2,367 research outputs found
SMars: Semi-Supervised Learning for Mars Semantic Segmentation
Deep learning has become a powerful tool for Mars exploration. Mars terrain
semantic segmentation is an important Martian vision task, which is the base of
rover autonomous planning and safe driving. However, there is a lack of
sufficient detailed and high-confidence data annotations, which are exactly
required by most deep learning methods to obtain a good model. To address this
problem, we propose our solution from the perspective of joint data and method
design. We first present a newdataset S5Mars for Semi-SuperviSed learning on
Mars Semantic Segmentation, which contains 6K high-resolution images and is
sparsely annotated based on confidence, ensuring the high quality of labels.
Then to learn from this sparse data, we propose a semi-supervised learning
(SSL) framework for Mars image semantic segmentation, to learn representations
from limited labeled data. Different from the existing SSL methods which are
mostly targeted at the Earth image data, our method takes into account Mars
data characteristics. Specifically, we first investigate the impact of current
widely used natural image augmentations on Mars images. Based on the analysis,
we then proposed two novel and effective augmentations for SSL of Mars
segmentation, AugIN and SAM-Mix, which serve as strong augmentations to boost
the model performance. Meanwhile, to fully leverage the unlabeled data, we
introduce a soft-to-hard consistency learning strategy, learning from different
targets based on prediction confidence. Experimental results show that our
method can outperform state-of-the-art SSL approaches remarkably. Our proposed
dataset is available at https://jhang2020.github.io/S5Mars.github.io/
Extracting and Representing Qualitative Behaviors of Complex Systems in Phase Spaces
We develop a qualitative method for understanding and representing phase space structures of complex systems and demonstrate the method with a program, MAPS --- Modeler and Analyzer for Phase Spaces, using deep domain knowledge of dynamical system theory. Given a dynamical system, the program generates a complete, high level symbolic description of the phase space structure sensible to human beings and manipulable by other programs. Using the phase space descriptions, we are developing a novel control synthesis strategy to automatically synthesize a controller for a nonlinear system in the phase space to achieve desired properties
Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation
Language models have achieved impressive performances on dialogue generation
tasks. However, when generating responses for a conversation that requires
factual knowledge, they are far from perfect, due to an absence of mechanisms
to retrieve, encode, and reflect the knowledge in the generated responses. Some
knowledge-grounded dialogue generation methods tackle this problem by
leveraging facts from Knowledge Graphs (KGs); however, they do not guarantee
that the model utilizes a relevant piece of knowledge from the KG. To overcome
this limitation, we propose SUbgraph Retrieval-augmented GEneration (SURGE), a
framework for generating context-relevant and knowledge-grounded dialogues with
the KG. Specifically, our SURGE framework first retrieves the relevant subgraph
from the KG, and then enforces consistency across facts by perturbing their
word embeddings conditioned by the retrieved subgraph. Then, we utilize
contrastive learning to ensure that the generated texts have high similarity to
the retrieved subgraphs. We validate our SURGE framework on OpendialKG and
KOMODIS datasets, showing that it generates high-quality dialogues that
faithfully reflect the knowledge from KG.Comment: Preprint. Under revie
Big-Data-Driven Materials Science and its FAIR Data Infrastructure
This chapter addresses the forth paradigm of materials research -- big-data
driven materials science. Its concepts and state-of-the-art are described, and
its challenges and chances are discussed. For furthering the field, Open Data
and an all-embracing sharing, an efficient data infrastructure, and the rich
ecosystem of computer codes used in the community are of critical importance.
For shaping this forth paradigm and contributing to the development or
discovery of improved and novel materials, data must be what is now called FAIR
-- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets
the stage for advances of methods from artificial intelligence that operate on
large data sets to find trends and patterns that cannot be obtained from
individual calculations and not even directly from high-throughput studies.
Recent progress is reviewed and demonstrated, and the chapter is concluded by a
forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W.
Andreoni), Springer 2018/201
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
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