4,160 research outputs found
Training Restricted Boltzmann Machines on Word Observations
The restricted Boltzmann machine (RBM) is a flexible tool for modeling
complex data, however there have been significant computational difficulties in
using RBMs to model high-dimensional multinomial observations. In natural
language processing applications, words are naturally modeled by K-ary discrete
distributions, where K is determined by the vocabulary size and can easily be
in the hundreds of thousands. The conventional approach to training RBMs on
word observations is limited because it requires sampling the states of K-way
softmax visible units during block Gibbs updates, an operation that takes time
linear in K. In this work, we address this issue by employing a more general
class of Markov chain Monte Carlo operators on the visible units, yielding
updates with computational complexity independent of K. We demonstrate the
success of our approach by training RBMs on hundreds of millions of word
n-grams using larger vocabularies than previously feasible and using the
learned features to improve performance on chunking and sentiment
classification tasks, achieving state-of-the-art results on the latter
Differentially Private Mixture of Generative Neural Networks
Generative models are used in a wide range of applications building on large
amounts of contextually rich information. Due to possible privacy violations of
the individuals whose data is used to train these models, however, publishing
or sharing generative models is not always viable. In this paper, we present a
novel technique for privately releasing generative models and entire
high-dimensional datasets produced by these models. We model the generator
distribution of the training data with a mixture of generative neural
networks. These are trained together and collectively learn the generator
distribution of a dataset. Data is divided into clusters, using a novel
differentially private kernel -means, then each cluster is given to separate
generative neural networks, such as Restricted Boltzmann Machines or
Variational Autoencoders, which are trained only on their own cluster using
differentially private gradient descent. We evaluate our approach using the
MNIST dataset, as well as call detail records and transit datasets, showing
that it produces realistic synthetic samples, which can also be used to
accurately compute arbitrary number of counting queries.Comment: A shorter version of this paper appeared at the 17th IEEE
International Conference on Data Mining (ICDM 2017). This is the full
version, published in IEEE Transactions on Knowledge and Data Engineering
(TKDE
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
Recent studies have shown that synaptic unreliability is a robust and
sufficient mechanism for inducing the stochasticity observed in cortex. Here,
we introduce Synaptic Sampling Machines, a class of neural network models that
uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised
learning. Similar to the original formulation of Boltzmann machines, these
models can be viewed as a stochastic counterpart of Hopfield networks, but
where stochasticity is induced by a random mask over the connections. Synaptic
stochasticity plays the dual role of an efficient mechanism for sampling, and a
regularizer during learning akin to DropConnect. A local synaptic plasticity
rule implementing an event-driven form of contrastive divergence enables the
learning of generative models in an on-line fashion. Synaptic sampling machines
perform equally well using discrete-timed artificial units (as in Hopfield
networks) or continuous-timed leaky integrate & fire neurons. The learned
representations are remarkably sparse and robust to reductions in bit precision
and synapse pruning: removal of more than 75% of the weakest connections
followed by cursory re-learning causes a negligible performance loss on
benchmark classification tasks. The spiking neuron-based synaptic sampling
machines outperform existing spike-based unsupervised learners, while
potentially offering substantial advantages in terms of power and complexity,
and are thus promising models for on-line learning in brain-inspired hardware
Neural-Network Quantum States, String-Bond States, and Chiral Topological States
Neural-Network Quantum States have been recently introduced as an Ansatz for
describing the wave function of quantum many-body systems. We show that there
are strong connections between Neural-Network Quantum States in the form of
Restricted Boltzmann Machines and some classes of Tensor-Network states in
arbitrary dimensions. In particular we demonstrate that short-range Restricted
Boltzmann Machines are Entangled Plaquette States, while fully connected
Restricted Boltzmann Machines are String-Bond States with a nonlocal geometry
and low bond dimension. These results shed light on the underlying architecture
of Restricted Boltzmann Machines and their efficiency at representing many-body
quantum states. String-Bond States also provide a generic way of enhancing the
power of Neural-Network Quantum States and a natural generalization to systems
with larger local Hilbert space. We compare the advantages and drawbacks of
these different classes of states and present a method to combine them
together. This allows us to benefit from both the entanglement structure of
Tensor Networks and the efficiency of Neural-Network Quantum States into a
single Ansatz capable of targeting the wave function of strongly correlated
systems. While it remains a challenge to describe states with chiral
topological order using traditional Tensor Networks, we show that
Neural-Network Quantum States and their String-Bond States extension can
describe a lattice Fractional Quantum Hall state exactly. In addition, we
provide numerical evidence that Neural-Network Quantum States can approximate a
chiral spin liquid with better accuracy than Entangled Plaquette States and
local String-Bond States. Our results demonstrate the efficiency of neural
networks to describe complex quantum wave functions and pave the way towards
the use of String-Bond States as a tool in more traditional machine-learning
applications.Comment: 15 pages, 7 figure
Crime incidents embedding using restricted Boltzmann machines
We present a new approach for detecting related crime series, by unsupervised
learning of the latent feature embeddings from narratives of crime record via
the Gaussian-Bernoulli Restricted Boltzmann Machines (RBM). This is a
drastically different approach from prior work on crime analysis, which
typically considers only time and location and at most category information.
After the embedding, related cases are closer to each other in the Euclidean
feature space, and the unrelated cases are far apart, which is a good property
can enable subsequent analysis such as detection and clustering of related
cases. Experiments over several series of related crime incidents hand labeled
by the Atlanta Police Department reveal the promise of our embedding methods.Comment: 5 pages, 3 figure
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