7,842 research outputs found
On Compression of Unsupervised Neural Nets by Pruning Weak Connections
Unsupervised neural nets such as Restricted Boltzmann Machines(RBMs) and Deep
Belif Networks(DBNs), are powerful in automatic feature extraction,unsupervised
weight initialization and density estimation. In this paper,we demonstrate that
the parameters of these neural nets can be dramatically reduced without
affecting their performance. We describe a method to reduce the parameters
required by RBM which is the basic building block for deep architectures.
Further we propose an unsupervised sparse deep architectures selection
algorithm to form sparse deep neural networks.Experimental results show that
there is virtually no loss in either generative or discriminative performance
A Deep Bag-of-Features Model for Music Auto-Tagging
Feature learning and deep learning have drawn great attention in recent years
as a way of transforming input data into more effective representations using
learning algorithms. Such interest has grown in the area of music information
retrieval (MIR) as well, particularly in music audio classification tasks such
as auto-tagging. In this paper, we present a two-stage learning model to
effectively predict multiple labels from music audio. The first stage learns to
project local spectral patterns of an audio track onto a high-dimensional
sparse space in an unsupervised manner and summarizes the audio track as a
bag-of-features. The second stage successively performs the unsupervised
learning on the bag-of-features in a layer-by-layer manner to initialize a deep
neural network and finally fine-tunes it with the tag labels. Through the
experiment, we rigorously examine training choices and tuning parameters, and
show that the model achieves high performance on Magnatagatune, a popularly
used dataset in music auto-tagging.Comment: We resubmit a new version to revive the paper and record it as a
technical report. We did not add any incremental work to the previous work
but removed out some sections (criticized by a review process) and polished
sentences accordingl
Towards Machine Intelligence
There exists a theory of a single general-purpose learning algorithm which
could explain the principles of its operation. This theory assumes that the
brain has some initial rough architecture, a small library of simple innate
circuits which are prewired at birth and proposes that all significant mental
algorithms can be learned. Given current understanding and observations, this
paper reviews and lists the ingredients of such an algorithm from both
architectural and functional perspectives.Comment: 10 pages, submitted to AGI-16. arXiv admin note: substantial text
overlap with arXiv:1512.0192
Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing
A typical image retrieval pipeline starts with the comparison of global
descriptors from a large database to find a short list of candidate matches. A
good image descriptor is key to the retrieval pipeline and should reconcile two
contradictory requirements: providing recall rates as high as possible and
being as compact as possible for fast matching. Following the recent successes
of Deep Convolutional Neural Networks (DCNN) for large scale image
classification, descriptors extracted from DCNNs are increasingly used in place
of the traditional hand crafted descriptors such as Fisher Vectors (FV) with
better retrieval performances. Nevertheless, the dimensionality of a typical
DCNN descriptor --extracted either from the visual feature pyramid or the
fully-connected layers-- remains quite high at several thousands of scalar
values. In this paper, we propose Unsupervised Triplet Hashing (UTH), a fully
unsupervised method to compute extremely compact binary hashes --in the 32-256
bits range-- from high-dimensional global descriptors. UTH consists of two
successive deep learning steps. First, Stacked Restricted Boltzmann Machines
(SRBM), a type of unsupervised deep neural nets, are used to learn binary
embedding functions able to bring the descriptor size down to the desired
bitrate. SRBMs are typically able to ensure a very high compression rate at the
expense of loosing some desirable metric properties of the original DCNN
descriptor space. Then, triplet networks, a rank learning scheme based on
weight sharing nets is used to fine-tune the binary embedding functions to
retain as much as possible of the useful metric properties of the original
space. A thorough empirical evaluation conducted on multiple publicly available
dataset using DCNN descriptors shows that our method is able to significantly
outperform state-of-the-art unsupervised schemes in the target bit range
DeepHash: Getting Regularization, Depth and Fine-Tuning Right
This work focuses on representing very high-dimensional global image
descriptors using very compact 64-1024 bit binary hashes for instance
retrieval. We propose DeepHash: a hashing scheme based on deep networks. Key to
making DeepHash work at extremely low bitrates are three important
considerations -- regularization, depth and fine-tuning -- each requiring
solutions specific to the hashing problem. In-depth evaluation shows that our
scheme consistently outperforms state-of-the-art methods across all data sets
for both Fisher Vectors and Deep Convolutional Neural Network features, by up
to 20 percent over other schemes. The retrieval performance with 256-bit hashes
is close to that of the uncompressed floating point features -- a remarkable
512 times compression
Learning Musical Relations using Gated Autoencoders
Music is usually highly structured and it is still an open question how to
design models which can successfully learn to recognize and represent musical
structure. A fundamental problem is that structurally related patterns can have
very distinct appearances, because the structural relationships are often based
on transformations of musical material, like chromatic or diatonic
transposition, inversion, retrograde, or rhythm change. In this preliminary
work, we study the potential of two unsupervised learning techniques -
Restricted Boltzmann Machines (RBMs) and Gated Autoencoders (GAEs) - to capture
pre-defined transformations from constructed data pairs. We evaluate the models
by using the learned representations as inputs in a discriminative task where
for a given type of transformation (e.g. diatonic transposition), the specific
relation between two musical patterns must be recognized (e.g. an upward
transposition of diatonic steps). Furthermore, we measure the reconstruction
error of models when reconstructing musical transformed patterns. Lastly, we
test the models in an analogy-making task. We find that it is difficult to
learn musical transformations with the RBM and that the GAE is much more
adequate for this task, since it is able to learn representations of specific
transformations that are largely content-invariant. We believe these results
show that models such as GAEs may provide the basis for more encompassing music
analysis systems, by endowing them with a better understanding of the
structures underlying music.Comment: In Proceedings of the 2nd Conference on Computer Simulation of
Musical Creativity (CSMC 2017
An exact mapping between the Variational Renormalization Group and Deep Learning
Deep learning is a broad set of techniques that uses multiple layers of
representation to automatically learn relevant features directly from
structured data. Recently, such techniques have yielded record-breaking results
on a diverse set of difficult machine learning tasks in computer vision, speech
recognition, and natural language processing. Despite the enormous success of
deep learning, relatively little is understood theoretically about why these
techniques are so successful at feature learning and compression. Here, we show
that deep learning is intimately related to one of the most important and
successful techniques in theoretical physics, the renormalization group (RG).
RG is an iterative coarse-graining scheme that allows for the extraction of
relevant features (i.e. operators) as a physical system is examined at
different length scales. We construct an exact mapping from the variational
renormalization group, first introduced by Kadanoff, and deep learning
architectures based on Restricted Boltzmann Machines (RBMs). We illustrate
these ideas using the nearest-neighbor Ising Model in one and two-dimensions.
Our results suggests that deep learning algorithms may be employing a
generalized RG-like scheme to learn relevant features from data.Comment: 8 pages, 3 figure
Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization
Learning to remember long sequences remains a challenging task for recurrent
neural networks. Register memory and attention mechanisms were both proposed to
resolve the issue with either high computational cost to retain memory
differentiability, or by discounting the RNN representation learning towards
encoding shorter local contexts than encouraging long sequence encoding.
Associative memory, which studies the compression of multiple patterns in a
fixed size memory, were rarely considered in recent years. Although some recent
work tries to introduce associative memory in RNN and mimic the energy decay
process in Hopfield nets, it inherits the shortcoming of rule-based memory
updates, and the memory capacity is limited. This paper proposes a method to
learn the memory update rule jointly with task objective to improve memory
capacity for remembering long sequences. Also, we propose an architecture that
uses multiple such associative memory for more complex input encoding. We
observed some interesting facts when compared to other RNN architectures on
some well-studied sequence learning tasks
Thinking Required
There exists a theory of a single general-purpose learning algorithm which
could explain the principles its operation. It assumes the initial rough
architecture, a small library of simple innate circuits which are prewired at
birth. and proposes that all significant mental algorithms are learned. Given
current understanding and observations, this paper reviews and lists the
ingredients of such an algorithm from architectural and functional
perspectives.Comment: 18 page
Decreasing the size of the Restricted Boltzmann machine
We propose a method to decrease the number of hidden units of the restricted
Boltzmann machine while avoiding decrease of the performance measured by the
Kullback-Leibler divergence. Then, we demonstrate our algorithm by using
numerical simulations
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