3,711 research outputs found
Robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks
Recent successes in deep learning have started to impact neuroscience. Of
particular significance are claims that current segmentation algorithms achieve
"super-human" accuracy in an area known as connectomics. However, as we will
show, these algorithms do not effectively generalize beyond the particular
source and brain tissues used for training -- severely limiting their usability
by the broader neuroscience community. To fill this gap, we describe a novel
connectomics challenge for source- and tissue-agnostic reconstruction of
neurons (STAR), which favors broad generalization over fitting specific
datasets. We first demonstrate that current state-of-the-art approaches to
neuron segmentation perform poorly on the challenge. We further describe a
novel convolutional recurrent neural network module that combines short-range
horizontal connections within a processing stage and long-range top-down
connections between stages. The resulting architecture establishes the state of
the art on the STAR challenge and represents a significant step towards widely
usable and fully-automated connectomics analysis
Describing Multimedia Content using Attention-based Encoder--Decoder Networks
Whereas deep neural networks were first mostly used for classification tasks,
they are rapidly expanding in the realm of structured output problems, where
the observed target is composed of multiple random variables that have a rich
joint distribution, given the input. We focus in this paper on the case where
the input also has a rich structure and the input and output structures are
somehow related. We describe systems that learn to attend to different places
in the input, for each element of the output, for a variety of tasks: machine
translation, image caption generation, video clip description and speech
recognition. All these systems are based on a shared set of building blocks:
gated recurrent neural networks and convolutional neural networks, along with
trained attention mechanisms. We report on experimental results with these
systems, showing impressively good performance and the advantage of the
attention mechanism.Comment: Submitted to IEEE Transactions on Multimedia Special Issue on Deep
Learning for Multimedia Computin
Leveraging Native Language Speech for Accent Identification using Deep Siamese Networks
The problem of automatic accent identification is important for several
applications like speaker profiling and recognition as well as for improving
speech recognition systems. The accented nature of speech can be primarily
attributed to the influence of the speaker's native language on the given
speech recording. In this paper, we propose a novel accent identification
system whose training exploits speech in native languages along with the
accented speech. Specifically, we develop a deep Siamese network-based model
which learns the association between accented speech recordings and the native
language speech recordings. The Siamese networks are trained with i-vector
features extracted from the speech recordings using either an unsupervised
Gaussian mixture model (GMM) or a supervised deep neural network (DNN) model.
We perform several accent identification experiments using the CSLU Foreign
Accented English (FAE) corpus. In these experiments, our proposed approach
using deep Siamese networks yield significant relative performance improvements
of 15.4 percent on a 10-class accent identification task, over a baseline
DNN-based classification system that uses GMM i-vectors. Furthermore, we
present a detailed error analysis of the proposed accent identification system.Comment: Published in ASRU 201
Neural network models and deep learning - a primer for biologists
Originally inspired by neurobiology, deep neural network models have become a
powerful tool of machine learning and artificial intelligence, where they are
used to approximate functions and dynamics by learning from examples. Here we
give a brief introduction to neural network models and deep learning for
biologists. We introduce feedforward and recurrent networks and explain the
expressive power of this modeling framework and the backpropagation algorithm
for setting the parameters. Finally, we consider how deep neural networks might
help us understand the brain's computations.Comment: 14 pages, 4 figures; added references, minor correction
Kafnets: kernel-based non-parametric activation functions for neural networks
Neural networks are generally built by interleaving (adaptable) linear layers
with (fixed) nonlinear activation functions. To increase their flexibility,
several authors have proposed methods for adapting the activation functions
themselves, endowing them with varying degrees of flexibility. None of these
approaches, however, have gained wide acceptance in practice, and research in
this topic remains open. In this paper, we introduce a novel family of flexible
activation functions that are based on an inexpensive kernel expansion at every
neuron. Leveraging over several properties of kernel-based models, we propose
multiple variations for designing and initializing these kernel activation
functions (KAFs), including a multidimensional scheme allowing to nonlinearly
combine information from different paths in the network. The resulting KAFs can
approximate any mapping defined over a subset of the real line, either convex
or nonconvex. Furthermore, they are smooth over their entire domain, linear in
their parameters, and they can be regularized using any known scheme, including
the use of penalties to enforce sparseness. To the best of our
knowledge, no other known model satisfies all these properties simultaneously.
In addition, we provide a relatively complete overview on alternative
techniques for adapting the activation functions, which is currently lacking in
the literature. A large set of experiments validates our proposal.Comment: Preprint submitted to Neural Networks (Elsevier
Deep Neural Networks for Choice Analysis: A Statistical Learning Theory Perspective
While researchers increasingly use deep neural networks (DNN) to analyze
individual choices, overfitting and interpretability issues remain as obstacles
in theory and practice. By using statistical learning theory, this study
presents a framework to examine the tradeoff between estimation and
approximation errors, and between prediction and interpretation losses. It
operationalizes the DNN interpretability in the choice analysis by formulating
the metrics of interpretation loss as the difference between true and estimated
choice probability functions. This study also uses the statistical learning
theory to upper bound the estimation error of both prediction and
interpretation losses in DNN, shedding light on why DNN does not have the
overfitting issue. Three scenarios are then simulated to compare DNN to binary
logit model (BNL). We found that DNN outperforms BNL in terms of both
prediction and interpretation for most of the scenarios, and larger sample size
unleashes the predictive power of DNN but not BNL. DNN is also used to analyze
the choice of trip purposes and travel modes based on the National Household
Travel Survey 2017 (NHTS2017) dataset. These experiments indicate that DNN can
be used for choice analysis beyond the current practice of demand forecasting
because it has the inherent utility interpretation, the flexibility of
accommodating various information formats, and the power of automatically
learning utility specification. DNN is both more predictive and interpretable
than BNL unless the modelers have complete knowledge about the choice task, and
the sample size is small. Overall, statistical learning theory can be a
foundation for future studies in the non-asymptotic data regime or using
high-dimensional statistical models in choice analysis, and the experiments
show the feasibility and effectiveness of DNN for its wide applications to
policy and behavioral analysis
Modeling Latent Attention Within Neural Networks
Deep neural networks are able to solve tasks across a variety of domains and
modalities of data. Despite many empirical successes, we lack the ability to
clearly understand and interpret the learned internal mechanisms that
contribute to such effective behaviors or, more critically, failure modes. In
this work, we present a general method for visualizing an arbitrary neural
network's inner mechanisms and their power and limitations. Our dataset-centric
method produces visualizations of how a trained network attends to components
of its inputs. The computed "attention masks" support improved interpretability
by highlighting which input attributes are critical in determining output. We
demonstrate the effectiveness of our framework on a variety of deep neural
network architectures in domains from computer vision, natural language
processing, and reinforcement learning. The primary contribution of our
approach is an interpretable visualization of attention that provides unique
insights into the network's underlying decision-making process irrespective of
the data modality
Modeling Time Series Similarity with Siamese Recurrent Networks
Traditional techniques for measuring similarities between time series are
based on handcrafted similarity measures, whereas more recent learning-based
approaches cannot exploit external supervision. We combine ideas from
time-series modeling and metric learning, and study siamese recurrent networks
(SRNs) that minimize a classification loss to learn a good similarity measure
between time series. Specifically, our approach learns a vectorial
representation for each time series in such a way that similar time series are
modeled by similar representations, and dissimilar time series by dissimilar
representations. Because it is a similarity prediction models, SRNs are
particularly well-suited to challenging scenarios such as signature
recognition, in which each person is a separate class and very few examples per
class are available. We demonstrate the potential merits of SRNs in
within-domain and out-of-domain classification experiments and in one-shot
learning experiments on tasks such as signature, voice, and sign language
recognition.Comment: 11 page
Fine-Grained Attention Mechanism for Neural Machine Translation
Neural machine translation (NMT) has been a new paradigm in machine
translation, and the attention mechanism has become the dominant approach with
the state-of-the-art records in many language pairs. While there are variants
of the attention mechanism, all of them use only temporal attention where one
scalar value is assigned to one context vector corresponding to a source word.
In this paper, we propose a fine-grained (or 2D) attention mechanism where each
dimension of a context vector will receive a separate attention score. In
experiments with the task of En-De and En-Fi translation, the fine-grained
attention method improves the translation quality in terms of BLEU score. In
addition, our alignment analysis reveals how the fine-grained attention
mechanism exploits the internal structure of context vectors.Comment: 9 pages, 4 figure
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain
Exponential growth in Electronic Healthcare Records (EHR) has resulted in new
opportunities and urgent needs for discovery of meaningful data-driven
representations and patterns of diseases in Computational Phenotyping research.
Deep Learning models have shown superior performance for robust prediction in
computational phenotyping tasks, but suffer from the issue of model
interpretability which is crucial for clinicians involved in decision-making.
In this paper, we introduce a novel knowledge-distillation approach called
Interpretable Mimic Learning, to learn interpretable phenotype features for
making robust prediction while mimicking the performance of deep learning
models. Our framework uses Gradient Boosting Trees to learn interpretable
features from deep learning models such as Stacked Denoising Autoencoder and
Long Short-Term Memory. Exhaustive experiments on a real-world clinical
time-series dataset show that our method obtains similar or better performance
than the deep learning models, and it provides interpretable phenotypes for
clinical decision making
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