15,195 research outputs found
Learning feed-forward one-shot learners
One-shot learning is usually tackled by using generative models or
discriminative embeddings. Discriminative methods based on deep learning, which
are very effective in other learning scenarios, are ill-suited for one-shot
learning as they need large amounts of training data. In this paper, we propose
a method to learn the parameters of a deep model in one shot. We construct the
learner as a second deep network, called a learnet, which predicts the
parameters of a pupil network from a single exemplar. In this manner we obtain
an efficient feed-forward one-shot learner, trained end-to-end by minimizing a
one-shot classification objective in a learning to learn formulation. In order
to make the construction feasible, we propose a number of factorizations of the
parameters of the pupil network. We demonstrate encouraging results by learning
characters from single exemplars in Omniglot, and by tracking visual objects
from a single initial exemplar in the Visual Object Tracking benchmark.Comment: The first three authors contributed equally, and are listed in
alphabetical orde
Learning feed-forward one-shot learners
Abstract One-shot learning is usually tackled by using generative models or discriminative embeddings. Discriminative methods based on deep learning, which are very effective in other learning scenarios, are ill-suited for one-shot learning as they need large amounts of training data. In this paper, we propose a method to learn the parameters of a deep model in one shot. We construct the learner as a second deep network, called a learnet, which predicts the parameters of a pupil network from a single exemplar. In this manner we obtain an efficient feed-forward one-shot learner, trained end-to-end by minimizing a one-shot classification objective in a learning to learn formulation. In order to make the construction feasible, we propose a number of factorizations of the parameters of the pupil network. We demonstrate encouraging results by learning characters from single exemplars in Omniglot, and by tracking visual objects from a single initial exemplar in the Visual Object Tracking benchmark
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
Sustaining K-12 Professional Development in Geology: Recurrent Participation in RockCamp
Researchers surveyed repeat attendees in a geology professional development program known as RockCamp in order to determine the reasons for their sustained involvement in this program. This article describes their findings, which suggest that the teachers' sustained involvement in the RockCamp Program is stimulated by situated learning experiences stressing a compare, contrast, connect, and construct pedagogy within a supportive learning community. Most teachers cited such reasons as efficacy, fun, right time of life, and support, as well as content, friendship, and methodology as reasons for their continued participation in the program. Educational levels: Graduate or professional
Making Good on LSTMs' Unfulfilled Promise
LSTMs promise much to financial time-series analysis, temporal and cross-sectional inference, but we find that they do not deliver in a real-world financial management task. We examine an alternative called Continual Learning (CL), a memory-augmented approach, which can provide transparent explanations, i.e. which memory did what and when. This work has implications for many financial applications including credit, time-varying fairness in decision making and more. We make three important new observations. Firstly, as well as being more explainable, time-series CL approaches outperform LSTMs as well as a simple sliding window learner using feed-forward neural networks (FFNN). Secondly, we show that CL based on a sliding window learner (FFNN) is more effective than CL based on a sequential learner (LSTM). Thirdly, we examine how real-world, time-series noise impacts several similarity approaches used in CL memory addressing. We provide these insights using an approach called Continual Learning Augmentation (CLA) tested on a complex real-world problem, emerging market equities investment decision making. CLA provides a test-bed as it can be based on different types of time-series learners, allowing testing of LSTM and FFNN learners side by side. CLA is also used to test several distance approaches used in a memory recall-gate: Euclidean distance (ED), dynamic time warping (DTW), auto-encoders (AE) and a novel hybrid approach, warp-AE. We find that ED under-performs DTW and AE but warp-AE shows the best overall performance in a real-world financial task
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