nils-herrmann/clean_scirepeval huggingface dataset:
Hard Negative Mining for Metric Learning Based Zero-Shot Classification

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. , which proposed an approach to solve ZeroShot classification problems (ZSC) by introducing a novel metric learning based objective function. This objective function allows to learn an optimal embedding of the attributes jointly with a measure of similarity between images and attributes. This paper extends their approach by proposing several schemes to control the generation of the negative pairs, resulting in a significant improvement of the performance and giving above state-of-the-art results on three challenging ZSC datasets.
NIPS 2016 Tutorial: Generative Adversarial Networks

This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks (GANs). The tutorial describes: (1) Why generative modeling is a topic worth studying, (2) how generative models work, and how GANs compare to other generative models, (3) the details of how GANs work, (4) research frontiers in GANs, and (5) state-of-the-art image models that combine GANs with other methods. Finally, the tutorial contains three exercises for readers to complete, and the solutions to these exercises.  This report summarizes the content of the NIPS 2016 tutorial on generative adversarial networks (GANs) (Goodfellow et al., 2014b). The tutorial was designed primarily to ensure that it answered most of the questions asked by audience members ahead of time, in order to make sure that the tutorial would be as useful as possible to the audience. This tutorial is not intended to be a comprehensive review of the field of GANs; many excellent papers are not described here, simply because they were not relevant to answering the most frequent questions, and because the tutorial was delivered as a two hour oral presentation and did not have unlimited time cover all subjects. The tutorial describes: (1) Why generative modeling is a topic worth studying, (2) how generative models work, and how GANs compare to other generative models, (3) the details of how GANs work, (4) research frontiers in GANs, and (5) state-of-the-art image models that combine GANs with other methods. Finally, the tutorial contains three exercises for readers to complete, and the solutions to these exercises. The slides for the tutorial are available in PDF and Keynote format at the following URLs: http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf 1This is the arxiv.org version of this tutorial. Some graphics have been compressed to respect arxiv.org’s 10MB limit on paper size, and do not reflect the full image quality. 1 ar X iv :1 70 1. 00 16 0v 4 [ cs .L G ] 3 A pr 2 01 7
Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation

Level set methods have been widely used to implement active contours for image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may hinder the accuracy of any segmentation method based on active contours implemented using level set methods. This paper proposes a method based on active contours implemented using level set methods for segmentation of such medical images. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region located inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the image’s gradient vector flow field and the evolving contour’s normal. We evaluate the proposed method for segmentation of various regions in real MRI and CT slices, X-ray images, and ultra sound images. Evaluation results confirm the advantage of weighting energy forces using local edge features to reduce leakage. These results also show that the proposed method leads to more accurate boundary detection results than the state-of-the-art edge-based level set segmentation methods, particularly around weak edges.
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs

The availability of large scale event data with time stamps has given rise to dynamically evolving knowledge graphs that contain temporal information for each edge. Reasoning over time in such dynamic knowledge graphs is not yet well understood. To this end, we present Know-Evolve, a novel deep evolutionary knowledge network that learns non-linearly evolving entity representations over time. The occurrence of a fact (edge) is modeled as a multivariate point process whose intensity function is modulated by the score for that fact computed based on the learned entity embeddings. We demonstrate significantly improved performance over various relational learning approaches on two large scale real-world datasets. Further, our method effectively predicts occurrence or recurrence time of a fact which is novel compared to prior reasoning approaches in multirelational setting.
K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms

Guided by an initial idea of building a complex (non linear) d ecision surface with maximalocal margin in input space, we give a possible geometrical intuition as to why K-Nearest Neighbor (KNN) al gorithms often perform more poorly than SVMs on classification tasks. We then propose modified K-Nearest Neighbor algorithms to overcome the perceived problem. The approach is similar in spirit to Tangent Distance , but with invariances inferred from the local neighborhood rath er than prior knowledge. Experimental results on real world classificati on asks suggest that the modified KNN algorithms often give a dramatic im provement over standard KNN and perform as well or better than SVMs .
Adversarial PoseNet: A Structure-Aware Convolutional Network for Human Pose Estimation

For human pose estimation in monocular images, joint occlusions and overlapping upon human bodies often result in deviated pose predictions. Under these circumstances, biologically implausible pose predictions may be produced. In contrast, human vision is able to predict poses by exploiting geometric constraints of joint inter-connectivity. To address the problem by incorporating priors about the structure of human bodies, we propose a novel structure-aware convolutional network to implicitly take such priors into account during training of the deep network. Explicit learning of such constraints is typically challenging. Instead, we design discriminators to distinguish the real poses from the fake ones (such as biologically implausible ones). If the pose generator (G) generates results that the discriminator fails to distinguish from real ones, the network successfully learns the priors.,,To better capture the structure dependency of human body joints, the generator G is designed in a stacked multi-task manner to predict poses as well as occlusion heatmaps. Then, the pose and occlusion heatmaps are sent to the discriminators to predict the likelihood of the pose being real. Training of the network follows the strategy of conditional Generative Adversarial Networks (GANs). The effectiveness of the proposed network is evaluated on two widely used human pose estimation benchmark datasets. Our approach significantly outperforms the state-of-the-art methods and almost always generates plausible human pose predictions.
Microsoft's Submission to the WMT2018 News Translation Task: How I Learned to Stop Worrying and Love the Data

This paper describes the Microsoft submission to the WMT2018 news translation shared task. We participated in one language direction – English-German. Our system follows current best-practice and combines state-of-theart models with new data filtering (dual conditional cross-entropy filtering) and sentence weighting methods. We trained fairly standard Transformer-big models with an updated version of Edinburgh’s training scheme for WMT2017 and experimented with different filtering schemes for Paracrawl. According to automatic metrics (BLEU) we reached the highest score for this subtask with a nearly 2 BLEU point margin over the next strongest system. Based on human evaluation we ranked first among constrained systems. We believe this is mostly caused by our data filtering/weighting regime.
NODDI: Practical <ce:italic>in vivo</ce:italic> neurite orientation dispersion and density imaging of the human brain

This paper introduces neurite orientation dispersion and density imaging (NODDI), a practical diffusion MRI technique for estimating the microstructural complexity of dendrites and axons in vivo on clinical MRI scanners. Such indices of neurites relate more directly to and provide more specific markers of brain tissue microstructure than standard indices from diffusion tensor imaging, such as fractional anisotropy (FA). Mapping these indices over the whole brain on clinical scanners presents new opportunities for understanding brain development and disorders. The proposed technique enables such mapping by combining a three-compartment tissue model with a two-shell high-angular-resolution diffusion imaging (HARDI) protocol optimized for clinical feasibility. An index of orientation dispersion is defined to characterize angular variation of neurites. We evaluate the method both in simulation and on a live human brain using a clinical 3T scanner. Results demonstrate that NODDI provides sensible neurite density and orientation dispersion estimates, thereby disentangling two key contributing factors to FA and enabling the analysis of each factor individually. We additionally show that while orientation dispersion can be estimated with just a single HARDI shell, neurite density requires at least two shells and can be estimated more accurately with the optimized two-shell protocol than with alternative two-shell protocols. The optimized protocol takes about 30 min to acquire, making it feasible for inclusion in a typical clinical setting. We further show that sampling fewer orientations in each shell can reduce the acquisition time to just 10 min with minimal impact on the accuracy of the estimates. This demonstrates the feasibility of NODDI even for the most time-sensitive clinical applications, such as neonatal and dementia imaging.
Feature Weighting for Lazy Learning Algorithms

Learning algorithms diier in the degree to which they process their inputs prior to their use in performance tasks. Many algorithms eagerly compile input samples and use only the compilations to make decisions. Others are lazy: they perform less precompilation and use the input samples to guide decision making. The performance of many lazy learners signiicantly degrades when samples are deened by features containing little or misleading information. Distinguishing feature relevance is a critical issue for these algorithms, and many solutions have been developed that assign weights to features. This chapter introduces a cat-egorization framework for feature weighting approaches used in lazy similarity learners and brieey surveys some examples in each category.  Lazy learning algorithms are machine learning algorithms (Mitchell, 1997) that are welcome members of procrastinators anonymous. Purely lazy learners typically display the following characteristics (Aha, 1997): 1. Defer: They delay the processing of their inputs until they receive requests for information; they simply store their inputs for future use. 2. Demand-Driven: They reply to information queries by combining information from their stored (e.g., training) samples. 3.  They delete the constructed query and any intermediate results. In contrast, eager algorithms greedily replace their inputs with an abstraction (e.g., a rule set, decision tree, or neural network) and use it to process queries.
Knowledge Acquisition for Visual Question Answering via Iterative Querying

Humans possess an extraordinary ability to learn new skills and new knowledge for problem solving. Such learning ability is also required by an automatic model to deal with arbitrary, open-ended questions in the visual world. We propose a neural-based approach to acquiring task-driven information for visual question answering (VQA). Our model proposes queries to actively acquire relevant information from external auxiliary data. Supporting evidence from either human-curated or automatic sources is encoded and stored into a memory bank. We show that acquiring task-driven evidence effectively improves model performance on both the Visual7W and VQA datasets, moreover, these queries offer certain level of interpretability in our iterative QA model.
Type-2 fuzzy sets made simple

Type-2 fuzzy sets let us model and minimize the effects of uncertainties in rule-base fuzzy logic systems. However, they are difficult to understand for a variety of reasons which we enunciate. In this paper, we strive to overcome the difficulties by: 1) establishing a small set of terms that let us easily communicate about type-2 fuzzy sets and also let us define such sets very precisely, 2) presenting a new representation for type-2 fuzzy sets, and 3) using this new representation to derive formulas for union, intersection and complement of type-2 fuzzy sets without having to use the Extension Principle.
Scratch: A Sneak Preview

information technologies as “the ability to reformulate knowledge, to express oneself creatively and appropriately, and to produce and generate information (rather than simply to comprehend it).” Fluency, according to the report, “goes beyond traditional notions of computer literacy...[It] requires a deeper, more essential understanding and mastery of information technology for information processing, communication, and problem solving than does computer literacy as traditionally defined.” Scratch is a networked, media-rich programming environment designed to enhance the development of technological fluency at after-school centers in economically-disadvantaged communities. Just as the LEGO MindStorms robotics kit added programmability to an activity deeply rooted in youth culture (building with LEGO bricks), Scratch adds programmability to the media-rich and network-based activities that are most popular among youth at afterschool computer centers. Taking advantage of the extraordinary processing power of current computers, Scratch supports new programming paradigms and activities that were previously infeasible, making it better positioned to succeed than previous attempts to introduce programming to youth. In the past, most initiatives to improve technological fluency have focused on school classrooms. But there is a growing recognition that after-school centers and other informal learning settings can play an important role, especially in economicallydisadvantaged communities, where schools typically have few technological resources and many young people are alienated from the formal education system. Our working hypothesis is that, as kids work on personally meaningful Scratch projects such as animated stories, games, and interactive art, they will develop technological fluency, mathematical and problem solving skills, and a justifiable selfconfidence that will serve them well in the wider spheres of their lives. During the past decade, more than 2000 community technology centers (CTCs) opened in the United States, specifically to provide better access to technology in economically-disadvantaged communities. But most CTCs support only the most basic computer activities such as word processing, email, and Web browsing, so participants do not gain the type of fluency described in the NRC report. Similarly, many after-school centers (which, unlike CTCs, focus exclusively on youth) have begun to introduce computers, but they too tend to offer only introductory computer activities, sometimes augmented by educational games.
Twitter sentiment analysis

Social media have received more attention nowadays. Public and private opinion about a wide variety of subjects are expressed and spread continually via numerous social media. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyze customers' perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers' perceptions. This paper reports on the design of a sentiment analysis, extracting a vast amount of tweets. Prototyping is used in this development. Results classify customers' perspective via tweets into positive and negative, which is represented in a pie chart and html page. However, the program has planned to develop on a web application system, but due to limitation of Django which can be worked on a Linux server or LAMP, for further this approach need to be done.
Loss is its own Reward: Self-Supervision for Reinforcement Learning

Reinforcement learning, driven by reward, addresses tasks by optimizing policies for expected return. Need the supervision be so narrow? Reward is delayed and sparse for many tasks, so we argue that reward alone is a difficult and impoverished signal for end-to-end optimization. To augment reward, we consider a range of self-supervised tasks that incorporate states, actions, and successors to provide auxiliary losses. These losses offer ubiquitous and instantaneous supervision for representation learning even in the absence of reward. While current results show that learning from reward alone is feasible, pure reinforcement learning methods are constrained by computational and data efficiency issues that can be remedied by auxiliary losses. Self-supervised pre-training improves the data efficiency and policy returns of end-to-end reinforcement learning.
Breaking boundaries: the uses & gratifications of grindr

"People-nearby applications" (PNAs) are a form of ubiquitous computing that connect users based on their physical location data. One example is Grindr, a popular PNA that facilitates connections among gay and bisexual men. Adopting a uses and gratifications approach, we conducted two studies. In study one, 63 users reported motivations for Grindr use through open-ended descriptions. In study two, those descriptions were coded into 26 items that were completed by 525 Grindr users. Factor analysis revealed six uses and gratifications: social inclusion, sex, friendship, entertainment, romantic relationships, and location-based search. Two additional analyses examine (1) the effects of geographic location (e.g., urban vs. suburban/rural) on men's use of Grindr and (2) how Grindr use is related to self-disclosure of information. Results highlight how the mixed-mode nature of PNA technology may change the boundaries of online and offline space, and how gay and bisexual men navigate physical environments.
Satellite image classification using granular neural networks

The increased synergy between neural networks (NN) and fuzzy sets has led to the introduction of granular neural networks (GNNs) that operate on granules of information, rather than information itself. The fact that processing is done on a conceptual rather than on a numerical level, combined with the representation of granules using linguistic terms, results in increased interpretability. This is the actual benefit, and not increased accuracy, gained by GNNs. The constraints used to implement the GNN are such that accuracy degradation should not be surprising. Having said that, it is well known that simple structured NNs tend to be less prone to over-fitting the training data set, maintaining the ability to generalize and more accurately classify previously unseen data. Standard NNs are frequently found to be accurate but difficult to explain, hence they are often associated with the black box syndrome. Because in GNNs the operation is carried out at a conceptual level, the components have unambiguous meaning, revealing how classification decisions are formed. In this paper, the interpretability of GNNs is exploited using a satellite image classification problem. We examine how land use classification using both spectral and non-spectral information is expressed in GNN terms. One further contribution of this paper is the use of specific symbolization of the network components to easily establish causality relationships.
Generating Parallel Execution Plans with a Partial-order Planner

Many real-world planning problems require generating plans that maximize the parallelism inherent in a problem. There are a number of partial-order planners that generate such plans; however, in most of these planners it is unclear under what conditions the resulting plans will be correct and whether the plaltner can even find a plan if one exists. This paper identifies the underlying assumptions about when a partial plan can be executed in parallel, defines the classes of parallel plans that can be generated by different partialorder planners, and describes the changes required to turn ucPoP into a parallel execution planner. In "addition, we describe how this planner can be applied to the problem of query access planning, where parallel execution produces ubstantial reductions in overall execution time.
Multi-scale Context Intertwining for Semantic Segmentation

Accurate semantic image segmentation requires the joint consideration of local appearance, semantic information, and global scene context. In today’s age of pre-trained deep networks and their powerful convolutional features, state-of-the-art semantic segmentation approaches differ mostly in how they choose to combine together these different kinds of information. In this work, we propose a novel scheme for aggregating features from different scales, which we refer to as MultiScale Context Intertwining (MSCI). In contrast to previous approaches, which typically propagate information between scales in a one-directional manner, we merge pairs of feature maps in a bidirectional and recurrent fashion, via connections between two LSTM chains. By training the parameters of the LSTM units on the segmentation task, the above approach learns how to extract powerful and effective features for pixellevel semantic segmentation, which are then combined hierarchically. Furthermore, rather than using fixed information propagation routes, we subdivide images into super-pixels, and use the spatial relationship between them in order to perform image-adapted context aggregation. Our extensive evaluation on public benchmarks indicates that all of the aforementioned components of our approach increase the effectiveness of information propagation throughout the network, and significantly improve its eventual segmentation accuracy.