340 research outputs found
A 3D explainability framework to uncover learning patterns and crucial sub-regions in variable sulci recognition
Precisely identifying sulcal features in brain MRI is made challenging by the
variability of brain folding. This research introduces an innovative 3D
explainability frame-work that validates outputs from deep learning networks in
their ability to detect the paracingulate sulcus, an anatomical feature that
may or may not be present on the frontal medial surface of the human brain.
This study trained and tested two networks, amalgamating local explainability
techniques GradCam and SHAP with a dimensionality reduction method. The
explainability framework provided both localized and global explanations, along
with accuracy of classification results, revealing pertinent sub-regions
contributing to the decision process through a post-fusion transformation of
explanatory and statistical features. Leveraging the TOP-OSLO dataset of MRI
acquired from patients with schizophrenia, greater accuracies of paracingulate
sulcus detection (presence or absence) were found in the left compared to right
hemispheres with distinct, but extensive sub-regions contributing to each
classification outcome. The study also inadvertently highlighted the critical
role of an unbiased annotation protocol in maintaining network performance
fairness. Our proposed method not only offers automated, impartial annotations
of a variable sulcus but also provides insights into the broader anatomical
variations associated with its presence throughout the brain. The adoption of
this methodology holds promise for instigating further explorations and
inquiries in the field of neuroscience
CaloDiffusion with GLaM for High Fidelity Calorimeter Simulation
Simulation is crucial for all aspects of collider data analysis, but the
available computing budget in the High Luminosity LHC era will be severely
constrained. Generative machine learning models may act as surrogates to
replace physics-based full simulation of particle detectors, and diffusion
models have recently emerged as the state of the art for other generative
tasks. We introduce CaloDiffusion, a denoising diffusion model trained on the
public CaloChallenge datasets to generate calorimeter showers. Our algorithm
employs 3D cylindrical convolutions, which take advantage of symmetries of the
underlying data representation. To handle irregular detector geometries, we
augment the diffusion model with a new geometry latent mapping (GLaM) layer to
learn forward and reverse transformations to a regular geometry that is
suitable for cylindrical convolutions. The showers generated by our approach
are nearly indistinguishable from the full simulation, as measured by several
different metrics.Comment: 21 pages, 9 figure
Efficient Methods for Natural Language Processing: A Survey
Recent work in natural language processing (NLP) has yielded appealing
results from scaling model parameters and training data; however, using only
scale to improve performance means that resource consumption also grows. Such
resources include data, time, storage, or energy, all of which are naturally
limited and unevenly distributed. This motivates research into efficient
methods that require fewer resources to achieve similar results. This survey
synthesizes and relates current methods and findings in efficient NLP. We aim
to provide both guidance for conducting NLP under limited resources, and point
towards promising research directions for developing more efficient methods.Comment: Accepted at TACL, pre publication versio
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