224,371 research outputs found
Introduction to a system for implementing neural net connections on SIMD architectures
Neural networks have attracted much interest recently, and using parallel architectures to simulate neural networks is a natural and necessary application. The SIMD model of parallel computation is chosen, because systems of this type can be built with large numbers of processing elements. However, such systems are not naturally suited to generalized communication. A method is proposed that allows an implementation of neural network connections on massively parallel SIMD architectures. The key to this system is an algorithm permitting the formation of arbitrary connections between the neurons. A feature is the ability to add new connections quickly. It also has error recovery ability and is robust over a variety of network topologies. Simulations of the general connection system, and its implementation on the Connection Machine, indicate that the time and space requirements are proportional to the product of the average number of connections per neuron and the diameter of the interconnection network
Synthetic Quantum Systems
So far proposed quantum computers use fragile and environmentally sensitive
natural quantum systems. Here we explore the new notion that synthetic quantum
systems suitable for quantum computation may be fabricated from smart
nanostructures using topological excitations of a stochastic neural-type
network that can mimic natural quantum systems. These developments are a
technological application of process physics which is an information theory of
reality in which space and quantum phenomena are emergent, and so indicates the
deep origins of quantum phenomena. Analogous complex stochastic dynamical
systems have recently been proposed within neurobiology to deal with the
emergent complexity of biosystems, particularly the biodynamics of higher brain
function. The reasons for analogous discoveries in fundamental physics and
neurobiology are discussed.Comment: 16 pages, Latex, 1 eps figure fil
Rate-Distortion Classification for Self-Tuning IoT Networks
Many future wireless sensor networks and the Internet of Things are expected
to follow a software defined paradigm, where protocol parameters and behaviors
will be dynamically tuned as a function of the signal statistics. New protocols
will be then injected as a software as certain events occur. For instance, new
data compressors could be (re)programmed on-the-fly as the monitored signal
type or its statistical properties change. We consider a lossy compression
scenario, where the application tolerates some distortion of the gathered
signal in return for improved energy efficiency. To reap the full benefits of
this paradigm, we discuss an automatic sensor profiling approach where the
signal class, and in particular the corresponding rate-distortion curve, is
automatically assessed using machine learning tools (namely, support vector
machines and neural networks). We show that this curve can be reliably
estimated on-the-fly through the computation of a small number (from ten to
twenty) of statistical features on time windows of a few hundreds samples
Optimization of FPGA Based Neural Network Processor
Neural information processing is an emerging new field, providing an alternative
form of computation for demanding tasks such as pattern recognition problems
which are usually reserved for human attention. Neural network computation i s
sought after where classification of input data is difficult to be worked out using
equations or sets of rules.
Technological advances in integrated circuits such as Field Programmable Gate
Array (FPGA) systems have made it easier to develop and implement hardware
devices based on these neural network architectures. The motivation in hardware
implementation of neural networks is its fast processing speed and suitability in
parallel and pipelined processing.
The project revolves around the design of an optimized neural network processor.
The processor design is based on the feedforward network architecture type with
BackPropagation trained weights for the Exclusive-OR non-linear problem.
Among the highlights of the project is the improvement in neural network
architecture through reconfigurable and recursive computation of a single hidden
layer for multiple layer applications. Improvements in processor organization were
also made which enables the design to parallel process with similar processors.
Other improvements include design considerations to reduce the amount of logic
required for implementation without much sacrifice of processing speed
Large-Scale Optical Neural Networks based on Photoelectric Multiplication
Recent success in deep neural networks has generated strong interest in
hardware accelerators to improve speed and energy consumption. This paper
presents a new type of photonic accelerator based on coherent detection that is
scalable to large () networks and can be operated at high (GHz)
speeds and very low (sub-aJ) energies per multiply-and-accumulate (MAC), using
the massive spatial multiplexing enabled by standard free-space optical
components. In contrast to previous approaches, both weights and inputs are
optically encoded so that the network can be reprogrammed and trained on the
fly. Simulations of the network using models for digit- and
image-classification reveal a "standard quantum limit" for optical neural
networks, set by photodetector shot noise. This bound, which can be as low as
50 zJ/MAC, suggests performance below the thermodynamic (Landauer) limit for
digital irreversible computation is theoretically possible in this device. The
proposed accelerator can implement both fully-connected and convolutional
networks. We also present a scheme for back-propagation and training that can
be performed in the same hardware. This architecture will enable a new class of
ultra-low-energy processors for deep learning.Comment: Text: 10 pages, 5 figures, 1 table. Supplementary: 8 pages, 5,
figures, 2 table
Interpolation Consistency Training for Semi-Supervised Learning
We introduce Interpolation Consistency Training (ICT), a simple and
computation efficient algorithm for training Deep Neural Networks in the
semi-supervised learning paradigm. ICT encourages the prediction at an
interpolation of unlabeled points to be consistent with the interpolation of
the predictions at those points. In classification problems, ICT moves the
decision boundary to low-density regions of the data distribution. Our
experiments show that ICT achieves state-of-the-art performance when applied to
standard neural network architectures on the CIFAR-10 and SVHN benchmark
datasets. Our theoretical analysis shows that ICT corresponds to a certain type
of data-adaptive regularization with unlabeled points which reduces overfitting
to labeled points under high confidence values.Comment: Extended version of IJCAI 2019 paper. Semi-supervised Learning, Deep
Learning, Neural Networks. All the previous results are unchanged; we added
new theoretical and empirical result
Estimating Neural Reflectance Field from Radiance Field using Tree Structures
We present a new method for estimating the Neural Reflectance Field (NReF) of
an object from a set of posed multi-view images under unknown lighting. NReF
represents 3D geometry and appearance of objects in a disentangled manner, and
are hard to be estimated from images only. Our method solves this problem by
exploiting the Neural Radiance Field (NeRF) as a proxy representation, from
which we perform further decomposition. A high-quality NeRF decomposition
relies on good geometry information extraction as well as good prior terms to
properly resolve ambiguities between different components. To extract
high-quality geometry information from radiance fields, we re-design a new
ray-casting based method for surface point extraction. To efficiently compute
and apply prior terms, we convert different prior terms into different type of
filter operations on the surface extracted from radiance field. We then employ
two type of auxiliary data structures, namely Gaussian KD-tree and octree, to
support fast querying of surface points and efficient computation of surface
filters during training. Based on this, we design a multi-stage decomposition
optimization pipeline for estimating neural reflectance field from neural
radiance fields. Extensive experiments show our method outperforms other
state-of-the-art methods on different data, and enable high-quality free-view
relighting as well as material editing tasks
Adaptive Computation with Elastic Input Sequence
Humans have the ability to adapt the type of information they use, the
procedure they employ, and the amount of time they spend when solving problems.
However, most standard neural networks have a fixed function type and
computation budget regardless of the sample's nature or difficulty. Adaptivity
is a powerful paradigm as it not only imbues practitioners with flexibility
pertaining to the downstream usage of these models but can also serve as a
powerful inductive bias for solving certain challenging classes of problems. In
this work, we introduce a new approach called AdaTape, which allows for dynamic
computation in neural networks through adaptive tape tokens. AdaTape utilizes
an elastic input sequence by equipping an architecture with a dynamic
read-and-write tape. Specifically, we adaptively generate input sequences using
tape tokens obtained from a tape bank which can be either trainable or derived
from input data. We examine the challenges and requirements to obtain dynamic
sequence content and length, and propose the Adaptive Tape Reading (ATR)
algorithm to achieve both goals. Through extensive experiments on image
recognition tasks, we show that AdaTape can achieve better performance while
maintaining the computational cost. To facilitate further research, we have
released code at https://github.com/google-research/scenic
Combining Feature Selection and Integration—A Neural Model for MT Motion Selectivity
Background: The computation of pattern motion in visual area MT based on motion input from area V1 has been investigated in many experiments and models attempting to replicate the main mechanisms. Two different core conceptual approaches were developed to explain the findings. In integrationist models the key mechanism to achieve pattern selectivity is the nonlinear integration of V1 motion activity. In contrast, selectionist models focus on the motion computation at positions with 2D features. Methodology/Principal Findings: Recent experiments revealed that neither of the two concepts alone is sufficient to explain all experimental data and that most of the existing models cannot account for the complex behaviour found. MT pattern selectivity changes over time for stimuli like type II plaids from vector average to the direction computed with an intersection of constraint rule or by feature tracking. Also, the spatial arrangement of the stimulus within the receptive field of a MT cell plays a crucial role. We propose a recurrent neural model showing how feature integration and selection can be combined into one common architecture to explain these findings. The key features of the model are the computation of 1D and 2D motion in model area V1 subpopulations that are integrated in model MT cells using feedforward and feedback processing. Our results are also in line with findings concerning the solution of the aperture problem. Conclusions/Significance: We propose a new neural model for MT pattern computation and motion disambiguation that i
Globally Normalized Reader
Rapid progress has been made towards question answering (QA) systems that can
extract answers from text. Existing neural approaches make use of expensive
bi-directional attention mechanisms or score all possible answer spans,
limiting scalability. We propose instead to cast extractive QA as an iterative
search problem: select the answer's sentence, start word, and end word. This
representation reduces the space of each search step and allows computation to
be conditionally allocated to promising search paths. We show that globally
normalizing the decision process and back-propagating through beam search makes
this representation viable and learning efficient. We empirically demonstrate
the benefits of this approach using our model, Globally Normalized Reader
(GNR), which achieves the second highest single model performance on the
Stanford Question Answering Dataset (68.4 EM, 76.21 F1 dev) and is 24.7x faster
than bi-attention-flow. We also introduce a data-augmentation method to produce
semantically valid examples by aligning named entities to a knowledge base and
swapping them with new entities of the same type. This method improves the
performance of all models considered in this work and is of independent
interest for a variety of NLP tasks.Comment: Presented at EMNLP 201
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