4 research outputs found
Applying Machine Learning to Crowd-sourced Data from Earthquake Detective
We present the Earthquake Detective dataset - A crowdsourced set of labels on
potentially triggered (PT) earthquakes and tremors. These events are those
which may have been triggered by large magnitude and often distant earthquakes.
We apply Machine Learning to classify these PT seismic events and explore the
challenges faced in segregating such low amplitude signals. The data set and
code are available online.Comment: Published in AI for Earth Sciences Workshop, NeurIPS 202
On the special role of class-selective neurons in early training
It is commonly observed that deep networks trained for classification exhibit
class-selective neurons in their early and intermediate layers. Intriguingly,
recent studies have shown that these class-selective neurons can be ablated
without deteriorating network function. But if class-selective neurons are not
necessary, why do they exist? We attempt to answer this question in a series of
experiments on ResNet-50s trained on ImageNet. We first show that
class-selective neurons emerge during the first few epochs of training, before
receding rapidly but not completely; this suggests that class-selective neurons
found in trained networks are in fact vestigial remains of early training. With
single-neuron ablation experiments, we then show that class-selective neurons
are important for network function in this early phase of training. We also
observe that the network is close to a linear regime in this early phase; we
thus speculate that class-selective neurons appear early in training as
quasi-linear shortcut solutions to the classification task. Finally, in causal
experiments where we regularize against class selectivity at different points
in training, we show that the presence of class-selective neurons early in
training is critical to the successful training of the network; in contrast,
class-selective neurons can be suppressed later in training with little effect
on final accuracy. It remains to be understood by which mechanism the presence
of class-selective neurons in the early phase of training contributes to the
successful training of networks
Image-based Early Detection System for Wildfires
Wildfires are a disastrous phenomenon which cause damage to land, loss of
property, air pollution, and even loss of human life. Due to the warmer and
drier conditions created by climate change, more severe and uncontrollable
wildfires are expected to occur in the coming years. This could lead to a
global wildfire crisis and have dire consequences on our planet. Hence, it has
become imperative to use technology to help prevent the spread of wildfires.
One way to prevent the spread of wildfires before they become too large is to
perform early detection i.e, detecting the smoke before the actual fire starts.
In this paper, we present our Wildfire Detection and Alert System which use
machine learning to detect wildfire smoke with a high degree of accuracy and
can send immediate alerts to users. Our technology is currently being used in
the USA to monitor data coming in from hundreds of cameras daily. We show that
our system has a high true detection rate and a low false detection rate. Our
performance evaluation study also shows that on an average our system detects
wildfire smoke faster than an actual person.Comment: Published in Tackling Climate Change with Machine Learning workshop,
Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS
2022