5,638 research outputs found
Constraining Implicit Space with Minimum Description Length: An Unsupervised Attention Mechanism across Neural Network Layers
Inspired by the adaptation phenomenon of neuronal firing, we propose the
regularity normalization (RN) as an unsupervised attention mechanism (UAM)
which computes the statistical regularity in the implicit space of neural
networks under the Minimum Description Length (MDL) principle. Treating the
neural network optimization process as a partially observable model selection
problem, UAM constrains the implicit space by a normalization factor, the
universal code length. We compute this universal code incrementally across
neural network layers and demonstrated the flexibility to include data priors
such as top-down attention and other oracle information. Empirically, our
approach outperforms existing normalization methods in tackling limited,
imbalanced and non-stationary input distribution in image classification,
classic control, procedurally-generated reinforcement learning, generative
modeling, handwriting generation and question answering tasks with various
neural network architectures. Lastly, UAM tracks dependency and critical
learning stages across layers and recurrent time steps of deep networks
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
Artificial autonomous agents and robots interacting in complex environments
are required to continually acquire and fine-tune knowledge over sustained
periods of time. The ability to learn from continuous streams of information is
referred to as lifelong learning and represents a long-standing challenge for
neural network models due to catastrophic forgetting. Computational models of
lifelong learning typically alleviate catastrophic forgetting in experimental
scenarios with given datasets of static images and limited complexity, thereby
differing significantly from the conditions artificial agents are exposed to.
In more natural settings, sequential information may become progressively
available over time and access to previous experience may be restricted. In
this paper, we propose a dual-memory self-organizing architecture for lifelong
learning scenarios. The architecture comprises two growing recurrent networks
with the complementary tasks of learning object instances (episodic memory) and
categories (semantic memory). Both growing networks can expand in response to
novel sensory experience: the episodic memory learns fine-grained
spatiotemporal representations of object instances in an unsupervised fashion
while the semantic memory uses task-relevant signals to regulate structural
plasticity levels and develop more compact representations from episodic
experience. For the consolidation of knowledge in the absence of external
sensory input, the episodic memory periodically replays trajectories of neural
reactivations. We evaluate the proposed model on the CORe50 benchmark dataset
for continuous object recognition, showing that we significantly outperform
current methods of lifelong learning in three different incremental learning
scenario
Sliced Cramer synaptic consolidation for preserving deeply learned representations
Deep neural networks suffer from the inability to preserve the learned data representation (i.e., catastrophic forgetting) in domains where the input data distribution is non-stationary, and it changes during training. Various selective synaptic
plasticity approaches have been recently proposed to preserve network parameters, which are crucial for previously learned tasks while learning new tasks.
We explore such selective synaptic plasticity approaches through a unifying lens
of memory replay and show the close relationship between methods like Elastic
Weight Consolidation (EWC) and Memory-Aware-Synapses (MAS). We then propose a fundamentally different class of preservation methods that aim at preserving the distribution of the network’s output at an arbitrary layer for previous tasks
while learning a new one. We propose the sliced Cramer distance as a suitable ´
choice for such preservation and evaluate our Sliced Cramer Preservation (SCP) ´
algorithm through extensive empirical investigations on various network architectures in both supervised and unsupervised learning settings. We show that SCP
consistently utilizes the learning capacity of the network better than online-EWC
and MAS methods on various incremental learning tasks
An Unsupervised Neural Network for Real-Time Low-Level Control of a Mobile Robot: Noise Resistance, Stability, and Hardware Implementation
We have recently introduced a neural network mobile robot controller (NETMORC). The controller is based on earlier neural network models of biological sensory-motor control. We have shown that NETMORC is able to guide a differential drive mobile robot to an arbitrary stationary or moving target while compensating for noise and other forms of disturbance, such as wheel slippage or changes in the robot's plant. Furthermore, NETMORC is able to adapt in response to long-term changes in the robot's plant, such as a change in the radius of the wheels. In this article we first review the NETMORC architecture, and then we prove that NETMORC is asymptotically stable. After presenting a series of simulations results showing robustness to disturbances, we compare NETMORC performance on a trajectory-following task with the performance of an alternative controller. Finally, we describe preliminary results on the hardware implementation of NETMORC with the mobile robot ROBUTER.Sloan Fellowship (BR-3122), Air Force Office of Scientific Research (F49620-92-J-0499
Learning Deep Belief Networks from Non-Stationary Streams
Deep learning has proven to be beneficial for complex tasks such as classifying images. However, this approach has been mostly applied to static datasets. The analysis of non-stationary (e.g., concept drift) streams of data involves specific issues connected with the temporal and changing nature of the data. In this paper, we propose a proof-of-concept method, called Adaptive Deep Belief Networks, of how deep learning can be generalized to learn online from changing streams of data. We do so by exploiting the generative properties of the model to incrementally re-train the Deep Belief Network whenever new data are collected. This approach eliminates the need to store past observations and, therefore, requires only constant memory consumption. Hence, our approach can be valuable for life-long learning from non-stationary data streams. © 2012 Springer-Verlag
A Real-Time Unsupervised Neural Network for the Low-Level Control of a Mobile Robot in a Nonstationary Environment
This article introduces a real-time, unsupervised neural network that learns to control a two-degree-of-freedom mobile robot in a nonstationary environment. The neural controller, which is termed neural NETwork MObile Robot Controller (NETMORC), combines associative learning and Vector Associative Map (YAM) learning to generate transformations between spatial and velocity coordinates. As a result, the controller learns the wheel velocities required to reach a target at an arbitrary distance and angle. The transformations are learned during an unsupervised training phase, during which the robot moves as a result of randomly selected wheel velocities. The robot learns the relationship between these velocities and the resulting incremental movements. Aside form being able to reach stationary or moving targets, the NETMORC structure also enables the robot to perform successfully in spite of disturbances in the enviroment, such as wheel slippage, or changes in the robot's plant, including changes in wheel radius, changes in inter-wheel distance, or changes in the internal time step of the system. Finally, the controller is extended to include a module that learns an internal odometric transformation, allowing the robot to reach targets when visual input is sporadic or unreliable.Sloan Fellowship (BR-3122), Air Force Office of Scientific Research (F49620-92-J-0499
Adaptive Resonance Theory
SyNAPSE program of the Defense Advanced Projects Research Agency (Hewlett-Packard Company, subcontract under DARPA prime contract HR0011-09-3-0001, and HRL Laboratories LLC, subcontract #801881-BS under DARPA prime contract HR0011-09-C-0001); CELEST, an NSF Science of Learning Center (SBE-0354378
Measuring Catastrophic Forgetting in Neural Networks
Deep neural networks are used in many state-of-the-art systems for machine
perception. Once a network is trained to do a specific task, e.g., bird
classification, it cannot easily be trained to do new tasks, e.g.,
incrementally learning to recognize additional bird species or learning an
entirely different task such as flower recognition. When new tasks are added,
typical deep neural networks are prone to catastrophically forgetting previous
tasks. Networks that are capable of assimilating new information incrementally,
much like how humans form new memories over time, will be more efficient than
re-training the model from scratch each time a new task needs to be learned.
There have been multiple attempts to develop schemes that mitigate catastrophic
forgetting, but these methods have not been directly compared, the tests used
to evaluate them vary considerably, and these methods have only been evaluated
on small-scale problems (e.g., MNIST). In this paper, we introduce new metrics
and benchmarks for directly comparing five different mechanisms designed to
mitigate catastrophic forgetting in neural networks: regularization,
ensembling, rehearsal, dual-memory, and sparse-coding. Our experiments on
real-world images and sounds show that the mechanism(s) that are critical for
optimal performance vary based on the incremental training paradigm and type of
data being used, but they all demonstrate that the catastrophic forgetting
problem has yet to be solved.Comment: To appear in AAAI 201
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