Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Xu, Canran"'
The emergence of Large Language Models (LLMs) has revolutionized natural language processing in various applications especially in e-commerce. One crucial step before the application of such LLMs in these fields is to understand and compare the perfo
Externí odkaz:
http://arxiv.org/abs/2408.12779
Recently, prefix-tuning was proposed to efficiently adapt pre-trained language models to a broad spectrum of natural language classification tasks. It leverages soft prefix as task-specific indicators and language verbalizers as categorical-label men
Externí odkaz:
http://arxiv.org/abs/2211.05987
State-of-the-art approaches to spelling error correction problem include Transformer-based Seq2Seq models, which require large training sets and suffer from slow inference time; and sequence labeling models based on Transformer encoders like BERT, wh
Externí odkaz:
http://arxiv.org/abs/2109.14259
Autor:
Xu, Canran, Wu, Ming
Learning representations for feature interactions to model user behaviors is critical for recommendation system and click-trough rate (CTR) predictions. Recent advances in this area are empowered by deep learning methods which could learn sophisticat
Externí odkaz:
http://arxiv.org/abs/1911.09821
Publikováno v:
Phys. Rev. B 101, 024204 (2020)
We consider a one-dimensional spin chain system with quenched disorder and in the presence of a local periodic drive. We study the time evolution of the system in the Floquet basis and evaluate the fidelity susceptibility, which is a measure of how a
Externí odkaz:
http://arxiv.org/abs/1910.01628
Autor:
Xu, Canran, Li, Ruijiang
Link prediction is critical for the application of incomplete knowledge graph (KG) in the downstream tasks. As a family of effective approaches for link predictions, embedding methods try to learn low-rank representations for both entities and relati
Externí odkaz:
http://arxiv.org/abs/1906.00687
Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich a
Externí odkaz:
http://arxiv.org/abs/1811.04540
Autor:
Xu, Canran, Vavilov, Maxim G.
Publikováno v:
Phys. Rev. B 95, 085139 (2017)
We consider a one dimensional spin $1/2$ chain with Heisenberg interaction in a disordered parallel magnetic field. This system is known to exhibit the many body localization (MBL) transition at critical strength of disorder. We analyze the response
Externí odkaz:
http://arxiv.org/abs/1509.05158
Autor:
Govia, Luke C. G., Pritchett, Emily J., Xu, Canran, Plourde, B. L. T., Vavilov, Maxim G., Wilhelm, Frank K., McDermott, R.
Publikováno v:
Phys. Rev. A 90, 062307 (2014)
High-fidelity, efficient quantum nondemolition readout of quantum bits is integral to the goal of quantum computation. As superconducting circuits approach the requirements of scalable, universal fault tolerance, qubit readout must also meet the dema
Externí odkaz:
http://arxiv.org/abs/1502.01564
Publikováno v:
Phys. Rev. A 89, 052102 (2014)
We study the dynamics of a two-level system described by a slowly varying Hamiltonian and weakly coupled to the Ohmic environment. We follow the Bloch--Redfield perturbative approach to include the effect of the environment on qubit evolution and tak
Externí odkaz:
http://arxiv.org/abs/1402.4210