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pro vyhledávání: '"BIBI, A."'
An isometric immersion $X: \Sigma^n \longrightarrow \mathbb{E}^{n+1}$ is biharmonic if $\Delta^2 X = 0$, i.e. if $\Delta H =0$, where $\Delta$ and $H$ are the metric Laplacian and the mean curvature vector field of $\Sigma^n$ respectively. More gener
Externí odkaz:
http://arxiv.org/abs/2410.13546
The use of intelligent automation is growing significantly in the automotive industry, as it assists drivers and fleet management companies, thus increasing their productivity. Dash cams are now been used for this purpose which enables the instant id
Externí odkaz:
http://arxiv.org/abs/2408.15809
Fine-tuning large language models (LLMs) on human preferences, typically through reinforcement learning from human feedback (RLHF), has proven successful in enhancing their capabilities. However, ensuring the safety of LLMs during the fine-tuning rem
Externí odkaz:
http://arxiv.org/abs/2408.15313
Autor:
Hirokawa, Soichi, Lee, Heun Jin, Banks, Rachel A, Duarte, Ana I, Najma, Bibi, Thomson, Matt, Phillips, Rob
Motor-driven cytoskeletal remodeling in cellular systems can often be accompanied by a diffusive-like effect at local scales, but distinguishing the contributions of the ordering process, such as active contraction of a network, from this active diff
Externí odkaz:
http://arxiv.org/abs/2408.11216
Autor:
Alhamoud, Kumail, Ghunaim, Yasir, Alfarra, Motasem, Hartvigsen, Thomas, Torr, Philip, Ghanem, Bernard, Bibi, Adel, Ghassemi, Marzyeh
For medical imaging AI models to be clinically impactful, they must generalize. However, this goal is hindered by (i) diverse types of distribution shifts, such as temporal, demographic, and label shifts, and (ii) limited diversity in datasets that a
Externí odkaz:
http://arxiv.org/abs/2407.08822
Autor:
Hammoud, Hasan Abed Al Kader, Michieli, Umberto, Pizzati, Fabio, Torr, Philip, Bibi, Adel, Ghanem, Bernard, Ozay, Mete
Merging Large Language Models (LLMs) is a cost-effective technique for combining multiple expert LLMs into a single versatile model, retaining the expertise of the original ones. However, current approaches often overlook the importance of safety ali
Externí odkaz:
http://arxiv.org/abs/2406.14563
Autor:
Dehghan, Mohammad, Alomrani, Mohammad Ali, Bagga, Sunyam, Alfonso-Hermelo, David, Bibi, Khalil, Ghaddar, Abbas, Zhang, Yingxue, Li, Xiaoguang, Hao, Jianye, Liu, Qun, Lin, Jimmy, Chen, Boxing, Parthasarathi, Prasanna, Biparva, Mahdi, Rezagholizadeh, Mehdi
The emerging citation-based QA systems are gaining more attention especially in generative AI search applications. The importance of extracted knowledge provided to these systems is vital from both accuracy (completeness of information) and efficienc
Externí odkaz:
http://arxiv.org/abs/2406.10393
Fine-tuning large language models on small, high-quality datasets can enhance their performance on specific downstream tasks. Recent research shows that fine-tuning on benign, instruction-following data can inadvertently undo the safety alignment pro
Externí odkaz:
http://arxiv.org/abs/2406.10288
Autor:
Yang, Yibo, Li, Xiaojie, Alfarra, Motasem, Hammoud, Hasan, Bibi, Adel, Torr, Philip, Ghanem, Bernard
Relieving the reliance of neural network training on a global back-propagation (BP) has emerged as a notable research topic due to the biological implausibility and huge memory consumption caused by BP. Among the existing solutions, local learning op
Externí odkaz:
http://arxiv.org/abs/2406.05222
Zero-shot and in-context learning enable solving tasks without model fine-tuning, making them essential for developing generative model solutions. Therefore, it is crucial to understand whether a pretrained model can be prompted to approximate any fu
Externí odkaz:
http://arxiv.org/abs/2406.01424