pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Nonnegative Matrix Factorization and Measure Comparisons for Recommender Systems
Authors: Gianna M. Del Corso, Francesco Romani
Date: 2016-07-26 00:00:00
Tasks: Recommendation Systems

The Nonnegative Matrix Factorization (NMF) of the rating matrix has shown to be an effective method to tackle the recommendation problem. In this paper we propose new methods based on the NMF of the rating matrix and we compare them with some classical algorithms such as the SVD and the regularized and unregularized non-negative matrix factorization approach. In particular a new algorithm is obtained changing adaptively the function to be minimized at each step, realizing a sort of dynamic prior strategy. Another algorithm is obtained modifying the function to be minimized in the NMF formulation by enforcing the reconstruction of the unknown ratings toward a prior term. We then combine different methods obtaining two mixed strategies which turn out to be very effective in the reconstruction of missing observations. We perform a thoughtful comparison of different methods on the basis of several evaluation measures. We consider in particular rating, classification and ranking measures showing that the algorithm obtaining the best score for a given measure is in general the best also when different measures are considered, lowering the interest in designing specific evaluation measures. The algorithms have been tested on different datasets, in particular the 1M, and 10M MovieLens datasets containing ratings on movies, the Jester dataset with ranting on jokes and Amazon Fine Foods dataset with ratings on foods. The comparison of the different algorithms, shows the good performance of methods employing both an explicit and an implicit regularization scheme. Moreover we can get a boost by mixed strategies combining a fast method with a more accurate one.
Generic Feature Learning for Wireless Capsule Endoscopy Analysis
Authors: Santi Seguí, Michal Drozdzal, Guillem Pascual, Petia Radeva, Carolina Malagelada, Fernando Azpiroz, Jordi Vitrià
Date: 2016-07-26 00:00:00

The interpretation and analysis of the wireless capsule endoscopy recording
is a complex task which requires sophisticated computer aided decision (CAD)
systems in order to help physicians with the video screening and, finally, with
the diagnosis. Most of the CAD systems in the capsule endoscopy share a common
system design, but use very different image and video representations. As a
result, each time a new clinical application of WCE appears, new CAD system has
to be designed from scratch. This characteristic makes the design of new CAD
systems a very time consuming. Therefore, in this paper we introduce a system
for small intestine motility characterization, based on Deep Convolutional
Neural Networks, which avoids the laborious step of designing specific features
for individual motility events. Experimental results show the superiority of
the learned features over alternative classifiers constructed by using state of
the art hand-crafted features. In particular, it reaches a mean classification
accuracy of 96% for six intestinal motility events, outperforming the other
classifiers by a large margin (a 14% relative performance increase).
OntoCat: Automatically categorizing knowledge in API Documentation
Authors: Niraj Kumar, Premkumar Devanbu
Date: 2016-07-26 00:00:00
Tasks: Navigate

Most application development happens in the context of complex APIs;
reference documentation for APIs has grown tremendously in variety, complexity,
and volume, and can be difficult to navigate. There is a growing need to
develop well-organized ways to access the knowledge latent in the
documentation; several research efforts deal with the organization (ontology)
of API-related knowledge. Extensive knowledge-engineering work, supported by a
rigorous qualitative analysis, by Maalej & Robillard [3] has identified a
useful taxonomy of API knowledge. Based on this taxonomy, we introduce a domain
independent technique to extract the knowledge types from the given API
reference documentation. Our system, OntoCat, introduces total nine different
features and their semantic and statistical combinations to classify the
different knowledge types. We tested OntoCat on python API reference
documentation. Our experimental results show the effectiveness of the system
and opens the scope of probably related research areas (i.e., user behavior,
documentation quality, etc.).
Variational Mixture Models with Gamma or inverse-Gamma components
Authors: A. Llera, D. Vidaurre, R. H. R. Pruim, C. F. Beckmann
Date: 2016-07-26 00:00:00
Tasks: blind source separation, regression

Mixture models with Gamma and or inverse-Gamma distributed mixture components
are useful for medical image tissue segmentation or as post-hoc models for
regression coefficients obtained from linear regression within a Generalised
Linear Modeling framework (GLM), used in this case to separate stochastic
(Gaussian) noise from some kind of positive or negative "activation" (modeled
as Gamma or inverse-Gamma distributed). To date, the most common choice in this
context it is Gaussian/Gamma mixture models learned through a maximum
likelihood (ML) approach; we recently extended such algorithm for mixture
models with inverse-Gamma components. Here, we introduce a fully analytical
Variational Bayes (VB) learning framework for both Gamma and/or inverse-Gamma
components. We use synthetic and resting state fMRI data to compare the
performance of the ML and VB algorithms in terms of area under the curve and
computational cost. We observed that the ML Gaussian/Gamma model is very
expensive specially when considering high resolution images; furthermore, these
solutions are highly variable and they occasionally can overestimate the
activations severely. The Bayesian Gauss-Gamma is in general the fastest
algorithm but provides too dense solutions. The maximum likelihood
Gaussian/inverse-Gamma is also very fast but provides in general very sparse
solutions. The variational Gaussian/inverse-Gamma mixture model is the most
robust and its cost is acceptable even for high resolution images. Further, the
presented methodology represents an essential building block that can be
directly used in more complex inference tasks, specially designed to analyse
MRI-fMRI data; such models include for example analytical variational mixture
models with adaptive spatial regularization or better source models for new
spatial blind source separation approaches.
Grounding Dynamic Spatial Relations for Embodied (Robot) Interaction
Authors: Michael Spranger, Jakob Suchan, Mehul Bhatt, Manfred Eppe
Date: 2016-07-26 00:00:00

This paper presents a computational model of the processing of dynamic
spatial relations occurring in an embodied robotic interaction setup. A
complete system is introduced that allows autonomous robots to produce and
interpret dynamic spatial phrases (in English) given an environment of moving
objects. The model unites two separate research strands: computational
cognitive semantics and on commonsense spatial representation and reasoning.
The model for the first time demonstrates an integration of these different
strands.
DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation
Authors: Yaroslav Ganin, Daniil Kononenko, Diana Sungatullina, Victor Lempitsky
Date: 2016-07-25 00:00:00
Tasks: Conditional Image Generation, Image Generation, Resynthesis

In this work, we consider the task of generating highly-realistic images of a
given face with a redirected gaze. We treat this problem as a specific instance
of conditional image generation and suggest a new deep architecture that can
handle this task very well as revealed by numerical comparison with prior art
and a user study. Our deep architecture performs coarse-to-fine warping with an
additional intensity correction of individual pixels. All these operations are
performed in a feed-forward manner, and the parameters associated with
different operations are learned jointly in the end-to-end fashion. After
learning, the resulting neural network can synthesize images with manipulated
gaze, while the redirection angle can be selected arbitrarily from a certain
range and provided as an input to the network.
Generic 3D Convolutional Fusion for image restoration
Authors: Jiqing Wu, Radu Timofte, Luc van Gool
Date: 2016-07-26 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration, Image Super-Resolution, Super-Resolution

Also recently, exciting strides forward have been made in the area of image
restoration, particularly for image denoising and single image
super-resolution. Deep learning techniques contributed to this significantly.
The top methods differ in their formulations and assumptions, so even if their
average performance may be similar, some work better on certain image types and
image regions than others. This complementarity motivated us to propose a novel
3D convolutional fusion (3DCF) method. Unlike other methods adapted to
different tasks, our method uses the exact same convolutional network
architecture to address both image denois- ing and single image
super-resolution. As a result, our 3DCF method achieves substantial
improvements (0.1dB-0.4dB PSNR) over the state-of-the-art methods that it
fuses, and this on standard benchmarks for both tasks. At the same time, the
method still is computationally efficient.
Towards Viewpoint Invariant 3D Human Pose Estimation
Authors: Albert Haque, Boya Peng, Zelun Luo, Alexandre Alahi, Serena Yeung, Li Fei-Fei
Date: 2016-03-23 00:00:00
Tasks: 3D Human Pose Estimation, Multi-Task Learning, Pose Estimation

We propose a viewpoint invariant model for 3D human pose estimation from a
single depth image. To achieve this, our discriminative model embeds local
regions into a learned viewpoint invariant feature space. Formulated as a
multi-task learning problem, our model is able to selectively predict partial
poses in the presence of noise and occlusion. Our approach leverages a
convolutional and recurrent network architecture with a top-down error feedback
mechanism to self-correct previous pose estimates in an end-to-end manner. We
evaluate our model on a previously published depth dataset and a newly
collected human pose dataset containing 100K annotated depth images from
extreme viewpoints. Experiments show that our model achieves competitive
performance on frontal views while achieving state-of-the-art performance on
alternate viewpoints.
Many Languages, One Parser
Authors: Waleed Ammar, George Mulcaire, Miguel Ballesteros, Chris Dyer, Noah A. Smith
Date: 2016-02-04 00:00:00
Proceeding: TACL 2016 1
Tasks: Cross-lingual zero-shot dependency parsing, Dependency Parsing, POS

We train one multilingual model for dependency parsing and use it to parse
sentences in several languages. The parsing model uses (i) multilingual word
clusters and embeddings; (ii) token-level language information; and (iii)
language-specific features (fine-grained POS tags). This input representation
enables the parser not only to parse effectively in multiple languages, but
also to generalize across languages based on linguistic universals and
typological similarities, making it more effective to learn from limited
annotations. Our parser's performance compares favorably to strong baselines in
a range of data scenarios, including when the target language has a large
treebank, a small treebank, or no treebank for training.
Top-Down Learning for Structured Labeling with Convolutional Pseudoprior
Authors: Saining Xie, Xun Huang, Zhuowen Tu
Date: 2015-11-23 00:00:00

Current practice in convolutional neural networks (CNN) remains largely
bottom-up and the role of top-down process in CNN for pattern analysis and
visual inference is not very clear. In this paper, we propose a new method for
structured labeling by developing convolutional pseudo-prior (ConvPP) on the
ground-truth labels. Our method has several interesting properties: (1)
compared with classical machine learning algorithms like CRFs and Structural
SVM, ConvPP automatically learns rich convolutional kernels to capture both
short- and long- range contexts; (2) compared with cascade classifiers like
Auto-Context, ConvPP avoids the iterative steps of learning a series of
discriminative classifiers and automatically learns contextual configurations;
(3) compared with recent efforts combing CNN models with CRFs and RNNs, ConvPP
learns convolution in the labeling space with much improved modeling capability
and less manual specification; (4) compared with Bayesian models like MRFs,
ConvPP capitalizes on the rich representation power of convolution by
automatically learning priors built on convolutional filters. We accomplish our
task using pseudo-likelihood approximation to the prior under a novel
fixed-point network structure that facilitates an end-to-end learning process.
We show state-of-the-art results on sequential labeling and image labeling
benchmarks.
Actions ~ Transformations
Authors: Xiaolong Wang, Ali Farhadi, Abhinav Gupta
Date: 2015-12-02 00:00:00
Proceeding: CVPR 2016 6
Tasks: Action Recognition, Temporal Action Localization

What defines an action like "kicking ball"? We argue that the true meaning of
an action lies in the change or transformation an action brings to the
environment. In this paper, we propose a novel representation for actions by
modeling an action as a transformation which changes the state of the
environment before the action happens (precondition) to the state after the
action (effect). Motivated by recent advancements of video representation using
deep learning, we design a Siamese network which models the action as a
transformation on a high-level feature space. We show that our model gives
improvements on standard action recognition datasets including UCF101 and
HMDB51. More importantly, our approach is able to generalize beyond learned
action categories and shows significant performance improvement on
cross-category generalization on our new ACT dataset.
Generative Image Modeling using Style and Structure Adversarial Networks
Authors: Xiaolong Wang, Abhinav Gupta
Date: 2016-03-17 00:00:00
Tasks: Generative Adversarial Network, Image Generation

Current generative frameworks use end-to-end learning and generate images by
sampling from uniform noise distribution. However, these approaches ignore the
most basic principle of image formation: images are product of: (a) Structure:
the underlying 3D model; (b) Style: the texture mapped onto structure. In this
paper, we factorize the image generation process and propose Style and
Structure Generative Adversarial Network (S^2-GAN). Our S^2-GAN has two
components: the Structure-GAN generates a surface normal map; the Style-GAN
takes the surface normal map as input and generates the 2D image. Apart from a
real vs. generated loss function, we use an additional loss with computed
surface normals from generated images. The two GANs are first trained
independently, and then merged together via joint learning. We show our S^2-GAN
model is interpretable, generates more realistic images and can be used to
learn unsupervised RGBD representations.
The Curious Robot: Learning Visual Representations via Physical Interactions
Authors: Lerrel Pinto, Dhiraj Gandhi, Yuanfeng Han, Yong-Lae Park, Abhinav Gupta
Date: 2016-04-05 00:00:00
Tasks: image-classification, Image Classification, Representation Learning, Retrieval

What is the right supervisory signal to train visual representations? Current
approaches in computer vision use category labels from datasets such as
ImageNet to train ConvNets. However, in case of biological agents, visual
representation learning does not require millions of semantic labels. We argue
that biological agents use physical interactions with the world to learn visual
representations unlike current vision systems which just use passive
observations (images and videos downloaded from web). For example, babies push
objects, poke them, put them in their mouth and throw them to learn
representations. Towards this goal, we build one of the first systems on a
Baxter platform that pushes, pokes, grasps and observes objects in a tabletop
environment. It uses four different types of physical interactions to collect
more than 130K datapoints, with each datapoint providing supervision to a
shared ConvNet architecture allowing us to learn visual representations. We
show the quality of learned representations by observing neuron activations and
performing nearest neighbor retrieval on this learned representation.
Quantitatively, we evaluate our learned ConvNet on image classification tasks
and show improvements compared to learning without external data. Finally, on
the task of instance retrieval, our network outperforms the ImageNet network on
recall@1 by 3%
Dynamic Probabilistic Network Based Human Action Recognition
Authors: Anne Veenendaal, Eddie Jones, Zhao Gang, Elliot Daly, Sumalini Vartak, Rahul Patwardhan
Date: 2016-07-26 00:00:00
Tasks: Action Recognition, Temporal Action Localization, Time Series, Time Series Analysis

This paper examines use of dynamic probabilistic networks (DPN) for human
action recognition. The actions of lifting objects and walking in the room,
sitting in the room and neutral standing pose were used for testing the
classification. The research used the dynamic interrelation between various
different regions of interest (ROI) on the human body (face, body, arms, legs)
and the time series based events related to the these ROIs. This dynamic links
are then used to recognize the human behavioral aspects in the scene. First a
model is developed to identify the human activities in an indoor scene and this
model is dependent on the key features and interlinks between the various
dynamic events using DPNs. The sub ROI are classified with DPN to associate the
combined interlink with a specific human activity. The recognition accuracy
performance between indoor (controlled lighting conditions) is compared with
the outdoor lighting conditions. The accuracy in outdoor scenes was lower than
the controlled environment.
Salient Object Subitizing
Authors: Jianming Zhang, Shugao Ma, Mehrnoosh Sameki, Stan Sclaroff, Margrit Betke, Zhe Lin, Xiaohui Shen, Brian Price, Radomir Mech
Date: 2016-07-26 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Retrieval, Object, object-detection, Object Detection, Retrieval, RGB Salient Object Detection, Salient Object Detection

We study the problem of Salient Object Subitizing, i.e. predicting the
existence and the number of salient objects in an image using holistic cues.
This task is inspired by the ability of people to quickly and accurately
identify the number of items within the subitizing range (1-4). To this end, we
present a salient object subitizing image dataset of about 14K everyday images
which are annotated using an online crowdsourcing marketplace. We show that
using an end-to-end trained Convolutional Neural Network (CNN) model, we
achieve prediction accuracy comparable to human performance in identifying
images with zero or one salient object. For images with multiple salient
objects, our model also provides significantly better than chance performance
without requiring any localization process. Moreover, we propose a method to
improve the training of the CNN subitizing model by leveraging synthetic
images. In experiments, we demonstrate the accuracy and generalizability of our
CNN subitizing model and its applications in salient object detection and image
retrieval.
Deepr: A Convolutional Net for Medical Records
Authors: Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, Svetha Venkatesh
Date: 2016-07-26 00:00:00
Tasks: Feature Engineering

Feature engineering remains a major bottleneck when creating predictive
systems from electronic medical records. At present, an important missing
element is detecting predictive regular clinical motifs from irregular episodic
records. We present Deepr (short for Deep record), a new end-to-end deep
learning system that learns to extract features from medical records and
predicts future risk automatically. Deepr transforms a record into a sequence
of discrete elements separated by coded time gaps and hospital transfers. On
top of the sequence is a convolutional neural net that detects and combines
predictive local clinical motifs to stratify the risk. Deepr permits
transparent inspection and visualization of its inner working. We validate
Deepr on hospital data to predict unplanned readmission after discharge. Deepr
achieves superior accuracy compared to traditional techniques, detects
meaningful clinical motifs, and uncovers the underlying structure of the
disease and intervention space.
Knowledge Transfer for Scene-specific Motion Prediction
Authors: Lamberto Ballan, Francesco Castaldo, Alexandre Alahi, Francesco Palmieri, Silvio Savarese
Date: 2016-03-22 00:00:00
Tasks: motion prediction, Prediction, Trajectory Prediction, Transfer Learning

When given a single frame of the video, humans can not only interpret the
content of the scene, but also they are able to forecast the near future. This
ability is mostly driven by their rich prior knowledge about the visual world,
both in terms of (i) the dynamics of moving agents, as well as (ii) the
semantic of the scene. In this work we exploit the interplay between these two
key elements to predict scene-specific motion patterns. First, we extract patch
descriptors encoding the probability of moving to the adjacent patches, and the
probability of being in that particular patch or changing behavior. Then, we
introduce a Dynamic Bayesian Network which exploits this scene specific
knowledge for trajectory prediction. Experimental results demonstrate that our
method is able to accurately predict trajectories and transfer predictions to a
novel scene characterized by similar elements.
Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder
Authors: Soroush Vosoughi, Prashanth Vijayaraghavan, Deb Roy
Date: 2016-07-26 00:00:00
Tasks: Decoder, Semantic Similarity, Semantic Textual Similarity

We present Tweet2Vec, a novel method for generating general-purpose vector
representation of tweets. The model learns tweet embeddings using
character-level CNN-LSTM encoder-decoder. We trained our model on 3 million,
randomly selected English-language tweets. The model was evaluated using two
methods: tweet semantic similarity and tweet sentiment categorization,
outperforming the previous state-of-the-art in both tasks. The evaluations
demonstrate the power of the tweet embeddings generated by our model for
various tweet categorization tasks. The vector representations generated by our
model are generic, and hence can be applied to a variety of tasks. Though the
model presented in this paper is trained on English-language tweets, the method
presented can be used to learn tweet embeddings for different languages.
Spot On: Action Localization from Pointly-Supervised Proposals
Authors: Pascal Mettes, Jan C. van Gemert, Cees G. M. Snoek
Date: 2016-04-26 00:00:00
Tasks: Action Localization, Multiple Instance Learning, Temporal Localization

We strive for spatio-temporal localization of actions in videos. The
state-of-the-art relies on action proposals at test time and selects the best
one with a classifier trained on carefully annotated box annotations.
Annotating action boxes in video is cumbersome, tedious, and error prone.
Rather than annotating boxes, we propose to annotate actions in video with
points on a sparse subset of frames only. We introduce an overlap measure
between action proposals and points and incorporate them all into the objective
of a non-convex Multiple Instance Learning optimization. Experimental
evaluation on the UCF Sports and UCF 101 datasets shows that (i)
spatio-temporal proposals can be used to train classifiers while retaining the
localization performance, (ii) point annotations yield results comparable to
box annotations while being significantly faster to annotate, (iii) with a
minimum amount of supervision our approach is competitive to the
state-of-the-art. Finally, we introduce spatio-temporal action annotations on
the train and test videos of Hollywood2, resulting in Hollywood2Tubes,
available at http://tinyurl.com/hollywood2tubes.