Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
200 | On the Importance of Consistency in Training Deep Neural Networks | cs.LG | We explain that the difficulties of training deep neural networks come from a
syndrome of three consistency issues. This paper describes our efforts in their
analysis and treatment. The first issue is the training speed inconsistency in
different layers. We propose to address it with an intuitive,
simple-to-implement, ... | computer science |
201 | UI-Net: Interactive Artificial Neural Networks for Iterative Image
Segmentation Based on a User Model | cs.CV | For complex segmentation tasks, fully automatic systems are inherently
limited in their achievable accuracy for extracting relevant objects.
Especially in cases where only few data sets need to be processed for a highly
accurate result, semi-automatic segmentation techniques exhibit a clear benefit
for the user. One ar... | computer science |
202 | Lightweight Neural Networks | cs.LG | Most of the weights in a Lightweight Neural Network have a value of zero,
while the remaining ones are either +1 or -1. These universal approximators
require approximately 1.1 bits/weight of storage, posses a quick forward pass
and achieve classification accuracies similar to conventional continuous-weight
networks. Th... | computer science |
203 | Tensor Field Networks: Rotation- and Translation-Equivariant Neural
Networks for 3D Point Clouds | cs.LG | We introduce tensor field networks, which are locally equivariant to 3D
rotations, translations, and permutations of points at every layer. 3D rotation
equivariance removes the need for data augmentation to identify features in
arbitrary orientations. Our network uses filters built from spherical
harmonics; due to the ... | computer science |
204 | Knowledge Matters: Importance of Prior Information for Optimization | cs.LG | We explore the effect of introducing prior information into the intermediate
level of neural networks for a learning task on which all the state-of-the-art
machine learning algorithms tested failed to learn. We motivate our work from
the hypothesis that humans learn such intermediate concepts from other
individuals via... | computer science |
205 | Zero-bias autoencoders and the benefits of co-adapting features | stat.ML | Regularized training of an autoencoder typically results in hidden unit
biases that take on large negative values. We show that negative biases are a
natural result of using a hidden layer whose responsibility is to both
represent the input data and act as a selection mechanism that ensures sparsity
of the representati... | computer science |
206 | Theory and Tools for the Conversion of Analog to Spiking Convolutional
Neural Networks | stat.ML | Deep convolutional neural networks (CNNs) have shown great potential for
numerous real-world machine learning applications, but performing inference in
large CNNs in real-time remains a challenge. We have previously demonstrated
that traditional CNNs can be converted into deep spiking neural networks
(SNNs), which exhi... | computer science |
207 | Stacked Generative Adversarial Networks | cs.CV | In this paper, we propose a novel generative model named Stacked Generative
Adversarial Networks (SGAN), which is trained to invert the hierarchical
representations of a bottom-up discriminative network. Our model consists of a
top-down stack of GANs, each learned to generate lower-level representations
conditioned on ... | computer science |
208 | Self-informed neural network structure learning | stat.ML | We study the problem of large scale, multi-label visual recognition with a
large number of possible classes. We propose a method for augmenting a trained
neural network classifier with auxiliary capacity in a manner designed to
significantly improve upon an already well-performing model, while minimally
impacting its c... | computer science |
209 | Learning Activation Functions to Improve Deep Neural Networks | cs.NE | Artificial neural networks typically have a fixed, non-linear activation
function at each neuron. We have designed a novel form of piecewise linear
activation function that is learned independently for each neuron using
gradient descent. With this adaptive activation function, we are able to
improve upon deep neural ne... | computer science |
210 | Denoising autoencoder with modulated lateral connections learns
invariant representations of natural images | cs.NE | Suitable lateral connections between encoder and decoder are shown to allow
higher layers of a denoising autoencoder (dAE) to focus on invariant
representations. In regular autoencoders, detailed information needs to be
carried through the highest layers but lateral connections from encoder to
decoder relieve this pres... | computer science |
211 | A Probabilistic Theory of Deep Learning | stat.ML | A grand challenge in machine learning is the development of computational
algorithms that match or outperform humans in perceptual inference tasks that
are complicated by nuisance variation. For instance, visual object recognition
involves the unknown object position, orientation, and scale in object
recognition while ... | computer science |
212 | Integrated Inference and Learning of Neural Factors in Structural
Support Vector Machines | stat.ML | Tackling pattern recognition problems in areas such as computer vision,
bioinformatics, speech or text recognition is often done best by taking into
account task-specific statistical relations between output variables. In
structured prediction, this internal structure is used to predict multiple
outputs simultaneously,... | computer science |
213 | What Happened to My Dog in That Network: Unraveling Top-down Generators
in Convolutional Neural Networks | cs.NE | Top-down information plays a central role in human perception, but plays
relatively little role in many current state-of-the-art deep networks, such as
Convolutional Neural Networks (CNNs). This work seeks to explore a path by
which top-down information can have a direct impact within current deep
networks. We explore ... | computer science |
214 | Virtual Worlds as Proxy for Multi-Object Tracking Analysis | cs.CV | Modern computer vision algorithms typically require expensive data
acquisition and accurate manual labeling. In this work, we instead leverage the
recent progress in computer graphics to generate fully labeled, dynamic, and
photo-realistic proxy virtual worlds. We propose an efficient real-to-virtual
world cloning meth... | computer science |
215 | Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet | stat.ML | Video sequences contain rich dynamic patterns, such as dynamic texture
patterns that exhibit stationarity in the temporal domain, and action patterns
that are non-stationary in either spatial or temporal domain. We show that a
spatial-temporal generative ConvNet can be used to model and synthesize dynamic
patterns. The... | computer science |
216 | Deep Learning with Darwin: Evolutionary Synthesis of Deep Neural
Networks | cs.CV | Taking inspiration from biological evolution, we explore the idea of "Can
deep neural networks evolve naturally over successive generations into highly
efficient deep neural networks?" by introducing the notion of synthesizing new
highly efficient, yet powerful deep neural networks over successive generations
via an ev... | computer science |
217 | Alternating Back-Propagation for Generator Network | stat.ML | This paper proposes an alternating back-propagation algorithm for learning
the generator network model. The model is a non-linear generalization of factor
analysis. In this model, the mapping from the continuous latent factors to the
observed signal is parametrized by a convolutional neural network. The
alternating bac... | computer science |
218 | Hyperparameter Transfer Learning through Surrogate Alignment for
Efficient Deep Neural Network Training | cs.LG | Recently, several optimization methods have been successfully applied to the
hyperparameter optimization of deep neural networks (DNNs). The methods work by
modeling the joint distribution of hyperparameter values and corresponding
error. Those methods become less practical when applied to modern DNNs whose
training ma... | computer science |
219 | Towards Bayesian Deep Learning: A Framework and Some Existing Methods | stat.ML | While perception tasks such as visual object recognition and text
understanding play an important role in human intelligence, the subsequent
tasks that involve inference, reasoning and planning require an even higher
level of intelligence. The past few years have seen major advances in many
perception tasks using deep ... | computer science |
220 | Deciding How to Decide: Dynamic Routing in Artificial Neural Networks | stat.ML | We propose and systematically evaluate three strategies for training
dynamically-routed artificial neural networks: graphs of learned
transformations through which different input signals may take different paths.
Though some approaches have advantages over others, the resulting networks are
often qualitatively similar... | computer science |
221 | Pixel Deconvolutional Networks | cs.LG | Deconvolutional layers have been widely used in a variety of deep models for
up-sampling, including encoder-decoder networks for semantic segmentation and
deep generative models for unsupervised learning. One of the key limitations of
deconvolutional operations is that they result in the so-called checkerboard
problem.... | computer science |
222 | Gaussian Prototypical Networks for Few-Shot Learning on Omniglot | cs.LG | We propose a novel architecture for $k$-shot classification on the Omniglot
dataset. Building on prototypical networks, we extend their architecture to
what we call Gaussian prototypical networks. Prototypical networks learn a map
between images and embedding vectors, and use their clustering for
classification. In our... | computer science |
223 | Super-Convergence: Very Fast Training of Residual Networks Using Large
Learning Rates | cs.LG | In this paper, we show a phenomenon, which we named "super-convergence",
where residual networks can be trained using an order of magnitude fewer
iterations than is used with standard training methods. The existence of
super-convergence is relevant to understanding why deep networks generalize
well. One of the key elem... | computer science |
224 | Generative learning for deep networks | cs.LG | Learning, taking into account full distribution of the data, referred to as
generative, is not feasible with deep neural networks (DNNs) because they model
only the conditional distribution of the outputs given the inputs. Current
solutions are either based on joint probability models facing difficult
estimation proble... | computer science |
225 | Hierarchical Representations for Efficient Architecture Search | cs.LG | We explore efficient neural architecture search methods and show that a
simple yet powerful evolutionary algorithm can discover new architectures with
excellent performance. Our approach combines a novel hierarchical genetic
representation scheme that imitates the modularized design pattern commonly
adopted by human ex... | computer science |
226 | Data Augmentation Generative Adversarial Networks | stat.ML | Effective training of neural networks requires much data. In the low-data
regime, parameters are underdetermined, and learnt networks generalise poorly.
Data Augmentation alleviates this by using existing data more effectively.
However standard data augmentation produces only limited plausible alternative
data. Given t... | computer science |
227 | DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the
Jigsaw Puzzle Problem | cs.CV | This paper introduces the first deep neural network-based estimation metric
for the jigsaw puzzle problem. Given two puzzle piece edges, the neural network
predicts whether or not they should be adjacent in the correct assembly of the
puzzle, using nothing but the pixels of each piece. The proposed metric
exhibits an e... | computer science |
228 | DeepPainter: Painter Classification Using Deep Convolutional
Autoencoders | cs.CV | In this paper we describe the problem of painter classification, and propose
a novel approach based on deep convolutional autoencoder neural networks. While
previous approaches relied on image processing and manual feature extraction
from paintings, our approach operates on the raw pixel level, without any
preprocessin... | computer science |
229 | DeepBrain: Functional Representation of Neural In-Situ Hybridization
Images for Gene Ontology Classification Using Deep Convolutional Autoencoders | cs.CV | This paper presents a novel deep learning-based method for learning a
functional representation of mammalian neural images. The method uses a deep
convolutional denoising autoencoder (CDAE) for generating an invariant, compact
representation of in situ hybridization (ISH) images. While most existing
methods for bio-ima... | computer science |
230 | Generative Adversarial Perturbations | cs.CV | In this paper, we propose novel generative models for creating adversarial
examples, slightly perturbed images resembling natural images but maliciously
crafted to fool pre-trained models. We present trainable deep neural networks
for transforming images to adversarial perturbations. Our proposed models can
produce ima... | computer science |
231 | A Rotation and a Translation Suffice: Fooling CNNs with Simple
Transformations | cs.LG | We show that simple transformations, namely translations and rotations alone,
are sufficient to fool neural network-based vision models on a significant
fraction of inputs. This is in sharp contrast to previous work that relied on
more complicated optimization approaches that are unlikely to appear outside of
a truly a... | computer science |
232 | Peephole: Predicting Network Performance Before Training | cs.LG | The quest for performant networks has been a significant force that drives
the advancements of deep learning in recent years. While rewarding, improving
network design has never been an easy journey. The large design space combined
with the tremendous cost required for network training poses a major obstacle
to this en... | computer science |
233 | An Architecture Combining Convolutional Neural Network (CNN) and Support
Vector Machine (SVM) for Image Classification | cs.CV | Convolutional neural networks (CNNs) are similar to "ordinary" neural
networks in the sense that they are made up of hidden layers consisting of
neurons with "learnable" parameters. These neurons receive inputs, performs a
dot product, and then follows it with a non-linearity. The whole network
expresses the mapping be... | computer science |
234 | Benchmarking Decoupled Neural Interfaces with Synthetic Gradients | cs.LG | Artifical Neural Networks are a particular class of learning systems modeled
after biological neural functions with an interesting penchant for Hebbian
learning, that is "neurons that wire together, fire together". However, unlike
their natural counterparts, artificial neural networks have a close and
stringent couplin... | computer science |
235 | Segmentation hiérarchique faiblement supervisée | stat.ML | Image segmentation is the process of partitioning an image into a set of
meaningful regions according to some criteria. Hierarchical segmentation has
emerged as a major trend in this regard as it favors the emergence of important
regions at different scales. On the other hand, many methods allow us to have
prior inform... | computer science |
236 | Training wide residual networks for deployment using a single bit for
each weight | cs.LG | For fast and energy-efficient deployment of trained deep neural networks on
resource-constrained embedded hardware, each learned weight parameter should
ideally be represented and stored using a single bit. Error-rates usually
increase when this requirement is imposed. Here, we report large improvements
in error rates ... | computer science |
237 | Deep Learning using Rectified Linear Units (ReLU) | cs.NE | We introduce the use of rectified linear units (ReLU) as the classification
function in a deep neural network (DNN). Conventionally, ReLU is used as an
activation function in DNNs, with Softmax function as their classification
function. However, there have been several studies on using a classification
function other t... | computer science |
238 | Rectified Factor Networks | cs.LG | We propose rectified factor networks (RFNs) to efficiently construct very
sparse, non-linear, high-dimensional representations of the input. RFN models
identify rare and small events in the input, have a low interference between
code units, have a small reconstruction error, and explain the data covariance
structure. R... | computer science |
239 | From Maxout to Channel-Out: Encoding Information on Sparse Pathways | cs.NE | Motivated by an important insight from neural science, we propose a new
framework for understanding the success of the recently proposed "maxout"
networks. The framework is based on encoding information on sparse pathways and
recognizing the correct pathway at inference time. Elaborating further on this
insight, we pro... | computer science |
240 | Competitive Learning with Feedforward Supervisory Signal for Pre-trained
Multilayered Networks | cs.NE | We propose a novel learning method for multilayered neural networks which
uses feedforward supervisory signal and associates classification of a new
input with that of pre-trained input. The proposed method effectively uses rich
input information in the earlier layer for robust leaning and revising internal
representat... | computer science |
241 | Deeply-Supervised Nets | stat.ML | Our proposed deeply-supervised nets (DSN) method simultaneously minimizes
classification error while making the learning process of hidden layers direct
and transparent. We make an attempt to boost the classification performance by
studying a new formulation in deep networks. Three aspects in convolutional
neural netwo... | computer science |
242 | Path-SGD: Path-Normalized Optimization in Deep Neural Networks | cs.LG | We revisit the choice of SGD for training deep neural networks by
reconsidering the appropriate geometry in which to optimize the weights. We
argue for a geometry invariant to rescaling of weights that does not affect the
output of the network, and suggest Path-SGD, which is an approximate steepest
descent method with ... | computer science |
243 | Adapting Resilient Propagation for Deep Learning | cs.NE | The Resilient Propagation (Rprop) algorithm has been very popular for
backpropagation training of multilayer feed-forward neural networks in various
applications. The standard Rprop however encounters difficulties in the context
of deep neural networks as typically happens with gradient-based learning
algorithms. In th... | computer science |
244 | Convolutional Neural Network for Stereotypical Motor Movement Detection
in Autism | cs.NE | Autism Spectrum Disorders (ASDs) are often associated with specific atypical
postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have
a specific visibility. While the identification and the quantification of SMM
patterns remain complex, its automation would provide support to accurate
tuning of t... | computer science |
245 | Resnet in Resnet: Generalizing Residual Architectures | cs.LG | Residual networks (ResNets) have recently achieved state-of-the-art on
challenging computer vision tasks. We introduce Resnet in Resnet (RiR): a deep
dual-stream architecture that generalizes ResNets and standard CNNs and is
easily implemented with no computational overhead. RiR consistently improves
performance over R... | computer science |
246 | Evolutionary Synthesis of Deep Neural Networks via Synaptic
Cluster-driven Genetic Encoding | cs.LG | There has been significant recent interest towards achieving highly efficient
deep neural network architectures. A promising paradigm for achieving this is
the concept of evolutionary deep intelligence, which attempts to mimic
biological evolution processes to synthesize highly-efficient deep neural
networks over succe... | computer science |
247 | Neural Photo Editing with Introspective Adversarial Networks | cs.LG | The increasingly photorealistic sample quality of generative image models
suggests their feasibility in applications beyond image generation. We present
the Neural Photo Editor, an interface that leverages the power of generative
neural networks to make large, semantically coherent changes to existing
images. To tackle... | computer science |
248 | Adaptive Neural Networks for Efficient Inference | cs.LG | We present an approach to adaptively utilize deep neural networks in order to
reduce the evaluation time on new examples without loss of accuracy. Rather
than attempting to redesign or approximate existing networks, we propose two
schemes that adaptively utilize networks. We first pose an adaptive network
evaluation sc... | computer science |
249 | Spatial Variational Auto-Encoding via Matrix-Variate Normal
Distributions | cs.LG | The key idea of variational auto-encoders (VAEs) resembles that of
traditional auto-encoder models in which spatial information is supposed to be
explicitly encoded in the latent space. However, the latent variables in VAEs
are vectors, which are commonly interpreted as multiple feature maps of size
1x1. Such represent... | computer science |
250 | Dense Transformer Networks | cs.CV | The key idea of current deep learning methods for dense prediction is to
apply a model on a regular patch centered on each pixel to make pixel-wise
predictions. These methods are limited in the sense that the patches are
determined by network architecture instead of learned from data. In this work,
we propose the dense... | computer science |
251 | Progressive Learning for Systematic Design of Large Neural Networks | cs.NE | We develop an algorithm for systematic design of a large artificial neural
network using a progression property. We find that some non-linear functions,
such as the rectifier linear unit and its derivatives, hold the property. The
systematic design addresses the choice of network size and regularization of
parameters. ... | computer science |
252 | A Classification-Based Perspective on GAN Distributions | cs.LG | A fundamental, and still largely unanswered, question in the context of
Generative Adversarial Networks (GANs) is whether GANs are actually able to
capture the key characteristics of the datasets they are trained on. The
current approaches to examining this issue require significant human
supervision, such as visual in... | computer science |
253 | Learning Visual Reasoning Without Strong Priors | cs.CV | Achieving artificial visual reasoning - the ability to answer image-related
questions which require a multi-step, high-level process - is an important step
towards artificial general intelligence. This multi-modal task requires
learning a question-dependent, structured reasoning process over images from
language. Stand... | computer science |
254 | Men Also Like Shopping: Reducing Gender Bias Amplification using
Corpus-level Constraints | cs.AI | Language is increasingly being used to define rich visual recognition
problems with supporting image collections sourced from the web. Structured
prediction models are used in these tasks to take advantage of correlations
between co-occurring labels and visual input but risk inadvertently encoding
social biases found i... | computer science |
255 | Acquiring Common Sense Spatial Knowledge through Implicit Spatial
Templates | cs.AI | Spatial understanding is a fundamental problem with wide-reaching real-world
applications. The representation of spatial knowledge is often modeled with
spatial templates, i.e., regions of acceptability of two objects under an
explicit spatial relationship (e.g., "on", "below", etc.). In contrast with
prior work that r... | computer science |
256 | FiLM: Visual Reasoning with a General Conditioning Layer | cs.CV | We introduce a general-purpose conditioning method for neural networks called
FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network
computation via a simple, feature-wise affine transformation based on
conditioning information. We show that FiLM layers are highly effective for
visual reasoning - an... | computer science |
257 | Unsupervised Induction of Semantic Roles within a Reconstruction-Error
Minimization Framework | cs.CL | We introduce a new approach to unsupervised estimation of feature-rich
semantic role labeling models. Our model consists of two components: (1) an
encoding component: a semantic role labeling model which predicts roles given a
rich set of syntactic and lexical features; (2) a reconstruction component: a
tensor factoriz... | computer science |
258 | Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word
Embeddings | cs.CL | The blind application of machine learning runs the risk of amplifying biases
present in data. Such a danger is facing us with word embedding, a popular
framework to represent text data as vectors which has been used in many machine
learning and natural language processing tasks. We show that even word
embeddings traine... | computer science |
259 | TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency | cs.CL | In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based
language model designed to directly capture the global semantic meaning
relating words in a document via latent topics. Because of their sequential
nature, RNNs are good at capturing the local structure of a word sequence -
both semantic and syn... | computer science |
260 | Gaussian Attention Model and Its Application to Knowledge Base Embedding
and Question Answering | stat.ML | We propose the Gaussian attention model for content-based neural memory
access. With the proposed attention model, a neural network has the additional
degree of freedom to control the focus of its attention from a laser sharp
attention to a broad attention. It is applicable whenever we can assume that
the distance in t... | computer science |
261 | Variable Computation in Recurrent Neural Networks | stat.ML | Recurrent neural networks (RNNs) have been used extensively and with
increasing success to model various types of sequential data. Much of this
progress has been achieved through devising recurrent units and architectures
with the flexibility to capture complex statistics in the data, such as long
range dependency or l... | computer science |
262 | Learning to Learn from Weak Supervision by Full Supervision | stat.ML | In this paper, we propose a method for training neural networks when we have
a large set of data with weak labels and a small amount of data with true
labels. In our proposed model, we train two neural networks: a target network,
the learner and a confidence network, the meta-learner. The target network is
optimized to... | computer science |
263 | SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for
Predicting Chemical Properties | stat.ML | Chemical databases store information in text representations, and the SMILES
format is a universal standard used in many cheminformatics software. Encoded
in each SMILES string is structural information that can be used to predict
complex chemical properties. In this work, we develop SMILES2vec, a deep RNN
that automat... | computer science |
264 | Sample Efficient Deep Reinforcement Learning for Dialogue Systems with
Large Action Spaces | cs.CL | In spoken dialogue systems, we aim to deploy artificial intelligence to build
automated dialogue agents that can converse with humans. A part of this effort
is the policy optimisation task, which attempts to find a policy describing how
to respond to humans, in the form of a function taking the current state of the
dia... | computer science |
265 | High-Dimensional Vector Semantics | cs.CL | In this paper we explore the "vector semantics" problem from the perspective
of "almost orthogonal" property of high-dimensional random vectors. We show
that this intriguing property can be used to "memorize" random vectors by
simply adding them, and we provide an efficient probabilistic solution to the
set membership ... | computer science |
266 | Learning Semantic Script Knowledge with Event Embeddings | cs.LG | Induction of common sense knowledge about prototypical sequences of events
has recently received much attention. Instead of inducing this knowledge in the
form of graphs, as in much of the previous work, in our method, distributed
representations of event realizations are computed based on distributed
representations o... | computer science |
267 | Mathematical Language Processing: Automatic Grading and Feedback for
Open Response Mathematical Questions | stat.ML | While computer and communication technologies have provided effective means
to scale up many aspects of education, the submission and grading of
assessments such as homework assignments and tests remains a weak link. In this
paper, we study the problem of automatically grading the kinds of open response
mathematical qu... | computer science |
268 | Nonparametric Bayesian Double Articulation Analyzer for Direct Language
Acquisition from Continuous Speech Signals | cs.AI | Human infants can discover words directly from unsegmented speech signals
without any explicitly labeled data. In this paper, we develop a novel machine
learning method called nonparametric Bayesian double articulation analyzer
(NPB-DAA) that can directly acquire language and acoustic models from observed
continuous sp... | computer science |
269 | Harnessing Deep Neural Networks with Logic Rules | cs.LG | Combining deep neural networks with structured logic rules is desirable to
harness flexibility and reduce uninterpretability of the neural models. We
propose a general framework capable of enhancing various types of neural
networks (e.g., CNNs and RNNs) with declarative first-order logic rules.
Specifically, we develop... | computer science |
270 | Toward Controlled Generation of Text | cs.LG | Generic generation and manipulation of text is challenging and has limited
success compared to recent deep generative modeling in visual domain. This
paper aims at generating plausible natural language sentences, whose attributes
are dynamically controlled by learning disentangled latent representations with
designated... | computer science |
271 | Adversarial Connective-exploiting Networks for Implicit Discourse
Relation Classification | cs.CL | Implicit discourse relation classification is of great challenge due to the
lack of connectives as strong linguistic cues, which motivates the use of
annotated implicit connectives to improve the recognition. We propose a feature
imitation framework in which an implicit relation network is driven to learn
from another ... | computer science |
272 | Abstract Syntax Networks for Code Generation and Semantic Parsing | cs.CL | Tasks like code generation and semantic parsing require mapping unstructured
(or partially structured) inputs to well-formed, executable outputs. We
introduce abstract syntax networks, a modeling framework for these problems.
The outputs are represented as abstract syntax trees (ASTs) and constructed by
a decoder with ... | computer science |
273 | Multimodal Word Distributions | stat.ML | Word embeddings provide point representations of words containing useful
semantic information. We introduce multimodal word distributions formed from
Gaussian mixtures, for multiple word meanings, entailment, and rich uncertainty
information. To learn these distributions, we propose an energy-based
max-margin objective... | computer science |
274 | Guiding Reinforcement Learning Exploration Using Natural Language | cs.AI | In this work we present a technique to use natural language to help
reinforcement learning generalize to unseen environments. This technique uses
neural machine translation, specifically the use of encoder-decoder networks,
to learn associations between natural language behavior descriptions and
state-action informatio... | computer science |
275 | Robust Task Clustering for Deep Many-Task Learning | cs.LG | We investigate task clustering for deep-learning based multi-task and
few-shot learning in a many-task setting. We propose a new method to measure
task similarities with cross-task transfer performance matrix for the deep
learning scenario. Although this matrix provides us critical information
regarding similarity betw... | computer science |
276 | Natural Language Multitasking: Analyzing and Improving Syntactic
Saliency of Hidden Representations | cs.CL | We train multi-task autoencoders on linguistic tasks and analyze the learned
hidden sentence representations. The representations change significantly when
translation and part-of-speech decoders are added. The more decoders a model
employs, the better it clusters sentences according to their syntactic
similarity, as t... | computer science |
277 | Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement
Learning | cs.LG | With the increasing popularity of video sharing websites such as YouTube and
Facebook, multimodal sentiment analysis has received increasing attention from
the scientific community. Contrary to previous works in multimodal sentiment
analysis which focus on holistic information in speech segments such as bag of
words re... | computer science |
278 | A Supervised Approach to Extractive Summarisation of Scientific Papers | cs.CL | Automatic summarisation is a popular approach to reduce a document to its
main arguments. Recent research in the area has focused on neural approaches to
summarisation, which can be very data-hungry. However, few large datasets exist
and none for the traditionally popular domain of scientific publications, which
opens ... | computer science |
279 | Language Models for Image Captioning: The Quirks and What Works | cs.CL | Two recent approaches have achieved state-of-the-art results in image
captioning. The first uses a pipelined process where a set of candidate words
is generated by a convolutional neural network (CNN) trained on images, and
then a maximum entropy (ME) language model is used to arrange these words into
a coherent senten... | computer science |
280 | Exploring Models and Data for Image Question Answering | cs.LG | This work aims to address the problem of image-based question-answering (QA)
with new models and datasets. In our work, we propose to use neural networks
and visual semantic embeddings, without intermediate stages such as object
detection and image segmentation, to predict answers to simple questions about
images. Our ... | computer science |
281 | Making the V in VQA Matter: Elevating the Role of Image Understanding in
Visual Question Answering | cs.CV | Problems at the intersection of vision and language are of significant
importance both as challenging research questions and for the rich set of
applications they enable. However, inherent structure in our world and bias in
our language tend to be a simpler signal for learning than visual modalities,
resulting in model... | computer science |
282 | A Multi-World Approach to Question Answering about Real-World Scenes
based on Uncertain Input | cs.AI | We propose a method for automatically answering questions about images by
bringing together recent advances from natural language processing and computer
vision. We combine discrete reasoning with uncertain predictions by a
multi-world approach that represents uncertainty about the perceived world in a
bayesian framewo... | computer science |
283 | Hard to Cheat: A Turing Test based on Answering Questions about Images | cs.AI | Progress in language and image understanding by machines has sparkled the
interest of the research community in more open-ended, holistic tasks, and
refueled an old AI dream of building intelligent machines. We discuss a few
prominent challenges that characterize such holistic tasks and argue for
"question answering ab... | computer science |
284 | Analyzing the Behavior of Visual Question Answering Models | cs.CL | Recently, a number of deep-learning based models have been proposed for the
task of Visual Question Answering (VQA). The performance of most models is
clustered around 60-70%. In this paper we propose systematic methods to analyze
the behavior of these models as a first step towards recognizing their
strengths and weak... | computer science |
285 | Sort Story: Sorting Jumbled Images and Captions into Stories | cs.CL | Temporal common sense has applications in AI tasks such as QA, multi-document
summarization, and human-AI communication. We propose the task of sequencing --
given a jumbled set of aligned image-caption pairs that belong to a story, the
task is to sort them such that the output sequence forms a coherent story. We
prese... | computer science |
286 | Mean Box Pooling: A Rich Image Representation and Output Embedding for
the Visual Madlibs Task | cs.CV | We present Mean Box Pooling, a novel visual representation that pools over
CNN representations of a large number, highly overlapping object proposals. We
show that such representation together with nCCA, a successful multimodal
embedding technique, achieves state-of-the-art performance on the Visual
Madlibs task. Moreo... | computer science |
287 | Learning to generalize to new compositions in image understanding | cs.CV | Recurrent neural networks have recently been used for learning to describe
images using natural language. However, it has been observed that these models
generalize poorly to scenes that were not observed during training, possibly
depending too strongly on the statistics of the text in the training data. Here
we propos... | computer science |
288 | Measuring Machine Intelligence Through Visual Question Answering | cs.AI | As machines have become more intelligent, there has been a renewed interest
in methods for measuring their intelligence. A common approach is to propose
tasks for which a human excels, but one which machines find difficult. However,
an ideal task should also be easy to evaluate and not be easily gameable. We
begin with... | computer science |
289 | Towards Transparent AI Systems: Interpreting Visual Question Answering
Models | cs.CV | Deep neural networks have shown striking progress and obtained
state-of-the-art results in many AI research fields in the recent years.
However, it is often unsatisfying to not know why they predict what they do. In
this paper, we address the problem of interpreting Visual Question Answering
(VQA) models. Specifically,... | computer science |
290 | Visual Dialog | cs.CV | We introduce the task of Visual Dialog, which requires an AI agent to hold a
meaningful dialog with humans in natural, conversational language about visual
content. Specifically, given an image, a dialog history, and a question about
the image, the agent has to ground the question in image, infer context from
history, ... | computer science |
291 | Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic
Speech Recognition | cs.CL | Multi-task learning (MTL) involves the simultaneous training of two or more
related tasks over shared representations. In this work, we apply MTL to
audio-visual automatic speech recognition(AV-ASR). Our primary task is to learn
a mapping between audio-visual fused features and frame labels obtained from
acoustic GMM/H... | computer science |
292 | Learning Cooperative Visual Dialog Agents with Deep Reinforcement
Learning | cs.CV | We introduce the first goal-driven training for visual question answering and
dialog agents. Specifically, we pose a cooperative 'image guessing' game
between two agents -- Qbot and Abot -- who communicate in natural language
dialog so that Qbot can select an unseen image from a lineup of images. We use
deep reinforcem... | computer science |
293 | Being Negative but Constructively: Lessons Learnt from Creating Better
Visual Question Answering Datasets | cs.CL | Visual question answering (QA) has attracted a lot of attention lately, seen
essentially as a form of (visual) Turing test that artificial intelligence
should strive to achieve. In this paper, we study a crucial component of this
task: how can we design good datasets for the task? We focus on the design of
multiple-cho... | computer science |
294 | C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0
Dataset | cs.CV | Visual Question Answering (VQA) has received a lot of attention over the past
couple of years. A number of deep learning models have been proposed for this
task. However, it has been shown that these models are heavily driven by
superficial correlations in the training data and lack compositionality -- the
ability to a... | computer science |
295 | Deep learning evaluation using deep linguistic processing | cs.CL | We discuss problems with the standard approaches to evaluation for tasks like
visual question answering, and argue that artificial data can be used to
address these as a complement to current practice. We demonstrate that with the
help of existing 'deep' linguistic processing technology we are able to create
challengin... | computer science |
296 | meProp: Sparsified Back Propagation for Accelerated Deep Learning with
Reduced Overfitting | cs.LG | We propose a simple yet effective technique for neural network learning. The
forward propagation is computed as usual. 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 m... | computer science |
297 | Towards Crafting Text Adversarial Samples | cs.LG | Adversarial samples are strategically modified samples, which are crafted
with the purpose of fooling a classifier at hand. An attacker introduces
specially crafted adversarial samples to a deployed classifier, which are being
mis-classified by the classifier. However, the samples are perceived to be
drawn from entirel... | computer science |
298 | Reinforced Video Captioning with Entailment Rewards | cs.CL | Sequence-to-sequence models have shown promising improvements on the temporal
task of video captioning, but they optimize word-level cross-entropy loss
during training. First, using policy gradient and mixed-loss methods for
reinforcement learning, we directly optimize sentence-level task-based metrics
(as rewards), ac... | computer science |
299 | Hierarchically-Attentive RNN for Album Summarization and Storytelling | cs.CL | We address the problem of end-to-end visual storytelling. Given a photo
album, our model first selects the most representative (summary) photos, and
then composes a natural language story for the album. For this task, we make
use of the Visual Storytelling dataset and a model composed of three
hierarchically-attentive ... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.