We develop a flow-based sampling algorithm for $\mathrm{SU}(N)$ lattice gauge theories that is gauge invariant by construction. Our key contribution is constructing a class of flows on an $\mathrm{SU}(N)$ variable [or on a $\mathrm{U}(N)$ variable by a simple alternative] that respects matrix conjugation symmetry. We apply this technique to sample distributions of single $\mathrm{SU}(N)$ variables and to construct flow-based samplers for SU(2) and SU(3) lattice gauge theory in two dimensions.

Sampling using SU ( N ) gauge equivariant flows

Recommender systems (RS) have become an integral part of our daily life. However, most current RS often repeatedly recommend items to users with similar profiles. We argue that recommendation should be diversified by leveraging session contexts with personalized user profiles. For this, current session-based RS (SBRS) often assume a rigidly ordered sequence over data which does not fit in many real-world cases. Moreover, personalization is often omitted in current SBRS. Accordingly, a personalized SBRS over relaxedly ordered user-session contexts is more pragmatic. In doing so, deep-structured models tend to be too complex to serve for online SBRS owing to the large number of users and items. Therefore, we design an efficient SBRS with shallow wide-in-wide-out networks, inspired by the successful experience in modern language modelings. The experiments on a real-world e-commerce dataset show the superiority of our model over the state-of-the-art methods.

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Diversifying personalized recommendation with user-session context

The purpose of the study was to provide a comprehensive review of recent machine learning (ML) and deep learning (DL) applications in urological practice. Numerous studies have reported their use in the medical care of various urological disorders; however, no critical analysis has been made to date. A detailed search of original articles was performed using the PubMed MEDLINE database to identify recent English literature relevant to ML and DL applications in the fields of urolithiasis, renal cell carcinoma (RCC), bladder cancer (BCa), and prostate cancer (PCa). In total, 43 articles were included addressing these four subfields. The most common ML and DL application in urolithiasis is in the prediction of endourologic surgical outcomes. The main area of research involving ML and DL in RCC concerns the differentiation between benign and malignant small renal masses, Fuhrman nuclear grade prediction, and gene expression-based molecular signatures. BCa studies employ radiomics and texture feature analysis for the distinction between low- and high-grade tumors, address accurate image-based cytology, and use algorithms to predict treatment response, tumor recurrence, and patient survival. PCa studies aim at developing algorithms for Gleason score prediction, MRI computer-aided diagnosis, and surgical outcomes and biochemical recurrence prediction. Studies consistently found the superiority of these methods over traditional statistical methods. The continuous incorporation of clinical data, further ML and DL algorithm retraining, and generalizability of models will augment the prediction accuracy and enhance individualized medicine.

Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer.

In metals additive manufacturing (AM), materials and components are concurrently made in a single process as layers of metal are fabricated on top of each other in the near-final topology required for the end-use product. Consequently, tens to hundreds of materials and part design degrees of freedom must be simultaneously controlled and understood; hence, metals AM is a highly interdisciplinary technology that requires synchronized consideration of physics, chemistry, materials science, physical metallurgy, computer science, electrical engineering, and mechanical engineering. The use of modern machine learning approaches to model these degrees of freedom can reduce the time and cost to elucidate the science of metals AM and to optimize the engineering of these complex, multidisciplinary processes. New machine learning techniques are not needed for most metals AM development; those used in other sects of materials science will also work for AM. Most prolifically, the density functional theory (DFT) community has used many of them since the early 2000s for evaluating numerous combinations of elements and crystal structures to discover new materials. This materials technologies-focused review introduces the basic mathematics and terminology of machine learning through the lens of metals AM, and then examines potential uses of machine learning to advance metals AM, highlighting the many parallels to previous efforts in materials science and manufacturing while also discussing new challenges and adaptations specific to metals AM.

Machine Learning for Materials Developments in Metals Additive Manufacturing

We consider the online version of the well known Principal Component Analysis (PCA) problem. In standard PCA, the input to the problem is a set of d-dimensional vectors X = [x1, . . ., xn] and a target dimension k

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Online principal components analysis

Proceedings of the 25th International Conference on Neural Information Processing Systems

Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 2

Proceedings of the 26th International Conference on Neural Information Processing Systems

Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2

Advances in neural information processing systems 26: 27th Annual Conference on Neural Information Processing Systems 2013: December 5-10, Lake Tahoe, Nevada, USA

C. J. C. Burges

Papers

Proceedings of the 25th International Conference on Neural Information Processing Systems

Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 2

Proceedings of the 26th International Conference on Neural Information Processing Systems

Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2

Advances in neural information processing systems 26: 27th Annual Conference on Neural Information Processing Systems 2013: December 5-10, Lake Tahoe, Nevada, USA