4,452 research outputs found
To go deep or wide in learning?
To achieve acceptable performance for AI tasks, one can either use
sophisticated feature extraction methods as the first layer in a two-layered
supervised learning model, or learn the features directly using a deep
(multi-layered) model. While the first approach is very problem-specific, the
second approach has computational overheads in learning multiple layers and
fine-tuning of the model. In this paper, we propose an approach called wide
learning based on arc-cosine kernels, that learns a single layer of infinite
width. We propose exact and inexact learning strategies for wide learning and
show that wide learning with single layer outperforms single layer as well as
deep architectures of finite width for some benchmark datasets.Comment: 9 pages, 1 figure, Accepted for publication in Seventeenth
International Conference on Artificial Intelligence and Statistic
A Deep Incremental Boltzmann Machine for Modeling Context in Robots
Context is an essential capability for robots that are to be as adaptive as
possible in challenging environments. Although there are many context modeling
efforts, they assume a fixed structure and number of contexts. In this paper,
we propose an incremental deep model that extends Restricted Boltzmann
Machines. Our model gets one scene at a time, and gradually extends the
contextual model when necessary, either by adding a new context or a new
context layer to form a hierarchy. We show on a scene classification benchmark
that our method converges to a good estimate of the contexts of the scenes, and
performs better or on-par on several tasks compared to other incremental models
or non-incremental models.Comment: 6 pages, 5 figures, International Conference on Robotics and
Automation (ICRA 2018
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