7,372 research outputs found
A Novel Progressive Multi-label Classifier for Classincremental Data
In this paper, a progressive learning algorithm for multi-label
classification to learn new labels while retaining the knowledge of previous
labels is designed. New output neurons corresponding to new labels are added
and the neural network connections and parameters are automatically
restructured as if the label has been introduced from the beginning. This work
is the first of the kind in multi-label classifier for class-incremental
learning. It is useful for real-world applications such as robotics where
streaming data are available and the number of labels is often unknown. Based
on the Extreme Learning Machine framework, a novel universal classifier with
plug and play capabilities for progressive multi-label classification is
developed. Experimental results on various benchmark synthetic and real
datasets validate the efficiency and effectiveness of our proposed algorithm.Comment: 5 pages, 3 figures, 4 table
An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning
DQN (Deep Q-Network) is a method to perform Q-learning for reinforcement
learning using deep neural networks. DQNs require a large buffer and batch
processing for an experience replay and rely on a backpropagation based
iterative optimization, making them difficult to be implemented on
resource-limited edge devices. In this paper, we propose a lightweight
on-device reinforcement learning approach for low-cost FPGA devices. It
exploits a recently proposed neural-network based on-device learning approach
that does not rely on the backpropagation method but uses OS-ELM (Online
Sequential Extreme Learning Machine) based training algorithm. In addition, we
propose a combination of L2 regularization and spectral normalization for the
on-device reinforcement learning so that output values of the neural network
can be fit into a certain range and the reinforcement learning becomes stable.
The proposed reinforcement learning approach is designed for PYNQ-Z1 board as a
low-cost FPGA platform. The evaluation results using OpenAI Gym demonstrate
that the proposed algorithm and its FPGA implementation complete a CartPole-v0
task 29.77x and 89.40x faster than a conventional DQN-based approach when the
number of hidden-layer nodes is 64
What does fault tolerant Deep Learning need from MPI?
Deep Learning (DL) algorithms have become the de facto Machine Learning (ML)
algorithm for large scale data analysis. DL algorithms are computationally
expensive - even distributed DL implementations which use MPI require days of
training (model learning) time on commonly studied datasets. Long running DL
applications become susceptible to faults - requiring development of a fault
tolerant system infrastructure, in addition to fault tolerant DL algorithms.
This raises an important question: What is needed from MPI for de- signing
fault tolerant DL implementations? In this paper, we address this problem for
permanent faults. We motivate the need for a fault tolerant MPI specification
by an in-depth consideration of recent innovations in DL algorithms and their
properties, which drive the need for specific fault tolerance features. We
present an in-depth discussion on the suitability of different parallelism
types (model, data and hybrid); a need (or lack thereof) for check-pointing of
any critical data structures; and most importantly, consideration for several
fault tolerance proposals (user-level fault mitigation (ULFM), Reinit) in MPI
and their applicability to fault tolerant DL implementations. We leverage a
distributed memory implementation of Caffe, currently available under the
Machine Learning Toolkit for Extreme Scale (MaTEx). We implement our approaches
by ex- tending MaTEx-Caffe for using ULFM-based implementation. Our evaluation
using the ImageNet dataset and AlexNet, and GoogLeNet neural network topologies
demonstrates the effectiveness of the proposed fault tolerant DL implementation
using OpenMPI based ULFM
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