Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMelody: Generating and Visualizing Machine Learning Model Summary to Understand Data and Classifiers Together
205
0
0.0
(
0
)
Added by
Gromit Yeuk-Yin Chan
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
With the increasing sophistication of machine learning models, there are growing trends of developing model explanation techniques that focus on only one instance (local explanation) to ensure faithfulness to the original model. While these techniques provide accurate model interpretability on various data primitive (e.g., tabular, image, or text), a holistic Explainable Artificial Intelligence (XAI) experience also requires a global explanation of the model and dataset to enable sensemaking in different granularity. Thus, there is a vast potential in synergizing the model explanation and visual analytics approaches. In this paper, we present MELODY, an interactive algorithm to construct an optimal global overview of the model and data behavior by summarizing the local explanations using information theory. The result (i.e., an explanation summary) does not require additional learning models, restrictions of data primitives, or the knowledge of machine learning from the users. We also design MELODY UI, an interactive visual analytics system to demonstrate how the explanation summary connects the dots in various XAI tasks from a global overview to local inspections. We present three usage scenarios regarding tabular, image, and text classifications to illustrate how to generalize model interpretability of different data. Our experiments show that our approaches: (1) provides a better explanation summary compared to a straightforward information-theoretic summarization and (2) achieves a significant speedup in the end-to-end data modeling pipeline.
rate research
Read More
Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VISis needed. In this paper, we systematically survey 88 ML4VIS studies, aiming to answer two motivating questions: what visualization processes can be assisted by ML? and how ML techniques can be used to solve visualization problems? This survey reveals seven main processes where the employment of ML techniques can benefit visualizations:Data Processing4VIS, Data-VIS Mapping, InsightCommunication, Style Imitation, VIS Interaction, VIS Reading, and User Profiling. The seven processes are related to existing visualization theoretical models in an ML4VIS pipeline, aiming to illuminate the role of ML-assisted visualization in general visualizations.Meanwhile, the seven processes are mapped into main learning tasks in ML to align the capabilities of ML with the needs in visualization. Current practices and future opportunities of ML4VIS are discussed in the context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are still needed in the area of ML4VIS, we hope this paper can provide a stepping-stone for future exploration. A web-based interactive browser of this survey is available at https://ml4vis.github.io
Bottom-up coarse-grained molecular dynamics models are parameterized using complex effective Hamiltonians. These models are typically optimized to approximate high dimensional data from atomistic simulations. In contrast, human validation of these models is often limited to low dimensional statistics that do not necessarily differentiate between the CG model and said atomistic simulations. We propose that explainable machine learning can directly convey high-dimensional error to scientists and use Shapley additive explanations do so in two coarse-grained protein models.
Machine learning and statistical tools are applied to identify how parameters, such as temperature, influence peak stress and ice behavior. To enable the analysis, a common and small scale experimental data base is established.
Music Genres serve as an important meta-data in the field of music information retrieval and have been widely used for music classification and analysis tasks. Visualizing these music genres can thus be helpful for music exploration, archival and recommendation. Probabilistic topic models have been very successful in modelling text documents. In this work, we visualize music genres using a probabilistic topic model. Unlike text documents, audio is continuous and needs to be sliced into smaller segments. We use simple MFCC features of these segments as musical words. We apply the topic model on the corpus and subsequently use the genre annotations of the data to interpret and visualize the latent space.
Multiverse analysis is an approach to data analysis in which all reasonable analytic decisions are evaluated in parallel and interpreted collectively, in order to foster robustness and transparency. However, specifying a multiverse is demanding because analysts must manage myriad variants from a cross-product of analytic decisions, and the results require nuanced interpretation. We contribute Boba: an integrated domain-specific language (DSL) and visual analysis system for authoring and reviewing multiverse analyses. With the Boba DSL, analysts write the shared portion of analysis code only once, alongside local variations defining alternative decisions, from which the compiler generates a multiplex of scripts representing all possible analysis paths. The Boba Visualizer provides linked views of model results and the multiverse decision space to enable rapid, systematic assessment of consequential decisions and robustness, including sampling uncertainty and model fit. We demonstrate Bobas utility through two data analysis case studies, and reflect on challenges and design opportunities for multiverse analysis software.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
