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300 | Generating Natural Adversarial Examples | cs.LG | Due to their complex nature, it is hard to characterize the ways in which
machine learning models can misbehave or be exploited when deployed. Recent
work on adversarial examples, i.e. inputs with minor perturbations that result
in substantially different model predictions, is helpful in evaluating the
robustness of th... | computer science |
301 | Training Simplification and Model Simplification for Deep Learning: A
Minimal Effort Back Propagation Method | cs.LG | We propose a simple yet effective technique to simplify the training and the
resulting model of neural networks. In back propagation, only a small subset of
the full gradient is computed to update the model parameters. The gradient
vectors are sparsified in such a way that only the top-$k$ elements (in terms
of magnitu... | computer science |
302 | Embodied Question Answering | cs.CV | We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where
an agent is spawned at a random location in a 3D environment and asked a
question ("What color is the car?"). In order to answer, the agent must first
intelligently navigate to explore the environment, gather information through
first-person ... | computer science |
303 | Don't Just Assume; Look and Answer: Overcoming Priors for Visual
Question Answering | cs.CV | A number of studies have found that today's Visual Question Answering (VQA)
models are heavily driven by superficial correlations in the training data and
lack sufficient image grounding. To encourage development of models geared
towards the latter, we propose a new setting for VQA where for every question
type, train ... | computer science |
304 | CoDraw: Visual Dialog for Collaborative Drawing | cs.CV | In this work, we propose a goal-driven collaborative task that contains
vision, language, and action in a virtual environment as its core components.
Specifically, we develop a collaborative `Image Drawing' game between two
agents, called CoDraw. Our game is grounded in a virtual world that contains
movable clip art ob... | computer science |
305 | Answerer in Questioner's Mind for Goal-Oriented Visual Dialogue | cs.CV | Goal-oriented dialogue has been paid attention for its numerous applications
in artificial intelligence. To solve this task, deep learning and reinforcement
learning have recently been applied. However, these approaches struggle to find
a competent recurrent neural questioner, owing to the complexity of learning a
seri... | computer science |
306 | Resource Constrained Structured Prediction | stat.ML | We study the problem of structured prediction under test-time budget
constraints. We propose a novel approach applicable to a wide range of
structured prediction problems in computer vision and natural language
processing. Our approach seeks to adaptively generate computationally costly
features during test-time in ord... | computer science |
307 | Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to
Action Sequences | cs.CL | We propose a neural sequence-to-sequence model for direction following, a
task that is essential to realizing effective autonomous agents. Our
alignment-based encoder-decoder model with long short-term memory recurrent
neural networks (LSTM-RNN) translates natural language instructions to action
sequences based upon a ... | computer science |
308 | Coupling Distributed and Symbolic Execution for Natural Language Queries | cs.LG | Building neural networks to query a knowledge base (a table) with natural
language is an emerging research topic in deep learning. An executor for table
querying typically requires multiple steps of execution because queries may
have complicated structures. In previous studies, researchers have developed
either fully d... | computer science |
309 | An agent-driven semantical identifier using radial basis neural networks
and reinforcement learning | cs.NE | Due to the huge availability of documents in digital form, and the deception
possibility raise bound to the essence of digital documents and the way they
are spread, the authorship attribution problem has constantly increased its
relevance. Nowadays, authorship attribution,for both information retrieval and
analysis, h... | computer science |
310 | Where is my forearm? Clustering of body parts from simultaneous tactile
and linguistic input using sequential mapping | cs.NE | Humans and animals are constantly exposed to a continuous stream of sensory
information from different modalities. At the same time, they form more
compressed representations like concepts or symbols. In species that use
language, this process is further structured by this interaction, where a
mapping between the senso... | computer science |
311 | Improvements to deep convolutional neural networks for LVCSR | cs.LG | Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Neural
Networks (DNN), as they are able to better reduce spectral variation in the
input signal. This has also been confirmed experimentally, with CNNs showing
improvements in word error rate (WER) between 4-12% relative compared to DNNs
across a var... | computer science |
312 | Collaborative Deep Learning for Recommender Systems | cs.LG | Collaborative filtering (CF) is a successful approach commonly used by many
recommender systems. Conventional CF-based methods use the ratings given to
items by users as the sole source of information for learning to make
recommendation. However, the ratings are often very sparse in many
applications, causing CF-based ... | computer science |
313 | Explaining Predictions of Non-Linear Classifiers in NLP | cs.CL | Layer-wise relevance propagation (LRP) is a recently proposed technique for
explaining predictions of complex non-linear classifiers in terms of input
variables. In this paper, we apply LRP for the first time to natural language
processing (NLP). More precisely, we use it to explain the predictions of a
convolutional n... | computer science |
314 | Tensor network language model | cs.CL | We propose a new statistical model suitable for machine learning of systems
with long distance correlations such as natural languages. The model is based
on directed acyclic graph decorated by multi-linear tensor maps in the vertices
and vector spaces in the edges, called tensor network. Such tensor networks
have been ... | computer science |
315 | Language as a matrix product state | cs.CL | We propose a statistical model for natural language that begins by
considering language as a monoid, then representing it in complex matrices with
a compatible translation invariant probability measure. We interpret the
probability measure as arising via the Born rule from a translation invariant
matrix product state. | computer science |
316 | Accelerating Hessian-free optimization for deep neural networks by
implicit preconditioning and sampling | cs.LG | Hessian-free training has become a popular parallel second or- der
optimization technique for Deep Neural Network training. This study aims at
speeding up Hessian-free training, both by means of decreasing the amount of
data used for training, as well as through reduction of the number of Krylov
subspace solver iterati... | computer science |
317 | Is a Picture Worth Ten Thousand Words in a Review Dataset? | cs.CV | While textual reviews have become prominent in many recommendation-based
systems, automated frameworks to provide relevant visual cues against text
reviews where pictures are not available is a new form of task confronted by
data mining and machine learning researchers. Suggestions of pictures that are
relevant to the ... | computer science |
318 | Validation of nonlinear PCA | cs.LG | Linear principal component analysis (PCA) can be extended to a nonlinear PCA
by using artificial neural networks. But the benefit of curved components
requires a careful control of the model complexity. Moreover, standard
techniques for model selection, including cross-validation and more generally
the use of an indepe... | computer science |
319 | Graph Approximation and Clustering on a Budget | stat.ML | We consider the problem of learning from a similarity matrix (such as
spectral clustering and lowd imensional embedding), when computing pairwise
similarities are costly, and only a limited number of entries can be observed.
We provide a theoretical analysis using standard notions of graph
approximation, significantly ... | computer science |
320 | ShareBoost: Efficient Multiclass Learning with Feature Sharing | cs.LG | Multiclass prediction is the problem of classifying an object into a relevant
target class. We consider the problem of learning a multiclass predictor that
uses only few features, and in particular, the number of used features should
increase sub-linearly with the number of possible classes. This implies that
features ... | computer science |
321 | Functional Principal Component Analysis and Randomized Sparse Clustering
Algorithm for Medical Image Analysis | stat.ML | Due to advances in sensors, growing large and complex medical image data have
the ability to visualize the pathological change in the cellular or even the
molecular level or anatomical changes in tissues and organs. As a consequence,
the medical images have the potential to enhance diagnosis of disease,
prediction of c... | computer science |
322 | Jointly Learning Multiple Measures of Similarities from Triplet
Comparisons | stat.ML | Similarity between objects is multi-faceted and it can be easier for human
annotators to measure it when the focus is on a specific aspect. We consider
the problem of mapping objects into view-specific embeddings where the distance
between them is consistent with the similarity comparisons of the form "from
the t-th vi... | computer science |
323 | Variational Inference for Uncertainty on the Inputs of Gaussian Process
Models | stat.ML | The Gaussian process latent variable model (GP-LVM) provides a flexible
approach for non-linear dimensionality reduction that has been widely applied.
However, the current approach for training GP-LVMs is based on maximum
likelihood, where the latent projection variables are maximized over rather
than integrated out. I... | computer science |
324 | Conditional Generative Adversarial Nets | cs.LG | Generative Adversarial Nets [8] were recently introduced as a novel way to
train generative models. In this work we introduce the conditional version of
generative adversarial nets, which can be constructed by simply feeding the
data, y, we wish to condition on to both the generator and discriminator. We
show that this... | computer science |
325 | Visual Causal Feature Learning | stat.ML | We provide a rigorous definition of the visual cause of a behavior that is
broadly applicable to the visually driven behavior in humans, animals, neurons,
robots and other perceiving systems. Our framework generalizes standard
accounts of causal learning to settings in which the causal variables need to
be constructed ... | computer science |
326 | In Search of the Real Inductive Bias: On the Role of Implicit
Regularization in Deep Learning | cs.LG | We present experiments demonstrating that some other form of capacity
control, different from network size, plays a central role in learning
multilayer feed-forward networks. We argue, partially through analogy to matrix
factorization, that this is an inductive bias that can help shed light on deep
learning. | computer science |
327 | Domain Generalization for Object Recognition with Multi-task
Autoencoders | cs.CV | The problem of domain generalization is to take knowledge acquired from a
number of related domains where training data is available, and to then
successfully apply it to previously unseen domains. We propose a new feature
learning algorithm, Multi-Task Autoencoder (MTAE), that provides good
generalization performance ... | computer science |
328 | Data-Efficient Learning of Feedback Policies from Image Pixels using
Deep Dynamical Models | cs.AI | Data-efficient reinforcement learning (RL) in continuous state-action spaces
using very high-dimensional observations remains a key challenge in developing
fully autonomous systems. We consider a particularly important instance of this
challenge, the pixels-to-torques problem, where an RL agent learns a
closed-loop con... | computer science |
329 | Scatter Component Analysis: A Unified Framework for Domain Adaptation
and Domain Generalization | cs.CV | This paper addresses classification tasks on a particular target domain in
which labeled training data are only available from source domains different
from (but related to) the target. Two closely related frameworks, domain
adaptation and domain generalization, are concerned with such tasks, where the
only difference ... | computer science |
330 | Robust Subspace Clustering via Tighter Rank Approximation | cs.CV | Matrix rank minimization problem is in general NP-hard. The nuclear norm is
used to substitute the rank function in many recent studies. Nevertheless, the
nuclear norm approximation adds all singular values together and the
approximation error may depend heavily on the magnitudes of singular values.
This might restrict... | computer science |
331 | Recognizing Semantic Features in Faces using Deep Learning | cs.LG | The human face constantly conveys information, both consciously and
subconsciously. However, as basic as it is for humans to visually interpret
this information, it is quite a big challenge for machines. Conventional
semantic facial feature recognition and analysis techniques are already in use
and are based on physiol... | computer science |
332 | Deep Reconstruction-Classification Networks for Unsupervised Domain
Adaptation | cs.CV | In this paper, we propose a novel unsupervised domain adaptation algorithm
based on deep learning for visual object recognition. Specifically, we design a
new model called Deep Reconstruction-Classification Network (DRCN), which
jointly learns a shared encoding representation for two tasks: i) supervised
classification... | computer science |
333 | A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation | stat.ML | Finding the most effective way to aggregate multi-subject fMRI data is a
long-standing and challenging problem. It is of increasing interest in
contemporary fMRI studies of human cognition due to the scarcity of data per
subject and the variability of brain anatomy and functional response across
subjects. Recent work o... | computer science |
334 | Feedback-Controlled Sequential Lasso Screening | cs.LG | One way to solve lasso problems when the dictionary does not fit into
available memory is to first screen the dictionary to remove unneeded features.
Prior research has shown that sequential screening methods offer the greatest
promise in this endeavor. Most existing work on sequential screening targets
the context of ... | computer science |
335 | The Symmetry of a Simple Optimization Problem in Lasso Screening | cs.LG | Recently dictionary screening has been proposed as an effective way to
improve the computational efficiency of solving the lasso problem, which is one
of the most commonly used method for learning sparse representations. To
address today's ever increasing large dataset, effective screening relies on a
tight region boun... | computer science |
336 | Hard Negative Mining for Metric Learning Based Zero-Shot Classification | cs.LG | Zero-Shot learning has been shown to be an efficient strategy for domain
adaptation. In this context, this paper builds on the recent work of Bucher et
al. [1], which proposed an approach to solve Zero-Shot classification problems
(ZSC) by introducing a novel metric learning based objective function. This
objective fun... | computer science |
337 | Pose-Selective Max Pooling for Measuring Similarity | cs.CV | In this paper, we deal with two challenges for measuring the similarity of
the subject identities in practical video-based face recognition - the
variation of the head pose in uncontrolled environments and the computational
expense of processing videos. Since the frame-wise feature mean is unable to
characterize the po... | computer science |
338 | Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble
of Autoencoders | cs.CV | A fall is an abnormal activity that occurs rarely, so it is hard to collect
real data for falls. It is, therefore, difficult to use supervised learning
methods to automatically detect falls. Another challenge in using machine
learning methods to automatically detect falls is the choice of engineered
features. In this p... | computer science |
339 | Generalization Error of Invariant Classifiers | stat.ML | This paper studies the generalization error of invariant classifiers. In
particular, we consider the common scenario where the classification task is
invariant to certain transformations of the input, and that the classifier is
constructed (or learned) to be invariant to these transformations. Our approach
relies on fa... | computer science |
340 | Universal adversarial perturbations | cs.CV | Given a state-of-the-art deep neural network classifier, we show the
existence of a universal (image-agnostic) and very small perturbation vector
that causes natural images to be misclassified with high probability. We
propose a systematic algorithm for computing universal perturbations, and show
that state-of-the-art ... | computer science |
341 | Linear Disentangled Representation Learning for Facial Actions | cs.CV | Limited annotated data available for the recognition of facial expression and
action units embarrasses the training of deep networks, which can learn
disentangled invariant features. However, a linear model with just several
parameters normally is not demanding in terms of training data. In this paper,
we propose an el... | computer science |
342 | On Detecting Adversarial Perturbations | stat.ML | Machine learning and deep learning in particular has advanced tremendously on
perceptual tasks in recent years. However, it remains vulnerable against
adversarial perturbations of the input that have been crafted specifically to
fool the system while being quasi-imperceptible to a human. In this work, we
propose to aug... | computer science |
343 | Activation Maximization Generative Adversarial Nets | cs.LG | Class labels have been empirically shown useful in improving the sample
quality of generative adversarial nets (GANs). In this paper, we mathematically
study the properties of the current variants of GANs that make use of class
label information. With class aware gradient and cross-entropy decomposition,
we reveal how ... | computer science |
344 | Interpretable Explanations of Black Boxes by Meaningful Perturbation | cs.CV | As machine learning algorithms are increasingly applied to high impact yet
high risk tasks, such as medical diagnosis or autonomous driving, it is
critical that researchers can explain how such algorithms arrived at their
predictions. In recent years, a number of image saliency methods have been
developed to summarize ... | computer science |
345 | A General Theory for Training Learning Machine | stat.ML | Though the deep learning is pushing the machine learning to a new stage,
basic theories of machine learning are still limited. The principle of
learning, the role of the a prior knowledge, the role of neuron bias, and the
basis for choosing neural transfer function and cost function, etc., are still
far from clear. In ... | computer science |
346 | A Generalization of Convolutional Neural Networks to Graph-Structured
Data | stat.ML | This paper introduces a generalization of Convolutional Neural Networks
(CNNs) from low-dimensional grid data, such as images, to graph-structured
data. We propose a novel spatial convolution utilizing a random walk to uncover
the relations within the input, analogous to the way the standard convolution
uses the spatia... | computer science |
347 | Formal Guarantees on the Robustness of a Classifier against Adversarial
Manipulation | cs.LG | Recent work has shown that state-of-the-art classifiers are quite brittle, in
the sense that a small adversarial change of an originally with high confidence
correctly classified input leads to a wrong classification again with high
confidence. This raises concerns that such classifiers are vulnerable to
attacks and ca... | computer science |
348 | Classification regions of deep neural networks | cs.CV | The goal of this paper is to analyze the geometric properties of deep neural
network classifiers in the input space. We specifically study the topology of
classification regions created by deep networks, as well as their associated
decision boundary. Through a systematic empirical investigation, we show that
state-of-t... | computer science |
349 | Analysis of universal adversarial perturbations | cs.CV | Deep networks have recently been shown to be vulnerable to universal
perturbations: there exist very small image-agnostic perturbations that cause
most natural images to be misclassified by such classifiers. In this paper, we
propose the first quantitative analysis of the robustness of classifiers to
universal perturba... | computer science |
350 | Bayesian GAN | stat.ML | Generative adversarial networks (GANs) can implicitly learn rich
distributions over images, audio, and data which are hard to model with an
explicit likelihood. We present a practical Bayesian formulation for
unsupervised and semi-supervised learning with GANs. Within this framework, we
use stochastic gradient Hamilton... | computer science |
351 | Unsupervised Learning of Disentangled Representations from Video | cs.LG | We present a new model DrNET that learns disentangled image representations
from video. Our approach leverages the temporal coherence of video and a novel
adversarial loss to learn a representation that factorizes each frame into a
stationary part and a temporally varying component. The disentangled
representation can ... | computer science |
352 | Dualing GANs | cs.LG | Generative adversarial nets (GANs) are a promising technique for modeling a
distribution from samples. It is however well known that GAN training suffers
from instability due to the nature of its maximin formulation. In this paper,
we explore ways to tackle the instability problem by dualizing the
discriminator. We sta... | computer science |
353 | Wavelet Residual Network for Low-Dose CT via Deep Convolutional
Framelets | stat.ML | Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT
are computationally expensive. To address this problem, we recently proposed
the world-first deep convolutional neural network (CNN) for low-dose X-ray CT
and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However,
some of the ... | computer science |
354 | 3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks | cs.CV | The success of various applications including robotics, digital content
creation, and visualization demand a structured and abstract representation of
the 3D world from limited sensor data. Inspired by the nature of human
perception of 3D shapes as a collection of simple parts, we explore such an
abstract shape represe... | computer science |
355 | Inception Score, Label Smoothing, Gradient Vanishing and -log(D(x))
Alternative | cs.LG | In this article, we mathematically study several GAN related topics,
including Inception score, label smoothing, gradient vanishing and the
-log(D(x)) alternative.
--- An advanced version is included in arXiv:1703.02000 "Activation
Maximization Generative Adversarial Nets". Please refer Section 6 in 1703.02000
for de... | computer science |
356 | A Brief Survey of Deep Reinforcement Learning | cs.LG | Deep reinforcement learning is poised to revolutionise the field of AI and
represents a step towards building autonomous systems with a higher level
understanding of the visual world. Currently, deep learning is enabling
reinforcement learning to scale to problems that were previously intractable,
such as learning to p... | computer science |
357 | CirCNN: Accelerating and Compressing Deep Neural Networks Using
Block-CirculantWeight Matrices | cs.CV | Large-scale deep neural networks (DNNs) are both compute and memory
intensive. As the size of DNNs continues to grow, it is critical to improve the
energy efficiency and performance while maintaining accuracy. For DNNs, the
model size is an important factor affecting performance, scalability and energy
efficiency. Weig... | computer science |
358 | XFlow: 1D-2D Cross-modal Deep Neural Networks for Audiovisual
Classification | stat.ML | We propose two multimodal deep learning architectures that allow for
cross-modal dataflow (XFlow) between the feature extractors, thereby extracting
more interpretable features and obtaining a better representation than through
unimodal learning, for the same amount of training data. These models can
usefully exploit c... | computer science |
359 | Context Embedding Networks | cs.LG | Low dimensional embeddings that capture the main variations of interest in
collections of data are important for many applications. One way to construct
these embeddings is to acquire estimates of similarity from the crowd. However,
similarity is a multi-dimensional concept that varies from individual to
individual. Ex... | computer science |
360 | How Much Chemistry Does a Deep Neural Network Need to Know to Make
Accurate Predictions? | stat.ML | The meteoric rise of deep learning models in computer vision research, having
achieved human-level accuracy in image recognition tasks is firm evidence of
the impact of representation learning of deep neural networks. In the chemistry
domain, recent advances have also led to the development of similar CNN models,
such ... | computer science |
361 | Variational Inference of Disentangled Latent Concepts from Unlabeled
Observations | cs.LG | Disentangled representations, where the higher level data generative factors
are reflected in disjoint latent dimensions, offer several benefits such as
ease of deriving invariant representations, transferability to other tasks,
interpretability, etc. We consider the problem of unsupervised learning of
disentangled rep... | computer science |
362 | Three Factors Influencing Minima in SGD | cs.LG | We study the properties of the endpoint of stochastic gradient descent (SGD).
By approximating SGD as a stochastic differential equation (SDE) we consider
the Boltzmann-Gibbs equilibrium distribution of that SDE under the assumption
of isotropic variance in loss gradients. Through this analysis, we find that
three fact... | computer science |
363 | Learning to Play Othello with Deep Neural Networks | cs.AI | Achieving superhuman playing level by AlphaGo corroborated the capabilities
of convolutional neural architectures (CNNs) for capturing complex spatial
patterns. This result was to a great extent due to several analogies between Go
board states and 2D images CNNs have been designed for, in particular
translational invar... | computer science |
364 | Deep Learning Can Reverse Photon Migration for Diffuse Optical
Tomography | cs.CV | Can artificial intelligence (AI) learn complicated non-linear physics? Here
we propose a novel deep learning approach that learns non-linear photon
scattering physics and obtains accurate 3D distribution of optical anomalies.
In contrast to the traditional black-box deep learning approaches to inverse
problems, our dee... | computer science |
365 | Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for
Transferable Chemical Property Prediction | stat.ML | With access to large datasets, deep neural networks (DNN) have achieved
human-level accuracy in image and speech recognition tasks. However, in
chemistry, data is inherently small and fragmented. In this work, we develop an
approach of using rule-based knowledge for training ChemNet, a transferable and
generalizable de... | computer science |
366 | Deep Learning in RF Sub-sampled B-mode Ultrasound Imaging | cs.CV | In portable, three dimensional, and ultra-fast ultrasound (US) imaging
systems, there is an increasing need to reconstruct high quality images from a
limited number of RF data from receiver (Rx) or scan-line (SC) sub-sampling.
However, due to the severe side lobe artifacts from RF sub-sampling, the
standard beam-former... | computer science |
367 | Deep Learning Interior Tomography for Region-of-Interest Reconstruction | cs.CV | Interior tomography for the region-of-interest (ROI) imaging has advantages
of using a small detector and reducing X-ray radiation dose. However, standard
analytic reconstruction suffers from severe cupping artifacts due to existence
of null space in the truncated Radon transform. Existing penalized
reconstruction meth... | computer science |
368 | Deep Learning Reconstruction for 9-View Dual Energy CT Baggage Scanner | cs.CV | For homeland and transportation security applications, 2D X-ray explosive
detection system (EDS) have been widely used, but they have limitations in
recognizing 3D shape of the hidden objects. Among various types of 3D computed
tomography (CT) systems to address this issue, this paper is interested in a
stationary CT u... | computer science |
369 | Effective Building Block Design for Deep Convolutional Neural Networks
using Search | cs.LG | Deep learning has shown promising results on many machine learning tasks but
DL models are often complex networks with large number of neurons and layers,
and recently, complex layer structures known as building blocks. Finding the
best deep model requires a combination of finding both the right architecture
and the co... | computer science |
370 | TVAE: Triplet-Based Variational Autoencoder using Metric Learning | stat.ML | Deep metric learning has been demonstrated to be highly effective in learning
semantic representation and encoding information that can be used to measure
data similarity, by relying on the embedding learned from metric learning. At
the same time, variational autoencoder (VAE) has widely been used to
approximate infere... | computer science |
371 | Learning to Play with Intrinsically-Motivated Self-Aware Agents | cs.LG | Infants are experts at playing, with an amazing ability to generate novel
structured behaviors in unstructured environments that lack clear extrinsic
reward signals. We seek to mathematically formalize these abilities using a
neural network that implements curiosity-driven intrinsic motivation. Using a
simple but ecolo... | computer science |
372 | Emergence of Structured Behaviors from Curiosity-Based Intrinsic
Motivation | cs.LG | Infants are experts at playing, with an amazing ability to generate novel
structured behaviors in unstructured environments that lack clear extrinsic
reward signals. We seek to replicate some of these abilities with a neural
network that implements curiosity-driven intrinsic motivation. Using a simple
but ecologically ... | computer science |
373 | Stochastic Video Generation with a Learned Prior | cs.CV | Generating video frames that accurately predict future world states is
challenging. Existing approaches either fail to capture the full distribution
of outcomes, or yield blurry generations, or both. In this paper we introduce
an unsupervised video generation model that learns a prior model of uncertainty
in a given en... | computer science |
374 | Multi-Evidence Filtering and Fusion for Multi-Label Classification,
Object Detection and Semantic Segmentation Based on Weakly Supervised
Learning | cs.CV | Supervised object detection and semantic segmentation require object or even
pixel level annotations. When there exist image level labels only, it is
challenging for weakly supervised algorithms to achieve accurate predictions.
The accuracy achieved by top weakly supervised algorithms is still
significantly lower than ... | computer science |
375 | Neural Networks Should Be Wide Enough to Learn Disconnected Decision
Regions | cs.LG | In the recent literature the important role of depth in deep learning has
been emphasized. In this paper we argue that sufficient width of a feedforward
network is equally important by answering the simple question under which
conditions the decision regions of a neural network are connected. It turns out
that for a cl... | computer science |
376 | Visual Explanations From Deep 3D Convolutional Neural Networks for
Alzheimer's Disease Classification | cs.CV | We develop three efficient approaches for generating visual explanations from
3D convolutional neural networks (3D-CNNs) for Alzheimer's disease
classification. One approach conducts sensitivity analysis on hierarchical 3D
image segmentation, and the other two visualize network activations on a
spatial map. Visual chec... | computer science |
377 | Averaging Weights Leads to Wider Optima and Better Generalization | cs.LG | Deep neural networks are typically trained by optimizing a loss function with
an SGD variant, in conjunction with a decaying learning rate, until
convergence. We show that simple averaging of multiple points along the
trajectory of SGD, with a cyclical or constant learning rate, leads to better
generalization than conv... | computer science |
378 | SENNS: Sparse Extraction Neural NetworkS for Feature Extraction | cs.CV | By drawing on ideas from optimisation theory, artificial neural networks
(ANN), graph embeddings and sparse representations, I develop a novel
technique, termed SENNS (Sparse Extraction Neural NetworkS), aimed at
addressing the feature extraction problem. The proposed method uses (preferably
deep) ANNs for projecting i... | computer science |
379 | Generative Models and Model Criticism via Optimized Maximum Mean
Discrepancy | stat.ML | We propose a method to optimize the representation and distinguishability of
samples from two probability distributions, by maximizing the estimated power
of a statistical test based on the maximum mean discrepancy (MMD). This
optimized MMD is applied to the setting of unsupervised learning by generative
adversarial ne... | computer science |
380 | Deep Learning Approximation for Stochastic Control Problems | cs.LG | Many real world stochastic control problems suffer from the "curse of
dimensionality". To overcome this difficulty, we develop a deep learning
approach that directly solves high-dimensional stochastic control problems
based on Monte-Carlo sampling. We approximate the time-dependent controls as
feedforward neural networ... | computer science |
381 | Generating Focussed Molecule Libraries for Drug Discovery with Recurrent
Neural Networks | cs.NE | In de novo drug design, computational strategies are used to generate novel
molecules with good affinity to the desired biological target. In this work, we
show that recurrent neural networks can be trained as generative models for
molecular structures, similar to statistical language models in natural
language process... | computer science |
382 | Parameter Space Noise for Exploration | cs.LG | Deep reinforcement learning (RL) methods generally engage in exploratory
behavior through noise injection in the action space. An alternative is to add
noise directly to the agent's parameters, which can lead to more consistent
exploration and a richer set of behaviors. Methods such as evolutionary
strategies use param... | computer science |
383 | On The Robustness of a Neural Network | stat.ML | With the development of neural networks based machine learning and their
usage in mission critical applications, voices are rising against the
\textit{black box} aspect of neural networks as it becomes crucial to
understand their limits and capabilities. With the rise of neuromorphic
hardware, it is even more critical ... | computer science |
384 | ZhuSuan: A Library for Bayesian Deep Learning | stat.ML | In this paper we introduce ZhuSuan, a python probabilistic programming
library for Bayesian deep learning, which conjoins the complimentary advantages
of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike
existing deep learning libraries, which are mainly designed for deterministic
neural netw... | computer science |
385 | Using Parameterized Black-Box Priors to Scale Up Model-Based Policy
Search for Robotics | cs.RO | The most data-efficient algorithms for reinforcement learning in robotics are
model-based policy search algorithms, which alternate between learning a
dynamical model of the robot and optimizing a policy to maximize the expected
return given the model and its uncertainties. Among the few proposed
approaches, the recent... | computer science |
386 | Bayesian Optimization with Automatic Prior Selection for Data-Efficient
Direct Policy Search | cs.RO | One of the most interesting features of Bayesian optimization for direct
policy search is that it can leverage priors (e.g., from simulation or from
previous tasks) to accelerate learning on a robot. In this paper, we are
interested in situations for which several priors exist but we do not know in
advance which one fi... | computer science |
387 | Bounding and Counting Linear Regions of Deep Neural Networks | cs.LG | In this paper, we study the representational power of deep neural networks
(DNN) that belong to the family of piecewise-linear (PWL) functions, based on
PWL activation units such as rectifier or maxout. We investigate the complexity
of such networks by studying the number of linear regions of the PWL function.
Typicall... | computer science |
388 | Deep Rewiring: Training very sparse deep networks | cs.NE | Neuromorphic hardware tends to pose limits on the connectivity of deep
networks that one can run on them. But also generic hardware and software
implementations of deep learning run more efficiently for sparse networks.
Several methods exist for pruning connections of a neural network after it was
trained without conne... | computer science |
389 | Comparing heterogeneous entities using artificial neural networks of
trainable weighted structural components and machine-learned activation
functions | stat.ML | To compare entities of differing types and structural components, the
artificial neural network paradigm was used to cross-compare structural
components between heterogeneous documents. Trainable weighted structural
components were input into machine-learned activation functions of the neurons.
The model was used for m... | computer science |
390 | Active Learning of Inverse Models with Intrinsically Motivated Goal
Exploration in Robots | cs.LG | We introduce the Self-Adaptive Goal Generation - Robust Intelligent Adaptive
Curiosity (SAGG-RIAC) architecture as an intrinsi- cally motivated goal
exploration mechanism which allows active learning of inverse models in
high-dimensional redundant robots. This allows a robot to efficiently and
actively learn distributi... | computer science |
391 | End-to-End Tracking and Semantic Segmentation Using Recurrent Neural
Networks | cs.LG | In this work we present a novel end-to-end framework for tracking and
classifying a robot's surroundings in complex, dynamic and only partially
observable real-world environments. The approach deploys a recurrent neural
network to filter an input stream of raw laser measurements in order to
directly infer object locati... | computer science |
392 | Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks | cs.LG | This paper presents to the best of our knowledge the first end-to-end object
tracking approach which directly maps from raw sensor input to object tracks in
sensor space without requiring any feature engineering or system identification
in the form of plant or sensor models. Specifically, our system accepts a
stream of... | computer science |
393 | Deep Predictive Coding Networks for Video Prediction and Unsupervised
Learning | cs.LG | While great strides have been made in using deep learning algorithms to solve
supervised learning tasks, the problem of unsupervised learning - leveraging
unlabeled examples to learn about the structure of a domain - remains a
difficult unsolved challenge. Here, we explore prediction of future frames in a
video sequenc... | computer science |
394 | Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient
Convolutional Neural Networks | cs.RO | This paper proposes a computationally efficient approach to detecting objects
natively in 3D point clouds using convolutional neural networks (CNNs). In
particular, this is achieved by leveraging a feature-centric voting scheme to
implement novel convolutional layers which explicitly exploit the sparsity
encountered in... | computer science |
395 | On Convergence and Stability of GANs | cs.AI | We propose studying GAN training dynamics as regret minimization, which is in
contrast to the popular view that there is consistent minimization of a
divergence between real and generated distributions. We analyze the convergence
of GAN training from this new point of view to understand why mode collapse
happens. We hy... | computer science |
396 | Imitation from Observation: Learning to Imitate Behaviors from Raw Video
via Context Translation | cs.LG | Imitation learning is an effective approach for autonomous systems to acquire
control policies when an explicit reward function is unavailable, using
supervision provided as demonstrations from an expert, typically a human
operator. However, standard imitation learning methods assume that the agent
receives examples of... | computer science |
397 | Convergence rates for pretraining and dropout: Guiding learning
parameters using network structure | cs.LG | Unsupervised pretraining and dropout have been well studied, especially with
respect to regularization and output consistency. However, our understanding
about the explicit convergence rates of the parameter estimates, and their
dependence on the learning (like denoising and dropout rate) and structural
(like depth and... | computer science |
398 | Learning Discriminative Features via Label Consistent Neural Network | cs.CV | Deep Convolutional Neural Networks (CNN) enforces supervised information only
at the output layer, and hidden layers are trained by back propagating the
prediction error from the output layer without explicit supervision. We propose
a supervised feature learning approach, Label Consistent Neural Network, which
enforces... | computer science |
399 | Out-of-Sample Extension for Dimensionality Reduction of Noisy Time
Series | stat.ML | This paper proposes an out-of-sample extension framework for a global
manifold learning algorithm (Isomap) that uses temporal information in
out-of-sample points in order to make the embedding more robust to noise and
artifacts. Given a set of noise-free training data and its embedding, the
proposed framework extends t... | computer science |
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