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100 | Monitoring Term Drift Based on Semantic Consistency in an Evolving
Vector Field | cs.CL | Based on the Aristotelian concept of potentiality vs. actuality allowing for
the study of energy and dynamics in language, we propose a field approach to
lexical analysis. Falling back on the distributional hypothesis to
statistically model word meaning, we used evolving fields as a metaphor to
express time-dependent c... | computer science |
101 | Towards better decoding and language model integration in sequence to
sequence models | cs.NE | The recently proposed Sequence-to-Sequence (seq2seq) framework advocates
replacing complex data processing pipelines, such as an entire automatic speech
recognition system, with a single neural network trained in an end-to-end
fashion. In this contribution, we analyse an attention-based seq2seq speech
recognition syste... | computer science |
102 | Neural Machine Translation by Jointly Learning to Align and Translate | cs.CL | Neural machine translation is a recently proposed approach to machine
translation. Unlike the traditional statistical machine translation, the neural
machine translation aims at building a single neural network that can be
jointly tuned to maximize the translation performance. The models proposed
recently for neural ma... | computer science |
103 | Overcoming the Curse of Sentence Length for Neural Machine Translation
using Automatic Segmentation | cs.CL | The authors of (Cho et al., 2014a) have shown that the recently introduced
neural network translation systems suffer from a significant drop in
translation quality when translating long sentences, unlike existing
phrase-based translation systems. In this paper, we propose a way to address
this issue by automatically se... | computer science |
104 | Transferring Knowledge from a RNN to a DNN | cs.LG | Deep Neural Network (DNN) acoustic models have yielded many state-of-the-art
results in Automatic Speech Recognition (ASR) tasks. More recently, Recurrent
Neural Network (RNN) models have been shown to outperform DNNs counterparts.
However, state-of-the-art DNN and RNN models tend to be impractical to deploy
on embedde... | computer science |
105 | Correlational Neural Networks | cs.CL | Common Representation Learning (CRL), wherein different descriptions (or
views) of the data are embedded in a common subspace, is receiving a lot of
attention recently. Two popular paradigms here are Canonical Correlation
Analysis (CCA) based approaches and Autoencoder (AE) based approaches. CCA
based approaches learn ... | computer science |
106 | Attention-Based Models for Speech Recognition | cs.CL | Recurrent sequence generators conditioned on input data through an attention
mechanism have recently shown very good performance on a range of tasks in-
cluding machine translation, handwriting synthesis and image caption gen-
eration. We extend the attention-mechanism with features needed for speech
recognition. We sh... | computer science |
107 | Fast and Accurate Recurrent Neural Network Acoustic Models for Speech
Recognition | cs.CL | We have recently shown that deep Long Short-Term Memory (LSTM) recurrent
neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as
acoustic models for speech recognition. More recently, we have shown that the
performance of sequence trained context dependent (CD) hidden Markov model
(HMM) acoustic m... | computer science |
108 | Listen, Attend and Spell | cs.CL | We present Listen, Attend and Spell (LAS), a neural network that learns to
transcribe speech utterances to characters. Unlike traditional DNN-HMM models,
this model learns all the components of a speech recognizer jointly. Our system
has two components: a listener and a speller. The listener is a pyramidal
recurrent ne... | computer science |
109 | BlackOut: Speeding up Recurrent Neural Network Language Models With Very
Large Vocabularies | cs.LG | We propose BlackOut, an approximation algorithm to efficiently train massive
recurrent neural network language models (RNNLMs) with million word
vocabularies. BlackOut is motivated by using a discriminative loss, and we
describe a new sampling strategy which significantly reduces computation while
improving stability, ... | computer science |
110 | Character-based Neural Machine Translation | cs.CL | Neural Machine Translation (MT) has reached state-of-the-art results.
However, one of the main challenges that neural MT still faces is dealing with
very large vocabularies and morphologically rich languages. In this paper, we
propose a neural MT system using character-based embeddings in combination with
convolutional... | computer science |
111 | A Latent Variable Recurrent Neural Network for Discourse Relation
Language Models | cs.CL | This paper presents a novel latent variable recurrent neural network
architecture for jointly modeling sequences of words and (possibly latent)
discourse relations between adjacent sentences. A recurrent neural network
generates individual words, thus reaping the benefits of
discriminatively-trained vector representati... | computer science |
112 | Multi-task Recurrent Model for Speech and Speaker Recognition | cs.CL | Although highly correlated, speech and speaker recognition have been regarded
as two independent tasks and studied by two communities. This is certainly not
the way that people behave: we decipher both speech content and speaker traits
at the same time. This paper presents a unified model to perform speech and
speaker ... | computer science |
113 | Hierarchical Memory Networks | stat.ML | Memory networks are neural networks with an explicit memory component that
can be both read and written to by the network. The memory is often addressed
in a soft way using a softmax function, making end-to-end training with
backpropagation possible. However, this is not computationally scalable for
applications which ... | computer science |
114 | Sequence-to-Sequence Learning as Beam-Search Optimization | cs.CL | Sequence-to-Sequence (seq2seq) modeling has rapidly become an important
general-purpose NLP tool that has proven effective for many text-generation and
sequence-labeling tasks. Seq2seq builds on deep neural language modeling and
inherits its remarkable accuracy in estimating local, next-word distributions.
In this work... | computer science |
115 | Grounded Recurrent Neural Networks | stat.ML | In this work, we present the Grounded Recurrent Neural Network (GRNN), a
recurrent neural network architecture for multi-label prediction which
explicitly ties labels to specific dimensions of the recurrent hidden state (we
call this process "grounding"). The approach is particularly well-suited for
extracting large nu... | computer science |
116 | Latent Intention Dialogue Models | cs.CL | Developing a dialogue agent that is capable of making autonomous decisions
and communicating by natural language is one of the long-term goals of machine
learning research. Traditional approaches either rely on hand-crafting a small
state-action set for applying reinforcement learning that is not scalable or
constructi... | computer science |
117 | Transfer Learning for Speech Recognition on a Budget | cs.LG | End-to-end training of automated speech recognition (ASR) systems requires
massive data and compute resources. We explore transfer learning based on model
adaptation as an approach for training ASR models under constrained GPU memory,
throughput and training data. We conduct several systematic experiments
adapting a Wa... | computer science |
118 | Optimizing expected word error rate via sampling for speech recognition | cs.CL | State-level minimum Bayes risk (sMBR) training has become the de facto
standard for sequence-level training of speech recognition acoustic models. It
has an elegant formulation using the expectation semiring, and gives large
improvements in word error rate (WER) over models trained solely using
cross-entropy (CE) or co... | computer science |
119 | Neural Networks Compression for Language Modeling | stat.ML | In this paper, we consider several compression techniques for the language
modeling problem based on recurrent neural networks (RNNs). It is known that
conventional RNNs, e.g, LSTM-based networks in language modeling, are
characterized with either high space complexity or substantial inference time.
This problem is esp... | computer science |
120 | Avoiding Your Teacher's Mistakes: Training Neural Networks with
Controlled Weak Supervision | cs.LG | Training deep neural networks requires massive amounts of training data, but
for many tasks only limited labeled data is available. This makes weak
supervision attractive, using weak or noisy signals like the output of
heuristic methods or user click-through data for training. In a semi-supervised
setting, we can use a... | computer science |
121 | Uncertainty Estimates for Efficient Neural Network-based Dialogue Policy
Optimisation | stat.ML | In statistical dialogue management, the dialogue manager learns a policy that
maps a belief state to an action for the system to perform. Efficient
exploration is key to successful policy optimisation. Current deep
reinforcement learning methods are very promising but rely on epsilon-greedy
exploration, thus subjecting... | computer science |
122 | On Extended Long Short-term Memory and Dependent Bidirectional Recurrent
Neural Network | cs.LG | In this work, we investigate the memory capability of recurrent neural
networks (RNNs), where this capability is defined as a function that maps an
element in a sequence to the current output. We first analyze the system
function of a recurrent neural network (RNN) cell, and provide analytical
results for three RNNs. T... | computer science |
123 | Learning to Answer Questions From Image Using Convolutional Neural
Network | cs.CL | In this paper, we propose to employ the convolutional neural network (CNN)
for the image question answering (QA). Our proposed CNN provides an end-to-end
framework with convolutional architectures for learning not only the image and
question representations, but also their inter-modal interactions to produce
the answer... | computer science |
124 | Stacked Attention Networks for Image Question Answering | cs.LG | This paper presents stacked attention networks (SANs) that learn to answer
natural language questions from images. SANs use semantic representation of a
question as query to search for the regions in an image that are related to the
answer. We argue that image question answering (QA) often requires multiple
steps of re... | computer science |
125 | Neural Module Networks | cs.CV | Visual question answering is fundamentally compositional in nature---a
question like "where is the dog?" shares substructure with questions like "what
color is the dog?" and "where is the cat?" This paper seeks to simultaneously
exploit the representational capacity of deep networks and the compositional
linguistic str... | computer science |
126 | Symbol Grounding Association in Multimodal Sequences with Missing
Elements | cs.CV | In this paper, we extend a symbolic association framework for being able to
handle missing elements in multimodal sequences. The general scope of the work
is the symbolic associations of object-word mappings as it happens in language
development in infants. In other words, two different representations of the
same abst... | computer science |
127 | Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe
Noise | cs.LG | The growing importance of massive datasets with the advent of deep learning
makes robustness to label noise a critical property for classifiers to have.
Sources of label noise include automatic labeling for large datasets,
non-expert labeling, and label corruption by data poisoning adversaries. In the
latter case, corr... | computer science |
128 | Describing Multimedia Content using Attention-based Encoder--Decoder
Networks | cs.NE | 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... | computer science |
129 | Multilingual Image Description with Neural Sequence Models | cs.CL | In this paper we present an approach to multi-language image description
bringing together insights from neural machine translation and neural image
description. To create a description of an image for a given target language,
our sequence generation models condition on feature vectors from the image, the
description f... | computer science |
130 | Deep Embedding for Spatial Role Labeling | cs.CL | This paper introduces the visually informed embedding of word (VIEW), a
continuous vector representation for a word extracted from a deep neural model
trained using the Microsoft COCO data set to forecast the spatial arrangements
between visual objects, given a textual description. The model is composed of a
deep multi... | computer science |
131 | Image-to-Markup Generation with Coarse-to-Fine Attention | cs.CV | We present a neural encoder-decoder model to convert images into
presentational markup based on a scalable coarse-to-fine attention mechanism.
Our method is evaluated in the context of image-to-LaTeX generation, and we
introduce a new dataset of real-world rendered mathematical expressions paired
with LaTeX markup. We ... | computer science |
132 | Teaching Machines to Code: Neural Markup Generation with Visual
Attention | cs.LG | We present a deep recurrent neural network model with soft visual attention
that learns to generate LaTeX markup of real-world math formulas given their
images. Applying neural sequence generation techniques that have been very
successful in the fields of machine translation and image/handwriting/speech
captioning, rec... | computer science |
133 | Evolution in Groups: A deeper look at synaptic cluster driven evolution
of deep neural networks | cs.NE | A promising paradigm for achieving highly efficient deep neural networks is
the idea of evolutionary deep intelligence, which mimics biological evolution
processes to progressively synthesize more efficient networks. A crucial design
factor in evolutionary deep intelligence is the genetic encoding scheme used to
simula... | computer science |
134 | Mesh Learning for Classifying Cognitive Processes | cs.NE | A relatively recent advance in cognitive neuroscience has been multi-voxel
pattern analysis (MVPA), which enables researchers to decode brain states
and/or the type of information represented in the brain during a cognitive
operation. MVPA methods utilize machine learning algorithms to distinguish
among types of inform... | computer science |
135 | Synthesizing Deep Neural Network Architectures using Biological Synaptic
Strength Distributions | cs.NE | In this work, we perform an exploratory study on synthesizing deep neural
networks using biological synaptic strength distributions, and the potential
influence of different distributions on modelling performance particularly for
the scenario associated with small data sets. Surprisingly, a CNN with
convolutional layer... | computer science |
136 | A PSO and Pattern Search based Memetic Algorithm for SVMs Parameters
Optimization | cs.LG | Addressing the issue of SVMs parameters optimization, this study proposes an
efficient memetic algorithm based on Particle Swarm Optimization algorithm
(PSO) and Pattern Search (PS). In the proposed memetic algorithm, PSO is
responsible for exploration of the search space and the detection of the
potential regions with... | computer science |
137 | Density estimation using Real NVP | cs.LG | Unsupervised learning of probabilistic models is a central yet challenging
problem in machine learning. Specifically, designing models with tractable
learning, sampling, inference and evaluation is crucial in solving this task.
We extend the space of such models using real-valued non-volume preserving
(real NVP) transf... | computer science |
138 | Evolution Strategies as a Scalable Alternative to Reinforcement Learning | stat.ML | We explore the use of Evolution Strategies (ES), a class of black box
optimization algorithms, as an alternative to popular MDP-based RL techniques
such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show
that ES is a viable solution strategy that scales extremely well with the
number of CPUs avail... | computer science |
139 | QMDP-Net: Deep Learning for Planning under Partial Observability | cs.AI | This paper introduces the QMDP-net, a neural network architecture for
planning under partial observability. The QMDP-net combines the strengths of
model-free learning and model-based planning. It is a recurrent policy network,
but it represents a policy for a parameterized set of tasks by connecting a
model with a plan... | computer science |
140 | TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep
Reinforcement Learning | cs.AI | Combining deep model-free reinforcement learning with on-line planning is a
promising approach to building on the successes of deep RL. On-line planning
with look-ahead trees has proven successful in environments where transition
models are known a priori. However, in complex environments where transition
models need t... | computer science |
141 | Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent
Networks | cs.AI | A major drawback of backpropagation through time (BPTT) is the difficulty of
learning long-term dependencies, coming from having to propagate credit
information backwards through every single step of the forward computation.
This makes BPTT both computationally impractical and biologically implausible.
For this reason,... | computer science |
142 | Stochastic Deep Learning in Memristive Networks | stat.ML | We study the performance of stochastically trained deep neural networks
(DNNs) whose synaptic weights are implemented using emerging memristive devices
that exhibit limited dynamic range, resolution, and variability in their
programming characteristics. We show that a key device parameter to optimize
the learning effic... | computer science |
143 | PSO-MISMO Modeling Strategy for Multi-Step-Ahead Time Series Prediction | cs.AI | Multi-step-ahead time series prediction is one of the most challenging
research topics in the field of time series modeling and prediction, and is
continually under research. Recently, the multiple-input several
multiple-outputs (MISMO) modeling strategy has been proposed as a promising
alternative for multi-step-ahead... | computer science |
144 | Norm-Based Capacity Control in Neural Networks | cs.LG | We investigate the capacity, convexity and characterization of a general
family of norm-constrained feed-forward networks. | computer science |
145 | Improving the Performance of Neural Networks in Regression Tasks Using
Drawering | cs.LG | The method presented extends a given regression neural network to make its
performance improve. The modification affects the learning procedure only,
hence the extension may be easily omitted during evaluation without any change
in prediction. It means that the modified model may be evaluated as quickly as
the original... | computer science |
146 | Learning unbiased features | cs.LG | A key element in transfer learning is representation learning; if
representations can be developed that expose the relevant factors underlying
the data, then new tasks and domains can be learned readily based on mappings
of these salient factors. We propose that an important aim for these
representations are to be unbi... | computer science |
147 | Compatible Value Gradients for Reinforcement Learning of Continuous Deep
Policies | cs.LG | This paper proposes GProp, a deep reinforcement learning algorithm for
continuous policies with compatible function approximation. The algorithm is
based on two innovations. Firstly, we present a temporal-difference based
method for learning the gradient of the value-function. Secondly, we present
the deviator-actor-cr... | computer science |
148 | Learning dynamic Boltzmann machines with spike-timing dependent
plasticity | cs.NE | We propose a particularly structured Boltzmann machine, which we refer to as
a dynamic Boltzmann machine (DyBM), as a stochastic model of a
multi-dimensional time-series. The DyBM can have infinitely many layers of
units but allows exact and efficient inference and learning when its parameters
have a proposed structure... | computer science |
149 | Gated Graph Sequence Neural Networks | cs.LG | Graph-structured data appears frequently in domains including chemistry,
natural language semantics, social networks, and knowledge bases. In this work,
we study feature learning techniques for graph-structured inputs. Our starting
point is previous work on Graph Neural Networks (Scarselli et al., 2009), which
we modif... | computer science |
150 | Deep Reinforcement Learning in Large Discrete Action Spaces | cs.AI | Being able to reason in an environment with a large number of discrete
actions is essential to bringing reinforcement learning to a larger class of
problems. Recommender systems, industrial plants and language models are only
some of the many real-world tasks involving large numbers of discrete actions
for which curren... | computer science |
151 | Value Iteration Networks | cs.AI | We introduce the value iteration network (VIN): a fully differentiable neural
network with a `planning module' embedded within. VINs can learn to plan, and
are suitable for predicting outcomes that involve planning-based reasoning,
such as policies for reinforcement learning. Key to our approach is a novel
differentiab... | computer science |
152 | Recurrent Orthogonal Networks and Long-Memory Tasks | cs.NE | Although RNNs have been shown to be powerful tools for processing sequential
data, finding architectures or optimization strategies that allow them to model
very long term dependencies is still an active area of research. In this work,
we carefully analyze two synthetic datasets originally outlined in (Hochreiter
and S... | computer science |
153 | Learning values across many orders of magnitude | cs.LG | Most learning algorithms are not invariant to the scale of the function that
is being approximated. We propose to adaptively normalize the targets used in
learning. This is useful in value-based reinforcement learning, where the
magnitude of appropriate value approximations can change over time when we
update the polic... | computer science |
154 | Genetic Architect: Discovering Genomic Structure with Learned Neural
Architectures | cs.LG | Each human genome is a 3 billion base pair set of encoding instructions.
Decoding the genome using deep learning fundamentally differs from most tasks,
as we do not know the full structure of the data and therefore cannot design
architectures to suit it. As such, architectures that fit the structure of
genomics should ... | computer science |
155 | Deep Successor Reinforcement Learning | stat.ML | Learning robust value functions given raw observations and rewards is now
possible with model-free and model-based deep reinforcement learning
algorithms. There is a third alternative, called Successor Representations
(SR), which decomposes the value function into two components -- a reward
predictor and a successor ma... | computer science |
156 | RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning | cs.AI | Deep reinforcement learning (deep RL) has been successful in learning
sophisticated behaviors automatically; however, the learning process requires a
huge number of trials. In contrast, animals can learn new tasks in just a few
trials, benefiting from their prior knowledge about the world. This paper seeks
to bridge th... | computer science |
157 | Capacity and Trainability in Recurrent Neural Networks | stat.ML | Two potential bottlenecks on the expressiveness of recurrent neural networks
(RNNs) are their ability to store information about the task in their
parameters, and to store information about the input history in their units. We
show experimentally that all common RNN architectures achieve nearly the same
per-task and pe... | computer science |
158 | Causal Regularization | cs.LG | In application domains such as healthcare, we want accurate predictive models
that are also causally interpretable. In pursuit of such models, we propose a
causal regularizer to steer predictive models towards causally-interpretable
solutions and theoretically study its properties. In a large-scale analysis of
Electron... | computer science |
159 | On the Behavior of Convolutional Nets for Feature Extraction | cs.NE | Deep neural networks are representation learning techniques. During training,
a deep net is capable of generating a descriptive language of unprecedented
size and detail in machine learning. Extracting the descriptive language coded
within a trained CNN model (in the case of image data), and reusing it for
other purpos... | computer science |
160 | Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in
Generative Models | cs.LG | Adversarial learning of probabilistic models has recently emerged as a
promising alternative to maximum likelihood. Implicit models such as generative
adversarial networks (GAN) often generate better samples compared to explicit
models trained by maximum likelihood. Yet, GANs sidestep the characterization
of an explici... | computer science |
161 | Filtering Variational Objectives | cs.LG | When used as a surrogate objective for maximum likelihood estimation in
latent variable models, the evidence lower bound (ELBO) produces
state-of-the-art results. Inspired by this, we consider the extension of the
ELBO to a family of lower bounds defined by a particle filter's estimator of
the marginal likelihood, the ... | computer science |
162 | Kernel Implicit Variational Inference | stat.ML | Recent progress in variational inference has paid much attention to the
flexibility of variational posteriors. One promising direction is to use
implicit distributions, i.e., distributions without tractable densities as the
variational posterior. However, existing methods on implicit posteriors still
face challenges of... | computer science |
163 | Non-Markovian Control with Gated End-to-End Memory Policy Networks | stat.ML | Partially observable environments present an important open challenge in the
domain of sequential control learning with delayed rewards. Despite numerous
attempts during the two last decades, the majority of reinforcement learning
algorithms and associated approximate models, applied to this context, still
assume Marko... | computer science |
164 | Automated Problem Identification: Regression vs Classification via
Evolutionary Deep Networks | cs.NE | Regression or classification? This is perhaps the most basic question faced
when tackling a new supervised learning problem. We present an Evolutionary
Deep Learning (EDL) algorithm that automatically solves this by identifying the
question type with high accuracy, along with a proposed deep architecture.
Typically, a ... | computer science |
165 | A Simple Neural Attentive Meta-Learner | cs.AI | Deep neural networks excel in regimes with large amounts of data, but tend to
struggle when data is scarce or when they need to adapt quickly to changes in
the task. In response, recent work in meta-learning proposes training a
meta-learner on a distribution of similar tasks, in the hopes of generalization
to novel but... | computer science |
166 | Kafnets: kernel-based non-parametric activation functions for neural
networks | stat.ML | 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, h... | computer science |
167 | Learning model-based planning from scratch | cs.AI | Conventional wisdom holds that model-based planning is a powerful approach to
sequential decision-making. It is often very challenging in practice, however,
because while a model can be used to evaluate a plan, it does not prescribe how
to construct a plan. Here we introduce the "Imagination-based Planner", the
first m... | computer science |
168 | Recurrent Ladder Networks | cs.NE | We propose a recurrent extension of the Ladder networks whose structure is
motivated by the inference required in hierarchical latent variable models. We
demonstrate that the recurrent Ladder is able to handle a wide variety of
complex learning tasks that benefit from iterative inference and temporal
modeling. The arch... | computer science |
169 | Generalization in Deep Learning | stat.ML | With a direct analysis of neural networks, this paper presents a
mathematically tight generalization theory to partially address an open problem
regarding the generalization of deep learning. Unlike previous bound-based
theory, our main theory is quantitatively as tight as possible for every
dataset individually, while... | computer science |
170 | Parametrizing filters of a CNN with a GAN | cs.LG | It is commonly agreed that the use of relevant invariances as a good
statistical bias is important in machine-learning. However, most approaches
that explicitly incorporate invariances into a model architecture only make use
of very simple transformations, such as translations and rotations. Hence,
there is a need for ... | computer science |
171 | Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence
Learning | stat.ML | Long Short-Term Memory (LSTM) is a popular approach to boosting the ability
of Recurrent Neural Networks to store longer term temporal information. The
capacity of an LSTM network can be increased by widening and adding layers.
However, usually the former introduces additional parameters, while the latter
increases the... | computer science |
172 | Learning and Real-time Classification of Hand-written Digits With
Spiking Neural Networks | stat.ML | We describe a novel spiking neural network (SNN) for automated, real-time
handwritten digit classification and its implementation on a GP-GPU platform.
Information processing within the network, from feature extraction to
classification is implemented by mimicking the basic aspects of neuronal spike
initiation and prop... | computer science |
173 | Overcoming catastrophic forgetting with hard attention to the task | cs.LG | Catastrophic forgetting occurs when a neural network loses the information
learned in a previous task after training on subsequent tasks. This problem
remains a hurdle for artificial intelligence systems with sequential learning
capabilities. In this paper, we propose a task-based hard attention mechanism
that preserve... | computer science |
174 | Detecting and Correcting for Label Shift with Black Box Predictors | cs.LG | Faced with distribution shift between training and test set, we wish to
detect and quantify the shift, and to correct our classifiers without test set
labels. Motivated by medical diagnosis, where diseases (targets), cause
symptoms (observations), we focus on label shift, where the label marginal
$p(y)$ changes but the... | computer science |
175 | Generalization in Machine Learning via Analytical Learning Theory | stat.ML | This paper introduces a novel measure-theoretic learning theory to analyze
generalization behaviors of practical interest. The proposed learning theory
has the following abilities: 1) to utilize the qualities of each learned
representation on the path from raw inputs to outputs in representation
learning, 2) to guarant... | computer science |
176 | Sensitivity and Generalization in Neural Networks: an Empirical Study | stat.ML | In practice it is often found that large over-parameterized neural networks
generalize better than their smaller counterparts, an observation that appears
to conflict with classical notions of function complexity, which typically
favor smaller models. In this work, we investigate this tension between
complexity and gen... | computer science |
177 | On the importance of single directions for generalization | stat.ML | Despite their ability to memorize large datasets, deep neural networks often
achieve good generalization performance. However, the differences between the
learned solutions of networks which generalize and those which do not remain
unclear. Additionally, the tuning properties of single directions (defined as
the activa... | computer science |
178 | Maximin affinity learning of image segmentation | cs.CV | Images can be segmented by first using a classifier to predict an affinity
graph that reflects the degree to which image pixels must be grouped together
and then partitioning the graph to yield a segmentation. Machine learning has
been applied to the affinity classifier to produce affinity graphs that are
good in the s... | computer science |
179 | A General Framework for Development of the Cortex-like Visual Object
Recognition System: Waves of Spikes, Predictive Coding and Universal
Dictionary of Features | cs.CV | This study is focused on the development of the cortex-like visual object
recognition system. We propose a general framework, which consists of three
hierarchical levels (modules). These modules functionally correspond to the V1,
V4 and IT areas. Both bottom-up and top-down connections between the
hierarchical levels V... | computer science |
180 | Handwritten Digit Recognition with a Committee of Deep Neural Nets on
GPUs | cs.LG | The competitive MNIST handwritten digit recognition benchmark has a long
history of broken records since 1998. The most recent substantial improvement
by others dates back 7 years (error rate 0.4%) . Recently we were able to
significantly improve this result, using graphics cards to greatly speed up
training of simple ... | computer science |
181 | Eclectic Extraction of Propositional Rules from Neural Networks | cs.LG | Artificial Neural Network is among the most popular algorithm for supervised
learning. However, Neural Networks have a well-known drawback of being a "Black
Box" learner that is not comprehensible to the Users. This lack of transparency
makes it unsuitable for many high risk tasks such as medical diagnosis that
require... | computer science |
182 | Message Passing Multi-Agent GANs | cs.CV | Communicating and sharing intelligence among agents is an important facet of
achieving Artificial General Intelligence. As a first step towards this
challenge, we introduce a novel framework for image generation: Message Passing
Multi-Agent Generative Adversarial Networks (MPM GANs). While GANs have
recently been shown... | computer science |
183 | Mode Regularized Generative Adversarial Networks | cs.LG | Although Generative Adversarial Networks achieve state-of-the-art results on
a variety of generative tasks, they are regarded as highly unstable and prone
to miss modes. We argue that these bad behaviors of GANs are due to the very
particular functional shape of the trained discriminators in high dimensional
spaces, wh... | computer science |
184 | Layer-Specific Adaptive Learning Rates for Deep Networks | cs.CV | The increasing complexity of deep learning architectures is resulting in
training time requiring weeks or even months. This slow training is due in part
to vanishing gradients, in which the gradients used by back-propagation are
extremely large for weights connecting deep layers (layers near the output
layer), and extr... | computer science |
185 | Return of Frustratingly Easy Domain Adaptation | cs.CV | Unlike human learning, machine learning often fails to handle changes between
training (source) and test (target) input distributions. Such domain shifts,
common in practical scenarios, severely damage the performance of conventional
machine learning methods. Supervised domain adaptation methods have been
proposed for ... | computer science |
186 | Origami: A 803 GOp/s/W Convolutional Network Accelerator | cs.CV | An ever increasing number of computer vision and image/video processing
challenges are being approached using deep convolutional neural networks,
obtaining state-of-the-art results in object recognition and detection,
semantic segmentation, action recognition, optical flow and superresolution.
Hardware acceleration of ... | computer science |
187 | Option Discovery in Hierarchical Reinforcement Learning using
Spatio-Temporal Clustering | cs.LG | This paper introduces an automated skill acquisition framework in
reinforcement learning which involves identifying a hierarchical description of
the given task in terms of abstract states and extended actions between
abstract states. Identifying such structures present in the task provides ways
to simplify and speed u... | computer science |
188 | Residual Networks Behave Like Ensembles of Relatively Shallow Networks | cs.CV | In this work we propose a novel interpretation of residual networks showing
that they can be seen as a collection of many paths of differing length.
Moreover, residual networks seem to enable very deep networks by leveraging
only the short paths during training. To support this observation, we rewrite
residual networks... | computer science |
189 | Synthesizing the preferred inputs for neurons in neural networks via
deep generator networks | cs.NE | Deep neural networks (DNNs) have demonstrated state-of-the-art results on
many pattern recognition tasks, especially vision classification problems.
Understanding the inner workings of such computational brains is both
fascinating basic science that is interesting in its own right - similar to why
we study the human br... | computer science |
190 | Structured Convolution Matrices for Energy-efficient Deep learning | cs.NE | We derive a relationship between network representation in energy-efficient
neuromorphic architectures and block Toplitz convolutional matrices. Inspired
by this connection, we develop deep convolutional networks using a family of
structured convolutional matrices and achieve state-of-the-art trade-off
between energy e... | computer science |
191 | Deep CORAL: Correlation Alignment for Deep Domain Adaptation | cs.CV | Deep neural networks are able to learn powerful representations from large
quantities of labeled input data, however they cannot always generalize well
across changes in input distributions. Domain adaptation algorithms have been
proposed to compensate for the degradation in performance due to domain shift.
In this pap... | computer science |
192 | Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition | cs.CV | 3D action recognition - analysis of human actions based on 3D skeleton data -
becomes popular recently due to its succinctness, robustness, and
view-invariant representation. Recent attempts on this problem suggested to
develop RNN-based learning methods to model the contextual dependency in the
temporal domain. In thi... | computer science |
193 | Generalized Dropout | cs.LG | Deep Neural Networks often require good regularizers to generalize well.
Dropout is one such regularizer that is widely used among Deep Learning
practitioners. Recent work has shown that Dropout can also be viewed as
performing Approximate Bayesian Inference over the network parameters. In this
work, we generalize this... | computer science |
194 | Parsimonious Inference on Convolutional Neural Networks: Learning and
applying on-line kernel activation rules | cs.CV | A new, radical CNN design approach is presented in this paper, considering
the reduction of the total computational load during inference. This is
achieved by a new holistic intervention on both the CNN architecture and the
training procedure, which targets to the parsimonious inference by learning to
exploit or remove... | computer science |
195 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | cs.LG | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on... | computer science |
196 | WRPN: Training and Inference using Wide Reduced-Precision Networks | cs.LG | For computer vision applications, prior works have shown the efficacy of
reducing the numeric precision of model parameters (network weights) in deep
neural networks but also that reducing the precision of activations hurts model
accuracy much more than reducing the precision of model parameters. We study
schemes to tr... | computer science |
197 | Deep Learning is Robust to Massive Label Noise | cs.LG | Deep neural networks trained on large supervised datasets have led to
impressive results in image classification and other tasks. However,
well-annotated datasets can be time-consuming and expensive to collect, lending
increased interest to larger but noisy datasets that are more easily obtained.
In this paper, we show... | computer science |
198 | Improving Content-Invariance in Gated Autoencoders for 2D and 3D Object
Rotation | cs.CV | Content-invariance in mapping codes learned by GAEs is a useful feature for
various relation learning tasks. In this paper we show that the
content-invariance of mapping codes for images of 2D and 3D rotated objects can
be substantially improved by extending the standard GAE loss (symmetric
reconstruction error) with a... | computer science |
199 | Deep Learning for Sensor-based Activity Recognition: A Survey | cs.CV | Sensor-based activity recognition seeks the profound high-level knowledge
about human activities from multitudes of low-level sensor readings.
Conventional pattern recognition approaches have made tremendous progress in
the past years. However, those methods often heavily rely on heuristic
hand-crafted feature extracti... | computer science |
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