405 research outputs found
Event Representations with Tensor-based Compositions
Robust and flexible event representations are important to many core areas in
language understanding. Scripts were proposed early on as a way of representing
sequences of events for such understanding, and has recently attracted renewed
attention. However, obtaining effective representations for modeling
script-like event sequences is challenging. It requires representations that
can capture event-level and scenario-level semantics. We propose a new
tensor-based composition method for creating event representations. The method
captures more subtle semantic interactions between an event and its entities
and yields representations that are effective at multiple event-related tasks.
With the continuous representations, we also devise a simple schema generation
method which produces better schemas compared to a prior discrete
representation based method. Our analysis shows that the tensors capture
distinct usages of a predicate even when there are only subtle differences in
their surface realizations.Comment: Accepted at AAAI 201
Hierarchical Quantized Representations for Script Generation
Scripts define knowledge about how everyday scenarios (such as going to a
restaurant) are expected to unfold. One of the challenges to learning scripts
is the hierarchical nature of the knowledge. For example, a suspect arrested
might plead innocent or guilty, and a very different track of events is then
expected to happen. To capture this type of information, we propose an
autoencoder model with a latent space defined by a hierarchy of categorical
variables. We utilize a recently proposed vector quantization based approach,
which allows continuous embeddings to be associated with each latent variable
value. This permits the decoder to softly decide what portions of the latent
hierarchy to condition on by attending over the value embeddings for a given
setting. Our model effectively encodes and generates scripts, outperforming a
recent language modeling-based method on several standard tasks, and allowing
the autoencoder model to achieve substantially lower perplexity scores compared
to the previous language modeling-based method.Comment: EMNLP 201
Spurious grain formation due to convection at cross-section-changes during directional solidification
Turbine blades are a critical component in high powered gas turbine engines. These components are directionally solidified to have a single grain orientation, which allows them to operate under high temperature and stress conditions. Spurious grain formation is a major concern when forming these turbine blades. The purpose of this study was to study the effect convection has on forming these defects within turbine blades. Two alloys, Pb-5.8%Sb (solutally unstable) and Al-19%Cu (solutally stable) were directionally solidified upward in a positive thermal gradient (thermally stable) in a graphite crucible having abrupt cross-sectional area change from 3.2 mm diameter to 9 mm diameter. In the Lead alloy after the cross-section-expansion there is no observable new grain formation. However, in the Aluminum alloy there is extensive new grain formation after the expansion.https://engagedscholarship.csuohio.edu/u_poster_2017/1055/thumbnail.jp
NeRFiller: Completing Scenes via Generative 3D Inpainting
We propose NeRFiller, an approach that completes missing portions of a 3D
capture via generative 3D inpainting using off-the-shelf 2D visual generative
models. Often parts of a captured 3D scene or object are missing due to mesh
reconstruction failures or a lack of observations (e.g., contact regions, such
as the bottom of objects, or hard-to-reach areas). We approach this challenging
3D inpainting problem by leveraging a 2D inpainting diffusion model. We
identify a surprising behavior of these models, where they generate more 3D
consistent inpaints when images form a 22 grid, and show how to
generalize this behavior to more than four images. We then present an iterative
framework to distill these inpainted regions into a single consistent 3D scene.
In contrast to related works, we focus on completing scenes rather than
deleting foreground objects, and our approach does not require tight 2D object
masks or text. We compare our approach to relevant baselines adapted to our
setting on a variety of scenes, where NeRFiller creates the most 3D consistent
and plausible scene completions. Our project page is at
https://ethanweber.me/nerfiller.Comment: Project page: https://ethanweber.me/nerfille
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