Zobrazeno 1 - 10
of 31 979
pro vyhledávání: '"Teoh, A."'
Palmprint recognition has emerged as a prominent biometric authentication method, owing to its high discriminative power and user-friendly nature. This paper introduces a novel Cross-Chirality Palmprint Verification (CCPV) framework that challenges t
Externí odkaz:
http://arxiv.org/abs/2409.13056
Weak measurement enables the extraction of targeted information from a quantum system while minimizing decoherence due to measurement backaction. However, in many-body quantum systems backaction can have unexpected effects on wavefunction collapse. W
Externí odkaz:
http://arxiv.org/abs/2409.09878
Autor:
Teoh, Yi Hong, Melko, Roger G.
Autoregressive models are a class of generative model that probabilistically predict the next output of a sequence based on previous inputs. The autoregressive sequence is by definition one-dimensional (1D), which is natural for language tasks and he
Externí odkaz:
http://arxiv.org/abs/2408.15715
Autor:
Scanlon, John M., Teoh, Eric R., Kidd, David G., Kusano, Kristofer D., Bärgman, Jonas, Chi-Johnston, Geoffrey, Di Lillo, Luigi, Favaro, Francesca, Flannagan, Carol, Liers, Henrik, Lin, Bonnie, Lindman, Magdalena, McLaughlin, Shane, Perez, Miguel, Victor, Trent
The public, regulators, and domain experts alike seek to understand the effect of deployed SAE level 4 automated driving system (ADS) technologies on safety. The recent expansion of ADS technology deployments is paving the way for early stage safety
Externí odkaz:
http://arxiv.org/abs/2408.07758
Autor:
Gupta, Prannaya, Yau, Le Qi, Low, Hao Han, Lee, I-Shiang, Lim, Hugo Maximus, Teoh, Yu Xin, Koh, Jia Hng, Liew, Dar Win, Bhardwaj, Rishabh, Bhardwaj, Rajat, Poria, Soujanya
WalledEval is a comprehensive AI safety testing toolkit designed to evaluate large language models (LLMs). It accommodates a diverse range of models, including both open-weight and API-based ones, and features over 35 safety benchmarks covering areas
Externí odkaz:
http://arxiv.org/abs/2408.03837
Recently, a lot of effort has been devoted towards designing erasure qubits in which dominant physical noise excites leakage states whose population can be detected and returned to the qubit subspace. Interest in these erasure qubits has been driven
Externí odkaz:
http://arxiv.org/abs/2408.00842
Autor:
Dong, Xingbo, Zhang, Hui, Lai, Yen Lung, Jin, Zhe, Huang, Junduan, Kang, Wenxiong, Teoh, Andrew Beng Jin
Deriving a unique cryptographic key from biometric measurements is a challenging task due to the existing noise gap between the biometric measurements and error correction coding. Additionally, privacy and security concerns arise as biometric measure
Externí odkaz:
http://arxiv.org/abs/2407.14804
This paper proposes a CNN classification network based on Bagging and stacking ensemble learning methods for breast cancer classification. The model was trained and tested on the public dataset of DDSM. The model is capable of fast and accurate class
Externí odkaz:
http://arxiv.org/abs/2407.10574
Generalising vision-based manipulation policies to novel environments remains a challenging area with limited exploration. Current practices involve collecting data in one location, training imitation learning or reinforcement learning policies with
Externí odkaz:
http://arxiv.org/abs/2407.07868
Autor:
Jung, Yoon Gyo, Park, Jaewoo, Dong, Xingbo, Park, Hojin, Teoh, Andrew Beng Jin, Camps, Octavia
Understanding the vulnerability of face recognition systems to malicious attacks is of critical importance. Previous works have focused on reconstructing face images that can penetrate a targeted verification system. Even in the white-box scenario, h
Externí odkaz:
http://arxiv.org/abs/2407.02403