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The Learning Grid and E-Assessment using Latent Semantic Analysis
E-assessment is an important component of e-learning and e-qualification. Formative and summative assessment serve different purposes and both types of evaluation are critical to the pedagogicalprocess. While students are studying, practicing, working, or revising, formative assessment provides direction, focus, and guidance. Summative assessment provides the means to evaluate a learner's achievement and communicate that achievement to interested parties. Latent Semantic Analysis (LSA) is a statistical method for inferring meaning from a text. Applications based on LSA exist that provide both summative and formative assessment of a learner's work. However, the huge computational needs are a major problem with this promising technique. This paper explains how LSA works, describes the breadth of existing applications using LSA, explains how LSA is particularly suited to e-assessment, and proposes research to exploit the potential computational power of the Grid to overcome one of LSA's drawbacks
Deep Generative Modeling of LiDAR Data
Building models capable of generating structured output is a key challenge
for AI and robotics. While generative models have been explored on many types
of data, little work has been done on synthesizing lidar scans, which play a
key role in robot mapping and localization. In this work, we show that one can
adapt deep generative models for this task by unravelling lidar scans into a 2D
point map. Our approach can generate high quality samples, while simultaneously
learning a meaningful latent representation of the data. We demonstrate
significant improvements against state-of-the-art point cloud generation
methods. Furthermore, we propose a novel data representation that augments the
2D signal with absolute positional information. We show that this helps
robustness to noisy and imputed input; the learned model can recover the
underlying lidar scan from seemingly uninformative dataComment: Presented at IROS 201
Learning Object Categories From Internet Image Searches
In this paper, we describe a simple approach to learning models of visual object categories from images gathered from Internet image search engines. The images for a given keyword are typically highly variable, with a large fraction being unrelated to the query term, and thus pose a challenging environment from which to learn. By training our models directly from Internet images, we remove the need to laboriously compile training data sets, required by most other recognition approaches-this opens up the possibility of learning object category models “on-the-fly.” We describe two simple approaches, derived from the probabilistic latent semantic analysis (pLSA) technique for text document analysis, that can be used to automatically learn object models from these data. We show two applications of the learned model: first, to rerank the images returned by the search engine, thus improving the quality of the search engine; and second, to recognize objects in other image data sets
3D Point Capsule Networks
In this paper, we propose 3D point-capsule networks, an auto-encoder designed
to process sparse 3D point clouds while preserving spatial arrangements of the
input data. 3D capsule networks arise as a direct consequence of our novel
unified 3D auto-encoder formulation. Their dynamic routing scheme and the
peculiar 2D latent space deployed by our approach bring in improvements for
several common point cloud-related tasks, such as object classification, object
reconstruction and part segmentation as substantiated by our extensive
evaluations. Moreover, it enables new applications such as part interpolation
and replacement.Comment: As published in CVPR 2019 (camera ready version), with supplementary
materia
DeepVoxels: Learning Persistent 3D Feature Embeddings
In this work, we address the lack of 3D understanding of generative neural
networks by introducing a persistent 3D feature embedding for view synthesis.
To this end, we propose DeepVoxels, a learned representation that encodes the
view-dependent appearance of a 3D scene without having to explicitly model its
geometry. At its core, our approach is based on a Cartesian 3D grid of
persistent embedded features that learn to make use of the underlying 3D scene
structure. Our approach combines insights from 3D geometric computer vision
with recent advances in learning image-to-image mappings based on adversarial
loss functions. DeepVoxels is supervised, without requiring a 3D reconstruction
of the scene, using a 2D re-rendering loss and enforces perspective and
multi-view geometry in a principled manner. We apply our persistent 3D scene
representation to the problem of novel view synthesis demonstrating
high-quality results for a variety of challenging scenes.Comment: Video: https://www.youtube.com/watch?v=HM_WsZhoGXw Supplemental
material:
https://drive.google.com/file/d/1BnZRyNcVUty6-LxAstN83H79ktUq8Cjp/view?usp=sharing
Code: https://github.com/vsitzmann/deepvoxels Project page:
https://vsitzmann.github.io/deepvoxels
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