learning model has been widely studied. Most previous studies assume that the data lie in the whole ambient space. We propose to take a new angle and take the manifold assumption into consideration.
Wenjia Zhang +6 more
doaj +1 more source
Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition
, 2020 Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system’s performance over multiple days is challenging, due to the transient nature of the ...
Côté-Allard, Ulysse +8 more
core +1 more source
How do humans perceive adversarial text? A reality check on the validity and naturalness of word-based adversarial attacks [PDF]
, 2023 peer reviewedNatural Language Processing (NLP) models based on Machine Learning (ML) are susceptible to adversarial attacks -- malicious algorithms that imperceptibly modify input text to force models into making incorrect predictions.
GHAMIZI, Salah +2 more
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Advanced Intelligent Discovery, EarlyView.Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto +3 more
wiley +1 more source
Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art
Advanced Intelligent Discovery, EarlyView.Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser +6 more
wiley +1 more source
Adversarial scheduling analysis of Game-Theoretic Models of Norm Diffusion.
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
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