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Found 6 papers, 0 papers with code
DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data
Despite the tremendous advances achieved over the past years by deep learning techniques, the latest risk prediction models for industrial applications still rely on highly handtuned stage-wised statistical learning tools, such as gradient boosting and random forest methods.
PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization
Few-shot abstractive summarization has become a challenging task in natural language generation.
Analytic model for feature maps in the primary visual cortex
This model consists of three parts: (i) an anisotropic Laplacian (AL) operator that represents the local neural sensitivity to the orientation of visual inputs; (ii) a receptive field (RF) operator that models the anisotropic spatial RF that projects to a given V1 cell over scales of a few tenths of a millimeter and combines with the AL operator to give an overall OP operator; and (iii) a map that describes how the parameters of these operators vary approximately periodically across V1.
Efficient Pipelines for Vision-Based Context Sensing
In summary, there are three major challenges for today's vision-based context sensing systems: data collection and labeling at large scale, process large data volumes efficiently with limited hardware resources, and extract accurate context out of vision data.
On Localizing a Camera from a Single Image
Public cameras often have limited metadata describing their attributes.
Grab: Fast and Accurate Sensor Processing for Cashier-Free Shopping
Cashier-free shopping systems like Amazon Go improve shopping experience, but can require significant store redesign.
