Up to Speed on AI and Deep Learning: December 2 to December 9


Research and Tutorials

  • Open Access Evaluation of colorectal cancer subtypes and cell lines using deep learning
    (Life Science Alliance)
    Molecular profiling techniques have been used to better understand the variability between tumors and disease models such as cell lines. The method used efficiently leverages multiomics data sets containing copy number alterations, gene expression, and point mutations and learns latent factors that describe data in lower dimensions.
  • Making Smart Homes Smarter: Optimizing Energy Consumption with Human in the Loop
    This paper presents a novel approach to accurately configure IoT devices while achieving the twin objectives of energy optimization along with conforming to user preferences. The study comprises of unsupervised clustering of devices’ data to find the states of operation for each device, followed by probabilistically analyzing user behavior to determine their preferred states.
  • Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
    Explaining the output of a complex machine learning (ML) model often requires approximation using a simpler model. To construct interpretable explanations that are also consistent with the original ML model, counterfactual examples — — showing how the model’s output changes with small perturbations to the input — — have been proposed. This paper extends the work in counterfactual explanations by addressing the challenge of feasibility of such examples.
  • A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?
    In this work, current state of the art light detection and ranging (lidar) sensors are tested in controlled conditions in a fog chamber. The research presents current problems and disturbance patterns for four different state of the art lidar systems. Moreover, the research investigates how tuning internal parameters can improve their performance in bad weather situations.

AI and ML in Society

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