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Realtime environments change even as agents perform action inference and learning, thus requiring high interaction frequencies to effectively minimize regret. However, recent advances in machine learning involve larger neural networks with longer inf

Externí odkaz: http://arxiv.org/abs/2412.14355

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Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs

Autor: Thakkar, Megh, More, Yash, Fournier, Quentin, Riemer, Matthew, Chen, Pin-Yu, Zouaq, Amal, Das, Payel, Chandar, Sarath

There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruction-tuned counterparts. However, these expert models often experience a loss in their safety abilities in the

Externí odkaz: http://arxiv.org/abs/2411.06824

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Rician Denoising Diffusion Probabilistic Models For Sodium Breast MRI Enhancement

Autor: Yuan, Shuaiyu, Whitmarsh, Tristan, Kessler, Dimitri A, Arponen, Otso, McLean, Mary A, Baxter, Gabrielle, Riemer, Frank, Kennerley, Aneurin J, Brackenbury, William J, Gilbert, Fiona J, Kaggie, Joshua D

Sodium MRI is an imaging technique used to visualize and quantify sodium concentrations in vivo, playing a role in many biological processes and potentially aiding in breast cancer characterization. Sodium MRI, however, suffers from inherently low si

Externí odkaz: http://arxiv.org/abs/2410.11511

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