2 research outputs found
Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild
Accessibility is a major challenge of machine learning (ML). Typical ML
models are built by specialists and require specialized hardware/software as
well as ML experience to validate. This makes it challenging for non-technical
collaborators and endpoint users (e.g. physicians) to easily provide feedback
on model development and to gain trust in ML. The accessibility challenge also
makes collaboration more difficult and limits the ML researcher's exposure to
realistic data and scenarios that occur in the wild. To improve accessibility
and facilitate collaboration, we developed an open-source Python package,
Gradio, which allows researchers to rapidly generate a visual interface for
their ML models. Gradio makes accessing any ML model as easy as sharing a URL.
Our development of Gradio is informed by interviews with a number of machine
learning researchers who participate in interdisciplinary collaborations. Their
feedback identified that Gradio should support a variety of interfaces and
frameworks, allow for easy sharing of the interface, allow for input
manipulation and interactive inference by the domain expert, as well as allow
embedding the interface in iPython notebooks. We developed these features and
carried out a case study to understand Gradio's usefulness and usability in the
setting of a machine learning collaboration between a researcher and a
cardiologist.Comment: Presented at 2019 ICML Workshop on Human in the Loop Learning (HILL
2019), Long Beach, US
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series
Recurrent neural networks (RNNs) are commonly applied to clinical time-series
data with the goal of learning patient risk stratification models. Their
effectiveness is due, in part, to their use of parameter sharing over time
(i.e., cells are repeated hence the name recurrent). We hypothesize, however,
that this trait also contributes to the increased difficulty such models have
with learning relationships that change over time. Conditional shift, i.e.,
changes in the relationship between the input X and the output y, arises when
risk factors associated with the event of interest change over the course of a
patient admission. While in theory, RNNs and gated RNNs (e.g., LSTMs) in
particular should be capable of learning time-varying relationships, when
training data are limited, such models often fail to accurately capture these
dynamics. We illustrate the advantages and disadvantages of complete parameter
sharing (RNNs) by comparing an LSTM with shared parameters to a sequential
architecture with time-varying parameters on prediction tasks involving three
clinically-relevant outcomes: acute respiratory failure (ARF), shock, and
in-hospital mortality. In experiments using synthetic data, we demonstrate how
parameter sharing in LSTMs leads to worse performance in the presence of
conditional shift. To improve upon the dichotomy between complete parameter
sharing and no parameter sharing, we propose a novel RNN formulation based on a
mixture model in which we relax parameter sharing over time. The proposed
method outperforms standard LSTMs and other state-of-the-art baselines across
all tasks. In settings with limited data, relaxed parameter sharing can lead to
improved patient risk stratification performance.Comment: Machine Learning for Healthcare 201