21 papers · ranked by Valyu relevance
Weijie Zhao, Shulong Tan, Ping Li
With the continuous popularity of deep learning and representation learning, fast vector search becomes a vital task in various ranking/retrieval based applications, say recommendation, ads ranking and question answering. Neural network based ranking is widely adopted due to its powerful capacity in modeling complex…
Jiafeng Guo, Yixing Fan, Liang Pang, Yang Liu + 5 more
'Hamed Zamani' 'Chen Wu' 'W. Bruce Croft' 'Xueqi Cheng'] Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from traditional heuristic methods, probabilistic methods, to modern machine learning…
Leonardo Rigutini, Tiziano Papini, Marco Maggini, Franco Scarselli
The problem of relevance ranking consists of sorting a set of objects with respect to a given criterion. Since users may prefer different relevance criteria, the ranking algorithms should be adaptable to the user needs. Two main approaches exist in literature for the task of learning to rank: 1) a score function…
Donging Liu, Muzhi Wang, Huan Luo
Ranking—a typical relational structure—helps people organize complex information and overcome cognitive load, yet in real-world settings it is often inferred from few-shot learning of partial comparisons. How the human brain makes relational inferences under such sparse conditions remains unknown. In a preregistered…
Marius Köppel, Alexander Segner, Martin Wagener, Lukas Pensel + 2 more
'Andreas Karwath' 'Stefan Krämer'] Abstract. We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture. We show mathematically that our model is reflexive, antisymmetric, and transitive allowing for simplified training and improved performance.…
Rui Zhang, Jingfei Li, Shaoyu Wu, Dabin Meng + 1 more
The research on service supply chain has attracted more and more focus from both academia and industrial community. In a service supply chain, the selection of supplier portfolio is an important and difficult problem due to the fact that a supplier portfolio may include multiple suppliers from a variety of fields. To…
Shahabeddin Sotudian, Ioannis Ch. Paschalidis
Personalized drug response prediction is an approach for tailoring effective therapeutic strategies for patients based on their tumors’ genomic characterization. The current study introduces a new listwise Learning-to-rank (LTR) model called Inversion Transformer-based Neural Ranking (ITNR). ITNR utilizes genomic…
Hai-Tao Yu
Deep neural networks has become the first choice for researchers working on algorithmic aspects of learning-to-rank. Unfortunately, it is not trivial to find the optimal setting of hyper-parameters that achieves the best ranking performance. As a result, it becomes more and more difficult to develop a new model and…
Ayman Elgharabawy, Mukesh Prasad, Chin-Teng Lin, Giorgio Terracina
Subgroup label ranking aims to rank groups of labels using a single ranking model, is a new problem faced in preference learning. This paper introduces the Subgroup Preference Neural Network (SGPNN) that combines multiple networks have different activation function, learning rate, and output layer into one artificial…
Marius Köppel, Alexander Segner, Martin Wagener, Lukas Pensel + 3 more
'Andreas Karwath' 'Christian Schmitt' 'Stefan Kramer'] In the extensive search for new physics, the precise measurement of the Higgs boson continues to play an important role. To this end, machine learning techniques have been recently applied to processes like the Higgs production via vector-boson fusion. In this…
Matteo Tadiello, Marko Ludaic, Vsevolod Viliuga, Arne Elofsson
AlphaFold has transformed structural biology with an unprecedented accuracy in modeling protein structures and their interactions with biomolecules, with AlphaFold3 (AF3) achieving state-of-the-art performance. However, AF3 and other methods often struggle to accurately predict the structure of protein complexes that…
Gabriele Di Antonio, Sofia Raglio, Maurizio Mattia
A general mathematical description of the way the brain encodes ordinal knowledge of sequences is still lacking. Coherently with the well-established idea of mixed selectivity in high-dimensional state spaces, we conjectured the existence of a linear solution for serial learning tasks. In this theoretical framework…
Oh-Hyeon Choung, Riccardo Vianello, Marwin Segler, Nikolaus Stiefl + 1 more
The lead optimization process in drug discovery campaigns is an arduous endeavour where the input of many medicinal chemists is weighed in order to reach a desired molecular property profile. Building the expertise to successfully drive such projects collaboratively is a very timeconsuming process that typically spans…
Chunting Wei, Jiwei Qin, Qiulin Ren, Leon Rothkrantz + 1 more
'Sergio Toral Marín'] In recent years, hybrid recommendation techniques based on feature fusion have gained extensive attention in the field of list ranking. Most of them fuse linear and nonlinear models to simultaneously learn the linear and nonlinear features of entities and jointly fit user-item interactions. These…
Jim Jing-Yan Wang, Halima Bensmail
In the database retrieval and nearest neighbor classification tasks, the two basic problems are to represent the query and database objects, and to learn the ranking scores of the database objects to the query. Many studies have been conducted for the representation learning and the ranking score learning problems…
Yuling Tian, Hongxian Zhang, Quan Zou
For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a…
Aakarsh Vermani, Valentina Kouznetsova, Igor Tsigelny
P38-alpha (MAPK14) is a protein kinase that is implicated in the pathological mechanisms of BAG3 P209L myofibrillar myopathy, cancers, Alzheimer’s disease and other diseases like rheumatoid arthritis. Inhibition of p38 has shown promise as treatment for these diseases. Traditional drug discovery methods were unable to…
Fei Cai, Deke Guo, Honghui Chen, Zhen Shu + 1 more
Information retrieval applications have to publish their output in the form of ranked lists. Such a requirement motivates researchers to develop methods that can automatically learn effective ranking models. Many existing methods usually perform analysis on multidimensional features of query-document pairs directly and…
Ivica Slavkov, Matej Petković, Pierre Geurts, Dragi Kocev + 2 more
In this article, we propose a method for evaluating feature ranking algorithms. A feature ranking algorithm estimates the importance of descriptive features when predicting the target variable, and the proposed method evaluates the correctness of these importance values by computing the error measures of two chains of…
Alessandro Tibo, Jiazhen He, Jon Paul Janet, Eva Nittinger + 1 more
How many near-neighbors does a molecule have? This is a simple, fundamental, but unsolved question in chemistry. It is key for solving many important molecular optimization problems, for example in lead optimization in drug discovery under the similarity principle assumption. Generative models can sample virtual…
Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi + 3 more
Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of…