Articles About Machine Learning

Residual Spatial Attention Network for Retinal Vessel Segmentation

Reliable segmentation of retinal vessels can be employed as a way of monitoring and diagnosing certain diseases, such as diabetes and hypertension, as they affect the retinal vascular structure. In this work, we propose the Residual Spatial Attention Network (RSAN) for retinal vessel segmentation… RSAN employs a modified residual block structure that integrates DropBlock, which can not only be utilized to construct deep networks to extract more complex vascular features, but can also effectively alleviate the overfitting. Moreover, in order […]

September 23, 2020 Machine Learning, TIME SERIES Leave a comment

Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change

Biology is both an important application area and a source of motivation for development of advanced machine learning techniques. Although much attention has been paid to large and complex data sets resulting from high-throughput sequencing, advances in high-quality video recording technology have begun to generate similarly rich data sets requiring sophisticated techniques from both computer vision and time-series analysis… Moreover, just as studying gene expression patterns in one organism can reveal general principles that apply to other organisms, the study […]

September 23, 2020 Machine Learning Leave a comment

Efficient Certification of Spatial Robustness

Recent work has exposed the vulnerability of computer vision models to spatial transformations. Due to the widespread usage of such models in safety-critical applications, it is crucial to quantify their robustness against spatial transformations… However, existing work only provides empirical quantification of spatial robustness via adversarial attacks, which lack provable guarantees. In this work, we propose novel convex relaxations, which enable us, for the first time, to provide a certificate of robustness against spatial transformations. Our convex relaxations are model-agnostic […]

September 22, 2020 ANOMALY DETECTION, Machine Learning Leave a comment

MStream: Fast Streaming Multi-Aspect Group Anomaly Detection

Given a stream of entries in a multi-aspect data setting i.e., entries having multiple dimensions, how can we detect anomalous activities? For example, in the intrusion detection setting, existing work seeks to detect anomalous events or edges in dynamic graph streams, but this does not allow us to take into account additional attributes of each entry… Our work aims to define a streaming multi-aspect data anomaly detection framework, termed MStream, which can detect unusual group anomalies as they occur, in […]

September 22, 2020 INTRUSION DETECTION, Machine Learning Leave a comment

Intrusion Detection for Cyber-Physical Systems using Generative Adversarial Networks in Fog Environment

Cyber-attacks on cyber-physical systems (CPSs) can lead to sensing and actuation misbehavior, severe damages to physical objects, and safety risks. Machine learning algorithms have been proposed for hindering cyber-attacks on CPSs, but the absence of labeled data from novel attacks makes their detection quite challenging… In this context, Generative Adversarial Networks (GANs) are a promising unsupervised approach to detect cyber-attacks by implicitly modeling the system. However, the detection of cyber-attacks on CPSs has strict latency requirements, since the attacks need […]

September 22, 2020 CROSS-LINGUAL TRANSFER, Machine Learning Leave a comment

FarsTail: A Persian Natural Language Inference Dataset

Natural language inference (NLI) is known as one of the central tasks in natural language processing (NLP) which encapsulates many fundamental aspects of language understanding. With the considerable achievements of data-hungry deep learning methods in NLP tasks, a great amount of effort has been devoted to develop more diverse datasets for different languages… In this paper, we present a new dataset for the NLI task in the Persian language, also known as Farsi, which is one of the dominant languages […]

September 22, 2020 Machine Learning, SEMANTIC PARSING Leave a comment

Progressive Semantic-Aware Style Transformation for Blind Face Restoration

Face restoration is important in face image processing, and has been widely studied in recent years. However, previous works often fail to generate plausible high quality (HQ) results for real-world low quality (LQ) face images… In this paper, we propose a new progressive semantic-aware style transformation framework, named PSFR-GAN, for face restoration. Specifically, instead of using an encoder-decoder framework as previous methods, we formulate the restoration of LQ face images as a multi-scale progressive restoration procedure through semantic-aware style transformation. […]

September 21, 2020 3D SHAPE REPRESENTATION, Machine Learning Leave a comment

Novel View Synthesis from Single Images via Point Cloud Transformation

In this paper the argument is made that for true novel view synthesis of objects, where the object can be synthesized from any viewpoint, an explicit 3D shape representation isdesired. Our method estimates point clouds to capture the geometry of the object, which can be freely rotated into the desired view and then projected into a new image… This image, however, is sparse by nature and hence this coarse view is used as the input of an image completion network […]

September 21, 2020 Machine Learning Leave a comment

ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis

Manually authoring 3D shapes is difficult and time consuming; generative models of 3D shapes offer compelling alternatives. Procedural representations are one such possibility: they offer high-quality and editable results but are difficult to author and often produce outputs with limited diversity… On the other extreme are deep generative models: given enough data, they can learn to generate any class of shape but their outputs have artifacts and the representation is not editable. In this paper, we take a step towards […]

September 21, 2020 CAUSAL INFERENCE, Machine Learning Leave a comment

Understanding Effects of Editing Tweets for News Sharing by Media Accounts through a Causal Inference Framework

To reach a broader audience and optimize traffic toward news articles, media outlets commonly run social media accounts and share their content with a short text summary. Despite its importance of writing a compelling message in sharing articles, research community does not own a sufficient level of understanding of what kinds of editing strategies are effective in promoting audience engagement… In this study, we aim to fill the gap by analyzing the current practices of media outlets using a data-driven […]

« 1 … 215 216 217 218 219 … 222 »