Timezone: »

 
Targeted Meta-Learning for Critical Incident Detection in Weather Data
Mohammad Mahdi Kamani · Sadegh Farhang · Mehrdad Mahdavi · James Wang

Fri Jun 14 02:50 PM -- 03:00 PM (PDT) @

Due to imbalanced or heavy-tailed nature of weather- and climate-related datasets, the performance of standard deep learning models significantly deviates from their expected behavior on test data. Classical methods to address these issues are mostly data or application dependent, hence burdensome to tune. Meta-learning approaches, on the other hand, aim to learn hyperparameters in the learning process using different objective functions on training and validation data. However, these methods suffer from high computational complexity and are not scalable to large datasets. In this paper, we aim to apply a novel framework named as targeted meta-learning to rectify this issue, and show its efficacy in dealing with the aforementioned biases in datasets. This framework employs a small, well-crafted target dataset that resembles the desired nature of test data in order to guide the learning process in a coupled manner. We empirically show that this framework can overcome the bias issue, common to weather-related datasets, in a bow echo detection case study.

Author Information

Mohammad Mahdi Kamani (The Pennsylvania State University)
Sadegh Farhang (Pennsylvania State University)
Mehrdad Mahdavi (Pennsylvania State University)
James Wang (Penn State University)

More from the Same Authors

  • 2019 : Networking Lunch (provided) + Poster Session »
    Abraham Stanway · Alex Robson · Aneesh Rangnekar · Ashesh Chattopadhyay · Ashley Pilipiszyn · Benjamin LeRoy · Bolong Cheng · Ce Zhang · Chaopeng Shen · Christian Schroeder · Christian Clough · Clement DUHART · Clement Fung · Cozmin Ududec · Dali Wang · David Dao · di wu · Dimitrios Giannakis · Dino Sejdinovic · Doina Precup · Duncan Watson-Parris · Gege Wen · George Chen · Gopal Erinjippurath · Haifeng Li · Han Zou · Herke van Hoof · Hillary A Scannell · Hiroshi Mamitsuka · Hongbao Zhang · Jaegul Choo · James Wang · James Requeima · Jessica Hwang · Jinfan Xu · Johan Mathe · Jonathan Binas · Joonseok Lee · Kalai Ramea · Kate Duffy · Kevin McCloskey · Kris Sankaran · Lester Mackey · Letif Mones · Loubna Benabbou · Lynn Kaack · Matthew Hoffman · Mayur Mudigonda · Mehrdad Mahdavi · Michael McCourt · Mingchao Jiang · Mohammad Mahdi Kamani · Neel Guha · Niccolo Dalmasso · Nick Pawlowski · Nikola Milojevic-Dupont · Paulo Orenstein · Pedram Hassanzadeh · Pekka Marttinen · Ramesh Nair · Sadegh Farhang · Samuel Kaski · Sandeep Manjanna · Sasha Luccioni · Shuby Deshpande · Soo Kim · Soukayna Mouatadid · Sunghyun Park · Tao Lin · Telmo Felgueira · Thomas Hornigold · Tianle Yuan · Tom Beucler · Tracy Cui · Volodymyr Kuleshov · Wei Yu · yang song · Ydo Wexler · Yoshua Bengio · Zhecheng Wang · Zhuangfang Yi · Zouheir Malki
  • 2019 Poster: Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization »
    Farzin Haddadpour · Mohammad Mahdi Kamani · Mehrdad Mahdavi · Viveck Cadambe
  • 2019 Oral: Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization »
    Farzin Haddadpour · Mohammad Mahdi Kamani · Mehrdad Mahdavi · Viveck Cadambe
  • 2017 Poster: A Simulated Annealing Based Inexact Oracle for Wasserstein Loss Minimization »
    Jianbo Ye · James Wang · Jia Li
  • 2017 Talk: A Simulated Annealing Based Inexact Oracle for Wasserstein Loss Minimization »
    Jianbo Ye · James Wang · Jia Li