1,538 research outputs found
Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference
In modern computer science education, massive open online courses (MOOCs) log
thousands of hours of data about how students solve coding challenges. Being so
rich in data, these platforms have garnered the interest of the machine
learning community, with many new algorithms attempting to autonomously provide
feedback to help future students learn. But what about those first hundred
thousand students? In most educational contexts (i.e. classrooms), assignments
do not have enough historical data for supervised learning. In this paper, we
introduce a human-in-the-loop "rubric sampling" approach to tackle the "zero
shot" feedback challenge. We are able to provide autonomous feedback for the
first students working on an introductory programming assignment with accuracy
that substantially outperforms data-hungry algorithms and approaches human
level fidelity. Rubric sampling requires minimal teacher effort, can associate
feedback with specific parts of a student's solution and can articulate a
student's misconceptions in the language of the instructor. Deep learning
inference enables rubric sampling to further improve as more assignment
specific student data is acquired. We demonstrate our results on a novel
dataset from Code.org, the world's largest programming education platform.Comment: To appear at AAAI 2019; 9 page
Multi-Source Neural Variational Inference
Learning from multiple sources of information is an important problem in
machine-learning research. The key challenges are learning representations and
formulating inference methods that take into account the complementarity and
redundancy of various information sources. In this paper we formulate a
variational autoencoder based multi-source learning framework in which each
encoder is conditioned on a different information source. This allows us to
relate the sources via the shared latent variables by computing divergence
measures between individual source's posterior approximations. We explore a
variety of options to learn these encoders and to integrate the beliefs they
compute into a consistent posterior approximation. We visualise learned beliefs
on a toy dataset and evaluate our methods for learning shared representations
and structured output prediction, showing trade-offs of learning separate
encoders for each information source. Furthermore, we demonstrate how conflict
detection and redundancy can increase robustness of inference in a multi-source
setting.Comment: AAAI 2019, Association for the Advancement of Artificial Intelligence
(AAAI) 201
Generalized Multimodal ELBO
Multiple data types naturally co-occur when describing real-world phenomena
and learning from them is a long-standing goal in machine learning research.
However, existing self-supervised generative models approximating an ELBO are
not able to fulfill all desired requirements of multimodal models: their
posterior approximation functions lead to a trade-off between the semantic
coherence and the ability to learn the joint data distribution. We propose a
new, generalized ELBO formulation for multimodal data that overcomes these
limitations. The new objective encompasses two previous methods as special
cases and combines their benefits without compromises. In extensive
experiments, we demonstrate the advantage of the proposed method compared to
state-of-the-art models in self-supervised, generative learning tasks.Comment: 2021 ICL
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
Learning from different data types is a long-standing goal in machine
learning research, as multiple information sources co-occur when describing
natural phenomena. However, existing generative models that approximate a
multimodal ELBO rely on difficult or inefficient training schemes to learn a
joint distribution and the dependencies between modalities. In this work, we
propose a novel, efficient objective function that utilizes the Jensen-Shannon
divergence for multiple distributions. It simultaneously approximates the
unimodal and joint multimodal posteriors directly via a dynamic prior. In
addition, we theoretically prove that the new multimodal JS-divergence (mmJSD)
objective optimizes an ELBO. In extensive experiments, we demonstrate the
advantage of the proposed mmJSD model compared to previous work in
unsupervised, generative learning tasks.Comment: Accepted at NeurIPS 2020, camera-ready versio
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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