17,153 research outputs found
Photonic Delay Systems as Machine Learning Implementations
Nonlinear photonic delay systems present interesting implementation platforms
for machine learning models. They can be extremely fast, offer great degrees of
parallelism and potentially consume far less power than digital processors. So
far they have been successfully employed for signal processing using the
Reservoir Computing paradigm. In this paper we show that their range of
applicability can be greatly extended if we use gradient descent with
backpropagation through time on a model of the system to optimize the input
encoding of such systems. We perform physical experiments that demonstrate that
the obtained input encodings work well in reality, and we show that optimized
systems perform significantly better than the common Reservoir Computing
approach. The results presented here demonstrate that common gradient descent
techniques from machine learning may well be applicable on physical
neuro-inspired analog computers
Deep representation learning for human motion prediction and classification
Generative models of 3D human motion are often restricted to a small number
of activities and can therefore not generalize well to novel movements or
applications. In this work we propose a deep learning framework for human
motion capture data that learns a generic representation from a large corpus of
motion capture data and generalizes well to new, unseen, motions. Using an
encoding-decoding network that learns to predict future 3D poses from the most
recent past, we extract a feature representation of human motion. Most work on
deep learning for sequence prediction focuses on video and speech. Since
skeletal data has a different structure, we present and evaluate different
network architectures that make different assumptions about time dependencies
and limb correlations. To quantify the learned features, we use the output of
different layers for action classification and visualize the receptive fields
of the network units. Our method outperforms the recent state of the art in
skeletal motion prediction even though these use action specific training data.
Our results show that deep feedforward networks, trained from a generic mocap
database, can successfully be used for feature extraction from human motion
data and that this representation can be used as a foundation for
classification and prediction.Comment: This paper is published at the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), 201
Action-Conditional Video Prediction using Deep Networks in Atari Games
Motivated by vision-based reinforcement learning (RL) problems, in particular
Atari games from the recent benchmark Aracade Learning Environment (ALE), we
consider spatio-temporal prediction problems where future (image-)frames are
dependent on control variables or actions as well as previous frames. While not
composed of natural scenes, frames in Atari games are high-dimensional in size,
can involve tens of objects with one or more objects being controlled by the
actions directly and many other objects being influenced indirectly, can
involve entry and departure of objects, and can involve deep partial
observability. We propose and evaluate two deep neural network architectures
that consist of encoding, action-conditional transformation, and decoding
layers based on convolutional neural networks and recurrent neural networks.
Experimental results show that the proposed architectures are able to generate
visually-realistic frames that are also useful for control over approximately
100-step action-conditional futures in some games. To the best of our
knowledge, this paper is the first to make and evaluate long-term predictions
on high-dimensional video conditioned by control inputs.Comment: Published at NIPS 2015 (Advances in Neural Information Processing
Systems 28
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
This paper takes a step towards temporal reasoning in a dynamically changing
video, not in the pixel space that constitutes its frames, but in a latent
space that describes the non-linear dynamics of the objects in its world. We
introduce the Kalman variational auto-encoder, a framework for unsupervised
learning of sequential data that disentangles two latent representations: an
object's representation, coming from a recognition model, and a latent state
describing its dynamics. As a result, the evolution of the world can be
imagined and missing data imputed, both without the need to generate high
dimensional frames at each time step. The model is trained end-to-end on videos
of a variety of simulated physical systems, and outperforms competing methods
in generative and missing data imputation tasks.Comment: NIPS 201
Discriminative Recurrent Sparse Auto-Encoders
We present the discriminative recurrent sparse auto-encoder model, comprising
a recurrent encoder of rectified linear units, unrolled for a fixed number of
iterations, and connected to two linear decoders that reconstruct the input and
predict its supervised classification. Training via
backpropagation-through-time initially minimizes an unsupervised sparse
reconstruction error; the loss function is then augmented with a discriminative
term on the supervised classification. The depth implicit in the
temporally-unrolled form allows the system to exhibit all the power of deep
networks, while substantially reducing the number of trainable parameters.
From an initially unstructured network the hidden units differentiate into
categorical-units, each of which represents an input prototype with a
well-defined class; and part-units representing deformations of these
prototypes. The learned organization of the recurrent encoder is hierarchical:
part-units are driven directly by the input, whereas the activity of
categorical-units builds up over time through interactions with the part-units.
Even using a small number of hidden units per layer, discriminative recurrent
sparse auto-encoders achieve excellent performance on MNIST.Comment: Added clarifications suggested by reviewers. 15 pages, 10 figure
A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines
Information in neural networks is represented as weighted connections, or
synapses, between neurons. This poses a problem as the primary computational
bottleneck for neural networks is the vector-matrix multiply when inputs are
multiplied by the neural network weights. Conventional processing architectures
are not well suited for simulating neural networks, often requiring large
amounts of energy and time. Additionally, synapses in biological neural
networks are not binary connections, but exhibit a nonlinear response function
as neurotransmitters are emitted and diffuse between neurons. Inspired by
neuroscience principles, we present a digital neuromorphic architecture, the
Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex
synaptic response functions without requiring additional hardware components.
We consider the paradigm of spiking neurons with temporally coded information
as opposed to non-spiking rate coded neurons used in most neural networks. In
this paradigm we examine liquid state machines applied to speech recognition
and show how a liquid state machine with temporal dynamics maps onto the
STPU-demonstrating the flexibility and efficiency of the STPU for instantiating
neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN
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