64 research outputs found
Adversarial Personalized Ranking for Recommendation
Item recommendation is a personalized ranking task. To this end, many
recommender systems optimize models with pairwise ranking objectives, such as
the Bayesian Personalized Ranking (BPR). Using matrix Factorization (MF) ---
the most widely used model in recommendation --- as a demonstration, we show
that optimizing it with BPR leads to a recommender model that is not robust. In
particular, we find that the resultant model is highly vulnerable to
adversarial perturbations on its model parameters, which implies the possibly
large error in generalization.
To enhance the robustness of a recommender model and thus improve its
generalization performance, we propose a new optimization framework, namely
Adversarial Personalized Ranking (APR). In short, our APR enhances the pairwise
ranking method BPR by performing adversarial training. It can be interpreted as
playing a minimax game, where the minimization of the BPR objective function
meanwhile defends an adversary, which adds adversarial perturbations on model
parameters to maximize the BPR objective function. To illustrate how it works,
we implement APR on MF by adding adversarial perturbations on the embedding
vectors of users and items. Extensive experiments on three public real-world
datasets demonstrate the effectiveness of APR --- by optimizing MF with APR, it
outperforms BPR with a relative improvement of 11.2% on average and achieves
state-of-the-art performance for item recommendation. Our implementation is
available at: https://github.com/hexiangnan/adversarial_personalized_ranking.Comment: SIGIR 201
Correlation length scalings in fusion edge plasma turbulence computations
The effect of changes in plasma parameters, that are characteristic near or
at an L-H transition in fusion edge plasmas, on fluctuation correlation lengths
are analysed by means of drift-Alfven turbulence computations. Scalings by
density gradient length, collisionality, plasma beta, and by an imposed shear
flow are considered. It is found that strongly sheared flows lead to the
appearence of long-range correlations in electrostatic potential fluctuations
parallel and perpendicular to the magnetic field.Comment: Submitted to "Plasma Physics and Controlled Fusion
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training
We study the recently introduced stability training as a general-purpose
method to increase the robustness of deep neural networks against input
perturbations. In particular, we explore its use as an alternative to data
augmentation and validate its performance against a number of distortion types
and transformations including adversarial examples. In our image classification
experiments using ImageNet data stability training performs on a par or even
outperforms data augmentation for specific transformations, while consistently
offering improved robustness against a broader range of distortion strengths
and types unseen during training, a considerably smaller hyperparameter
dependence and less potentially negative side effects compared to data
augmentation.Comment: 18 pages, 25 figures; Camera-ready versio
TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning
Fusing data from multiple modalities provides more information to train
machine learning systems. However, it is prohibitively expensive and
time-consuming to label each modality with a large amount of data, which leads
to a crucial problem of semi-supervised multi-modal learning. Existing methods
suffer from either ineffective fusion across modalities or lack of theoretical
guarantees under proper assumptions. In this paper, we propose a novel
information-theoretic approach, namely \textbf{T}otal \textbf{C}orrelation
\textbf{G}ain \textbf{M}aximization (TCGM), for semi-supervised multi-modal
learning, which is endowed with promising properties: (i) it can utilize
effectively the information across different modalities of unlabeled data
points to facilitate training classifiers of each modality (ii) it has
theoretical guarantee to identify Bayesian classifiers, i.e., the ground truth
posteriors of all modalities. Specifically, by maximizing TC-induced loss
(namely TC gain) over classifiers of all modalities, these classifiers can
cooperatively discover the equivalent class of ground-truth classifiers; and
identify the unique ones by leveraging limited percentage of labeled data. We
apply our method to various tasks and achieve state-of-the-art results,
including news classification, emotion recognition and disease prediction.Comment: ECCV 2020 (oral
Transformation Consistency Regularization – A Semi-supervised Paradigm for Image-to-Image Translation
Scarcity of labeled data has motivated the development of semi-supervised
learning methods, which learn from large portions of unlabeled data alongside a
few labeled samples. Consistency Regularization between model's predictions
under different input perturbations, particularly has shown to provide
state-of-the art results in a semi-supervised framework. However, most of these
method have been limited to classification and segmentation applications. We
propose Transformation Consistency Regularization, which delves into a more
challenging setting of image-to-image translation, which remains unexplored by
semi-supervised algorithms. The method introduces a diverse set of geometric
transformations and enforces the model's predictions for unlabeled data to be
invariant to those transformations. We evaluate the efficacy of our algorithm
on three different applications: image colorization, denoising and
super-resolution. Our method is significantly data efficient, requiring only
around 10 - 20% of labeled samples to achieve similar image reconstructions to
its fully-supervised counterpart. Furthermore, we show the effectiveness of our
method in video processing applications, where knowledge from a few frames can
be leveraged to enhance the quality of the rest of the movie
IntersectGAN: Learning Domain Intersection for Generating Images with Multiple Attributes
Generative adversarial networks (GANs) have demonstrated great success in
generating various visual content. However, images generated by existing GANs
are often of attributes (e.g., smiling expression) learned from one image
domain. As a result, generating images of multiple attributes requires many
real samples possessing multiple attributes which are very resource expensive
to be collected. In this paper, we propose a novel GAN, namely IntersectGAN, to
learn multiple attributes from different image domains through an intersecting
architecture. For example, given two image domains and with certain
attributes, the intersection denotes a new domain where images
possess the attributes from both and domains. The proposed
IntersectGAN consists of two discriminators and to distinguish
between generated and real samples of different domains, and three generators
where the intersection generator is trained against both discriminators. And an
overall adversarial loss function is defined over three generators. As a
result, our proposed IntersectGAN can be trained on multiple domains of which
each presents one specific attribute, and eventually eliminates the need of
real sample images simultaneously possessing multiple attributes. By using the
CelebFaces Attributes dataset, our proposed IntersectGAN is able to produce
high quality face images possessing multiple attributes (e.g., a face with
black hair and a smiling expression). Both qualitative and quantitative
evaluations are conducted to compare our proposed IntersectGAN with other
baseline methods. Besides, several different applications of IntersectGAN have
been explored with promising results
Intramucosal adenocarcinoma of the ileum originated 40 years after ileosigmoidostomy
<p>Abstract</p> <p>Background</p> <p>Small bowel adenocarcinomas (SBAs) are rare carcinomas. They are asymptomatic and usually neither endoscopy nor contrast studies are performed for screening</p> <p>Case presentation</p> <p>A 72-year-old Japanese male had a positive fecal occult blood test at a regular check-up in 2006. He suffered appendicitis and received an ileosigmoidostomy in 1966. A colonoscopy revealed an irregular mucosal lesion with an unclear margin at the ileum side of the anastomosis. A mucosal biopsy specimen showed adenocarcinoma histopathologically. Excision of the anastomosis was performed for this patient. The resected specimen showed a flat mucosal lesion with a slight depression at the ileum adjacent to the anastomosis. Histological examination revealed a well differentiated intramucosal adenocarcinoma (adenocarcinoma in situ). Immunohistological staining demonstrated the overexpression of p53 protein in the adenocarcinoma.</p> <p>Conclusion</p> <p>Adenocarcinoma of the ileum at such an early stage is a very rare event. In this case, there is a possibility that the ileosigmoidostomy resulted in a back flow of colonic stool to the ileum that caused the carcinogenesis of the small intestine.</p
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