10 research outputs found
Hurricane Forecasting: A Novel Multimodal Machine Learning Framework
This paper describes a machine learning (ML) framework for tropical cyclone
intensity and track forecasting, combining multiple distinct ML techniques and
utilizing diverse data sources. Our framework, which we refer to as Hurricast
(HURR), is built upon the combination of distinct data processing techniques
using gradient-boosted trees and novel encoder-decoder architectures, including
CNN, GRU and Transformers components. We propose a deep-feature extractor
methodology to mix spatial-temporal data with statistical data efficiently. Our
multimodal framework unleashes the potential of making forecasts based on a
wide range of data sources, including historical storm data, and visual data
such as reanalysis atmospheric images. We evaluate our models with current
operational forecasts in North Atlantic and Eastern Pacific basins on 2016-2019
for 24-hour lead time, and show our models consistently outperform
statistical-dynamical models and compete with the best dynamical models, while
computing forecasts in seconds. Furthermore, the inclusion of Hurricast into an
operational forecast consensus model leads to a significant improvement of 5% -
15% over NHC's official forecast, thus highlighting the complementary
properties with existing approaches. In summary, our work demonstrates that
combining different data sources and distinct machine learning methodologies
can lead to superior tropical cyclone forecasting. We hope that this work opens
the door for further use of machine learning in meteorological forecasting.Comment: Under revision by the AMS' Weather and Forecasting journa
InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification
Insects play such a crucial role in ecosystems that a shift in demography of
just a few species can have devastating consequences at environmental, social
and economic levels. Despite this, evaluation of insect demography is strongly
limited by the difficulty of collecting census data at sufficient scale. We
propose a method to gather and leverage observations from bystanders, hikers,
and entomology enthusiasts in order to provide researchers with data that could
significantly help anticipate and identify environmental threats. Finally, we
show that there is indeed interest on both sides for such collaboration.Comment: Appearing at the International Conference on Machine Learning, AI for
Social Good Workshop, Long Beach, United States, 2019 Appearing at the
International Conference on Computer Vision, AI for Wildlife Conservation
Workshop, Seoul, South Korea, 2019 5 pages, 6 figure
Holistic Deep Learning
There is much interest in deep learning to solve challenges in applying
neural network models in real-world environments. In particular, three areas
have received considerable attention: adversarial robustness, parameter
sparsity, and output stability. Despite numerous attempts to solve these
problems independently, little work simultaneously addresses the challenges. In
this paper, we address the problem of constructing holistic deep learning
models by proposing a novel formulation that solves these issues in
combination. Real-world experiments on both tabular and MNIST datasets show
that our formulation can simultaneously improve the accuracy, robustness,
stability, and sparsity over traditional deep learning models among many
others.Comment: In preparation for Machine Learnin
TabText: A Flexible and Contextual Approach to Tabular Data Representation
Tabular data is essential for applying machine learning tasks across various
industries. However, traditional data processing methods do not fully utilize
all the information available in the tables, ignoring important contextual
information such as column header descriptions. In addition, pre-processing
data into a tabular format can remain a labor-intensive bottleneck in model
development. This work introduces TabText, a processing and feature extraction
framework that extracts contextual information from tabular data structures.
TabText addresses processing difficulties by converting the content into
language and utilizing pre-trained large language models (LLMs). We evaluate
our framework on nine healthcare prediction tasks ranging from patient
discharge, ICU admission, and mortality. We show that 1) applying our TabText
framework enables the generation of high-performing and simple machine learning
baseline models with minimal data pre-processing, and 2) augmenting
pre-processed tabular data with TabText representations improves the average
and worst-case AUC performance of standard machine learning models by as much
as 6%
Over-MAP: Structural Attention Mechanism and Automated Semantic Segmentation Ensembled for Uncertainty Prediction
International audienceBoth theoretical and practical problems in deep learning classification require solutions for assessing uncertainty prediction but current state-of-the-art methods in this area are computationally expensive. In this paper, we propose a new confidence measure dubbed Over-MAP that utilizes a measure of overlap between structural attention mechanisms and segmentation methods, that is of particular interest in accurate fine-grained contexts. We show that this classification confidence increases with the degree of overlap. The associated confidence and identification tools are conceptually simple, efficient, and of high practical interest as they allow for weeding out misleading examples in training data. Our measure is currently deployed in the real-world on widely used platforms to annotate large-scale data efficiently
Gradient-Based Localization and Spatial Attention for Confidence Measure in Fine-Grained Recognition using Deep Neural Networks
Both theoretical and practical problems in deep learning classification benefit from assessing uncertainty prediction. In addition, current state-of-the-art methods in this area are computationally expensive: for example,~\cite{loquercio2020general} is a general method for uncertainty estimation in deep learning that relies on Monte-Carlo sampling. We propose a new, efficient confidence measure later dubbed Over-MAP that utilizes a measure of overlap between structural attention mechanisms and segmentation methods. It does not rely on sampling or retraining. We show that the classification confidence increases with the degree of overlap. The associated confidence and identification tools are conceptually simple, efficient and of high practical interest as they allow for weeding out misleading examples in training data. Our measure is currently deployed in the real-world on widely used platforms to annotate large-scale data efficiently
Over-MAP: Structural Attention Mechanism and Automated Semantic Segmentation Ensembled for Uncertainty Prediction
International audienceBoth theoretical and practical problems in deep learning classification require solutions for assessing uncertainty prediction but current state-of-the-art methods in this area are computationally expensive. In this paper, we propose a new confidence measure dubbed Over-MAP that utilizes a measure of overlap between structural attention mechanisms and segmentation methods, that is of particular interest in accurate fine-grained contexts. We show that this classification confidence increases with the degree of overlap. The associated confidence and identification tools are conceptually simple, efficient, and of high practical interest as they allow for weeding out misleading examples in training data. Our measure is currently deployed in the real-world on widely used platforms to annotate large-scale data efficiently
Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification
Copyright by the authors. All rights reserved to authors only. Correspondence to: ckantor (at) stanford [dot] eduInternational audienceFine-grained classification aims at distinguishing between items with similar global perception and patterns, but that differ by minute details. Our primary challenges come from both small inter-class variations and large intra-class variations. In this article, we propose to combine several innovations to improve fine-grained classification within the use-case of wildlife, which is of practical interest for experts. We utilize geo-spatiotemporal data to enrich the picture information and further improve the performance. We also investigate state-of-the-art methods for handling the imbalanced data issue