264,060 research outputs found
Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance
Natural language understanding(NLU) is challenging for finance due to the
lack of annotated data and the specialized language in that domain. As a
result, researchers have proposed to use pre-trained language model and
multi-task learning to learn robust representations. However, aggressive
fine-tuning often causes over-fitting and multi-task learning may favor tasks
with significantly larger amounts data, etc. To address these problems, in this
paper, we investigate model-agnostic meta-learning algorithm(MAML) in
low-resource financial NLU tasks. Our contribution includes: 1. we explore the
performance of MAML method with multiple types of tasks: GLUE datasets, SNLI,
Sci-Tail and Financial PhraseBank; 2. we study the performance of MAML method
with multiple single-type tasks: a real scenario stock price prediction problem
with twitter text data. Our models achieve the state-of-the-art performance
according to the experimental results, which demonstrate that our method can
adapt fast and well to low-resource situations.Comment: 13 pages, 6 figures, 8 table
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
We study the problem of learning Granger causality between event types from
asynchronous, interdependent, multi-type event sequences. Existing work suffers
from either limited model flexibility or poor model explainability and thus
fails to uncover Granger causality across a wide variety of event sequences
with diverse event interdependency. To address these weaknesses, we propose
CAUSE (Causality from AttribUtions on Sequence of Events), a novel framework
for the studied task. The key idea of CAUSE is to first implicitly capture the
underlying event interdependency by fitting a neural point process, and then
extract from the process a Granger causality statistic using an axiomatic
attribution method. Across multiple datasets riddled with diverse event
interdependency, we demonstrate that CAUSE achieves superior performance on
correctly inferring the inter-type Granger causality over a range of
state-of-the-art methods
Studying the effects of various process parameters on early age hydration of single- and multi-phase cementitious systems
”The hydration of multi-phase ordinary Portland cement (OPC) and its pure phase derivatives, such as tricalcium silicate (C3S) and belite (ß-C2S), are studied in the context varying process parameters -- for instance, variable water content, water activity, superplasticizer structure and dose, and mineral additive type and particle size. These parameters are studied by means of physical experiments and numerical/computational techniques, such as: thermodynamic estimations; numerical kinetic-based modelling; and artificial intelligence techniques like machine learning (ML) models. In the past decade, numerical kinetic modeling has greatly improved in terms of fitting experimental, isothermal calorimetry to kinetic-based modelling based the evolving understanding of hydration processes. However, there are remaining points of contention within literature, that could potentially take an additional decade to resolve. The dissertation work utilizes the numeric kinetic-based, phase boundary nucleation and growth (pBNG) model but also introduces ML models as a technique to predict the heat-evolution -- which, is related to other fresh properties, such as rheological, microstructural, and mechanical properties -- of a paste system by utilizing underlying nonlinear time-dependent composition-property relationships”--Abstract, page iv
XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera
We present a real-time approach for multi-person 3D motion capture at over 30
fps using a single RGB camera. It operates successfully in generic scenes which
may contain occlusions by objects and by other people. Our method operates in
subsequent stages. The first stage is a convolutional neural network (CNN) that
estimates 2D and 3D pose features along with identity assignments for all
visible joints of all individuals.We contribute a new architecture for this
CNN, called SelecSLS Net, that uses novel selective long and short range skip
connections to improve the information flow allowing for a drastically faster
network without compromising accuracy. In the second stage, a fully connected
neural network turns the possibly partial (on account of occlusion) 2Dpose and
3Dpose features for each subject into a complete 3Dpose estimate per
individual. The third stage applies space-time skeletal model fitting to the
predicted 2D and 3D pose per subject to further reconcile the 2D and 3D pose,
and enforce temporal coherence. Our method returns the full skeletal pose in
joint angles for each subject. This is a further key distinction from previous
work that do not produce joint angle results of a coherent skeleton in real
time for multi-person scenes. The proposed system runs on consumer hardware at
a previously unseen speed of more than 30 fps given 512x320 images as input
while achieving state-of-the-art accuracy, which we will demonstrate on a range
of challenging real-world scenes.Comment: To appear in ACM Transactions on Graphics (SIGGRAPH) 202
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