stp2y

arXiv:2406.01733v1 Announce Type: new Abstract: Diffusion Transformers have recently demonstrated unprecedented generative capabilities for various tasks. The encouraging results, however, come with the cost of slow inference, since each denoising step requires inference on a transformer model with a large scale of parameters. In this study, we make an interesting and somehow surprising observation: the computation of a large proportion of layers in the diffusion transformer, through introducing a caching mechanism, can be readily removed even without updating the model parameters. In the case of U-ViT-H/2, for example, we may remove up to 93.68% of the computation in the cache…

arXiv:2406.01815v1 Announce Type: new Abstract: Automated cell segmentation has become increasingly crucial for disease diagnosis and drug discovery, as manual delineation is excessively laborious and subjective. To address this issue with limited manual annotation, researchers have developed semi/unsupervised segmentation approaches. Among these approaches, the Deep Gaussian mixture model plays a vital role due to its capacity to facilitate complex data distributions. However, these models assume that the data follows symmetric normal distributions, which is inapplicable for data that is asymmetrically distributed. These models also obstacles weak generalization capacity and are sensitive to outliers. To address these issues, this paper presents…

arXiv:2406.01860v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed in real-world settings, understanding the knowledge they implicitly use when making decisions is critical. One way to capture this knowledge is in the form of Bayesian prior distributions. We develop a prompt-based workflow for eliciting prior distributions from LLMs. Our approach is based on iterated learning, a Markov chain Monte Carlo method in which successive inferences are chained in a way that supports sampling from the prior distribution. We validated our method in settings where iterated learning has previously been used to estimate the priors of human…

(Dave Hoeek/Shutterstock) With the National Institute of Standards and Technology (NIST) set to publish the first Post Quantum Cryptography (PQC) Standards in a few weeks, attention is shifting to how to put the new quantum-resistant algorithms into practice. Indeed, the number of companies with practices to help others implement PQC is mushrooming and contains familiar (IBM, Deloitte, et al.) and unfamiliar names (QuSecure, SandboxAQ, etc.). The Migration to Post-Quantum Cryptography project, being run out of NIST’s National Cybersecurity Center of Excellence (NCCoE), is running at full-tilt and includes on the order of 40 commercial participants. In its own words, “The project will engage industry…