240,690 research outputs found
Challenges in Representation Learning: A report on three machine learning contests
The ICML 2013 Workshop on Challenges in Representation Learning focused on
three challenges: the black box learning challenge, the facial expression
recognition challenge, and the multimodal learning challenge. We describe the
datasets created for these challenges and summarize the results of the
competitions. We provide suggestions for organizers of future challenges and
some comments on what kind of knowledge can be gained from machine learning
competitions.Comment: 8 pages, 2 figure
Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities
There is an increasing interest in exploiting mobile sensing technologies and
machine learning techniques for mental health monitoring and intervention.
Researchers have effectively used contextual information, such as mobility,
communication and mobile phone usage patterns for quantifying individuals' mood
and wellbeing. In this paper, we investigate the effectiveness of neural
network models for predicting users' level of stress by using the location
information collected by smartphones. We characterize the mobility patterns of
individuals using the GPS metrics presented in the literature and employ these
metrics as input to the network. We evaluate our approach on the open-source
StudentLife dataset. Moreover, we discuss the challenges and trade-offs
involved in building machine learning models for digital mental health and
highlight potential future work in this direction.Comment: 6 pages, 2 figures, In Proceedings of the NIPS Workshop on Machine
Learning for Healthcare 2017 (ML4H 2017). Colocated with NIPS 201
Evaluation Challenges for Geospatial ML
As geospatial machine learning models and maps derived from their predictions
are increasingly used for downstream analyses in science and policy, it is
imperative to evaluate their accuracy and applicability. Geospatial machine
learning has key distinctions from other learning paradigms, and as such, the
correct way to measure performance of spatial machine learning outputs has been
a topic of debate. In this paper, I delineate unique challenges of model
evaluation for geospatial machine learning with global or remotely sensed
datasets, culminating in concrete takeaways to improve evaluations of
geospatial model performance.Comment: ICLR 2023 Workshop on Machine Learning for Remote Sensin
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