Yujia Li

Yujia Li
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Yujia Li

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Computer Science - Learning (6)
Statistics - Machine Learning (5)
Computer Science - Artificial Intelligence (4)
Physics - Optics (2)
Computer Science - Neural and Evolutionary Computing (2)
Computer Science - Computer Vision and Pattern Recognition (1)

Publications Authored By Yujia Li

Affiliations: 1Key Laboratory of Optoelectronic Technology and Systems, 2Key Laboratory of Optoelectronic Technology and Systems, 3Key Laboratory of Optoelectronic Technology and Systems, 4Key Laboratory of Optoelectronic Technology and Systems, 5Key Laboratory of Optoelectronic Technology and Systems

We report a wavelength-tunable Q-switched mode-locked fiber laser based on a compact optical tuning device, which is fabricated by coating single-layer graphene on the surface of micro-fiber Bragg grating (MFBG). Based on thermal-optical effect through evanescent interaction between graphene and MFBG, the center wavelength of MFBG can be accurately controlled by adjusting power of an external laser. By inserting the fabricated device into a compact fiber laser cavity mode-locked by single-wall carbon nanotubes, stable Q-switched mode-locked pulse is generated. Read More

We study characteristics of receptive fields of units in deep convolutional networks. The receptive field size is a crucial issue in many visual tasks, as the output must respond to large enough areas in the image to capture information about large objects. We introduce the notion of an effective receptive field, and show that it both has a Gaussian distribution and only occupies a fraction of the full theoretical receptive field. Read More

Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases. In this work, we study feature learning techniques for graph-structured inputs. Our starting point is previous work on Graph Neural Networks (Scarselli et al. Read More

We investigate the problem of learning representations that are invariant to certain nuisance or sensitive factors of variation in the data while retaining as much of the remaining information as possible. Our model is based on a variational autoencoding architecture with priors that encourage independence between sensitive and latent factors of variation. Any subsequent processing, such as classification, can then be performed on this purged latent representation. Read More

We propose a Watt-level, all-fiber, ultrafast Er/Yb-codoped double-clad fiber laser passively mode-locked by reduced graphene oxide (rGO) interacting with a weak evanescent field of photonic crystal fiber (PCF). The rGO solution is filled into the cladding holes of the PCF based on total reflection, and after evaporation, the rGO flakes bear only 1/107 of the total energy in laser system, which enhances the thermal damage threshold and decreases the accumulated nonlinearity. By incorporating the saturable absorber into an Er/Yb-codoped fiber ring cavity, stable conventional soliton with a duration of 573 fs is generated, and a average output power up to 1. Read More

We consider the problem of learning deep generative models from data. We formulate a method that generates an independent sample via a single feedforward pass through a multilayer perceptron, as in the recently proposed generative adversarial networks (Goodfellow et al., 2014). Read More

A key element in transfer learning is representation learning; if representations can be developed that expose the relevant factors underlying the data, then new tasks and domains can be learned readily based on mappings of these salient factors. We propose that an important aim for these representations are to be unbiased. Different forms of representation learning can be derived from alternative definitions of unwanted bias, e. Read More

The mean field algorithm is a widely used approximate inference algorithm for graphical models whose exact inference is intractable. In each iteration of mean field, the approximate marginals for each variable are updated by getting information from the neighbors. This process can be equivalently converted into a feedforward network, with each layer representing one iteration of mean field and with tied weights on all layers. Read More