skip to main content

Search for: All records

Creators/Authors contains: "Tang, S."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. The inductive biases of trained neural networks are difficult to understand and, consequently, to adapt to new settings. We study the inductive biases of linearizations of neural networks, which we show to be surprisingly good summaries of the full network functions. Inspired by this finding, we propose a technique for embedding these inductive biases into Gaussian processes through a kernel designed from the Jacobian of the network. In this setting, domain adaptation takes the form of interpretable posterior inference, with accompanying uncertainty estimation. This inference is analytic and free of local optima issues found in standard techniques such as fine-tuningmore »neural network weights to a new task. We develop significant computational speed-ups based on matrix multiplies, including a novel implementation for scalable Fisher vector products. Our experiments on both image classification and regression demonstrate the promise and convenience of this framework for transfer learning, compared to neural network fine-tuning.« less
  2. Abstract The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program User Facility produces ground-based long-term continuous unique measurements for atmospheric state, precipitation, turbulent fluxes, radiation, aerosol, cloud, and the land surface, which are collected at multiple sites. These comprehensive datasets have been widely used to calibrate climate models and are proven to be invaluable for climate model development and improvement. This article introduces an evaluation package to facilitate the use of ground-based ARM measurements in climate model evaluation. The ARM data-oriented metrics and diagnostics package (ARM-DIAGS) includes both ARM observational datasets and a Python-based analysis toolkit for computationmore »and visualization. The observational datasets are compiled from multiple ARM data products and specifically tailored for use in climate model evaluation. In addition, ARM-DIAGS also includes simulation data from models participating the Coupled Model Intercomparison Project (CMIP), which will allow climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The analysis toolkit is designed to make the metrics and diagnostics quickly available to the model developers.« less
  3. Abstract The Phase-I trigger readout electronics upgrade of the ATLAS Liquid Argon calorimeters enhances thephysics reach of the experiment during the upcoming operation atincreasing Large Hadron Collider luminosities.The new system, installed during the second Large Hadron Collider Long Shutdown,increases the trigger readout granularity by up to a factor of tenas well as its precision and range.Consequently, the background rejection at trigger level is improvedthrough enhanced filtering algorithms utilizing the additional informationfor topological discrimination of electromagnetic and hadronic shower shapes.This paper presents the final designs of the new electronic elements,their custom electronic devices, the proceduresused to validate their proper functioning, andmore »the performance achievedduring the commissioning of this system.« less
    Free, publicly-accessible full text available May 1, 2023
  4. Free, publicly-accessible full text available September 1, 2022