Zobrazeno 1 - 10
of 5 507
pro vyhledávání: '"Heldt, A"'
A $k$-height on a graph $G=(V, E)$ is an assignment $V\to\{0, \ldots, k\}$ such that the value on ajacent vertices differs by at most $1$. We study the Markov chain on $k$-heights that in each step selects a vertex at random, and, if admissible, incr
Externí odkaz:
http://arxiv.org/abs/2410.08992
We present the Learned Ranking Function (LRF), a system that takes short-term user-item behavior predictions as input and outputs a slate of recommendations that directly optimizes for long-term user satisfaction. Most previous work is based on optim
Externí odkaz:
http://arxiv.org/abs/2408.06512
Autor:
Zajac, Jonathan W. P., Muralikrishnan, Praveen, Heldt, Caryn L., Perry, Sarah L., Sarupria, Sapna
The stabilization of liquid biological products is a complex task that depends on the chemical composition of both the active ingredient and any excipients in solution. Frequently, a large number of unique excipients are required to stabilize biologi
Externí odkaz:
http://arxiv.org/abs/2407.00885
Autor:
Cao, Yuwei, Mehta, Nikhil, Yi, Xinyang, Keshavan, Raghunandan, Heldt, Lukasz, Hong, Lichan, Chi, Ed H., Sathiamoorthy, Maheswaran
Large language models (LLMs) have recently been used as backbones for recommender systems. However, their performance often lags behind conventional methods in standard tasks like retrieval. We attribute this to a mismatch between LLMs' knowledge and
Externí odkaz:
http://arxiv.org/abs/2404.00245
Autor:
Zajac, Jonathan W. P., Muralikrishnan, Praveen, Tohidian, Idris, Zeng, Xianci, Heldt, Caryn L., Perry, Sarah L., Sarupria, Sapna
Arginine has been a mainstay in biological formulation development for decades. To date, the way arginine modulates protein stability has been widely studied and debated. Here, we employed a hydrophobic polymer to decouple hydrophobic effects from ot
Externí odkaz:
http://arxiv.org/abs/2403.11305
Autor:
Yi, Xinyang, Wang, Shao-Chuan, He, Ruining, Chandrasekaran, Hariharan, Wu, Charles, Heldt, Lukasz, Hong, Lichan, Chen, Minmin, Chi, Ed H.
The last decade has witnessed many successes of deep learning-based models for industry-scale recommender systems. These models are typically trained offline in a batch manner. While being effective in capturing users' past interactions with recommen
Externí odkaz:
http://arxiv.org/abs/2307.15893
Autor:
Singh, Anima, Vu, Trung, Mehta, Nikhil, Keshavan, Raghunandan, Sathiamoorthy, Maheswaran, Zheng, Yilin, Hong, Lichan, Heldt, Lukasz, Wei, Li, Tandon, Devansh, Chi, Ed H., Yi, Xinyang
Randomly-hashed item ids are used ubiquitously in recommendation models. However, the learned representations from random hashing prevents generalization across similar items, causing problems of learning unseen and long-tail items, especially when i
Externí odkaz:
http://arxiv.org/abs/2306.08121
Autor:
Su, Yi, Wang, Xiangyu, Le, Elaine Ya, Liu, Liang, Li, Yuening, Lu, Haokai, Lipshitz, Benjamin, Badam, Sriraj, Heldt, Lukasz, Bi, Shuchao, Chi, Ed, Goodrow, Cristos, Wu, Su-Lin, Baugher, Lexi, Chen, Minmin
Effective exploration is believed to positively influence the long-term user experience on recommendation platforms. Determining its exact benefits, however, has been challenging. Regular A/B tests on exploration often measure neutral or even negativ
Externí odkaz:
http://arxiv.org/abs/2305.07764
Autor:
Rajput, Shashank, Mehta, Nikhil, Singh, Anima, Keshavan, Raghunandan H., Vu, Trung, Heldt, Lukasz, Hong, Lichan, Tay, Yi, Tran, Vinh Q., Samost, Jonah, Kula, Maciej, Chi, Ed H., Sathiamoorthy, Maheswaran
Modern recommender systems perform large-scale retrieval by first embedding queries and item candidates in the same unified space, followed by approximate nearest neighbor search to select top candidates given a query embedding. In this paper, we pro
Externí odkaz:
http://arxiv.org/abs/2305.05065
Autor:
Simona Boccaletti, MSc, Rafael Alfonso-Cristancho, PhD, Waseem Ahmed, MBA, Lehanne Sergison, Adaeze Eze, MSc, Prashant Auti, MS (Pharm), Cathelijne Alleman, MSc, Lohit Badgujar, PhD, Nicholas Halfpenny, MSc, Dorothea Heldt, MSc
Publikováno v:
Journal of Allergy and Clinical Immunology: Global, Vol 3, Iss 4, Pp 100334- (2024)
Background: Several biologics for the treatment of severe asthma are available as self-administration devices. Objective: We performed a systematic literature review to understand the use, benefits, and challenges of these self-administration devices
Externí odkaz:
https://doaj.org/article/0dd005e00b5b440ea1b7d523664cf921
