Miracle-dz/UniFAR-FTUs huggingface dataset:
Looking out of the window: object localization by joint analysis of all windows in the image

Traditionally, object localization is cast as an image window classification problem, where each window is considered independently and scored based on its appearance alone. Instead, we propose a method which scores each candidate window in the context of all other windows in the image, taking into account their similarity in appearance space as well as their spatial relations in the image plane. We devise a fast and exact procedure to optimize our score function over all candidate windows in an image, and we learn its parameters using structured output regression. We demonstrate on 92000 images from ImageNet that this significantly improves localization over some of the best recent techniques that score windows in isolation.
Empirical Coordination for Joint Source-Channel Coding

We investigate the problem of simultaneous transmission and coordination for a point-to-point communication model. The encoder and decoder choose their symbols/actions (i.e. channel input and decoder output) in order to transmit information and to coordinate themselves. This paradigm has promising repercussions for future decentralized networks. Exploiting the coordination capabilities, devices can implement decentralized policies that are coordinated. Exploiting transmission capabilities, devices choose their actions (power control, the channel allocation) in order to encode additional embedded information (channel state information, future allocations). The feasible trade-offs between transmissions and coordinations are characterized using joint probability distributions over the source and channel symbols. Encoder and decoder implement a coding scheme such that the empirical frequency of symbols are close to target joint probability distribution. We characterize the set of achievable probability distribution for a point-to-point source-channel model with strictly-causal and causal decoding. Compared to classical coding results, the coordination requirement induces a stronger information constraint. We determine optimal trade-offs between transmission and coordination by maximizing a utility function over the set of achievable probability distribution.
Meta-Learning with Temporal Convolutions

Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. Recent work in meta-learning seeks to overcome this shortcoming by training a meta-learner on a distribution of similar tasks; the goal is for the meta-learner to generalize to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve. However, most recent approaches to meta-learning are extensively hand-designed, either using architectures that are specialized to a particular application, or hard-coding algorithmic components that tell the meta-learner how to solve the task. We propose a class of simple and generic meta-learner architectures, based on temporal convolutions, that is domain- agnostic and has no particular strategy or algorithm encoded into it. We validate our temporal-convolution-based meta-learner (TCML) through experiments pertaining to both supervised and reinforcement learning, and demonstrate that it outperforms state-of-the-art methods that are less general and more complex.
Fuzzy functional dependencies and lossless join decomposition of fuzzy relational database systems

This paper deals with the application of fuzzy logic in a relational database environment with the objective of capturing more meaning of the data. It is shown that with suitable interpretations for the fuzzy membership functions, a fuzzy relational data model can be used to represent ambiguities in data values as well as impreciseness in the association among them. Relational operators for fuzzy relations have been studied, and applicability of fuzzy logic in capturing integrity constraints has been investigated. By introducing a fuzzy resemblance measure EQUAL for comparing domain values, the definition of classical functional dependency has been generalized to fuzzy functional dependency (ffd). The implication problem of ffds has been examined and a set of sound and complete inference axioms has been proposed. Next, the problem of lossless join decomposition of fuzzy relations for a given set of fuzzy functional dependencies is investigated. It is proved that with a suitable restriction on EQUAL, the design theory of a classical relational database with functional dependencies can be extended to fuzzy relations satisfying fuzzy functional dependencies.
Semi-Supervised Learning with Multi-View Embedding: Theory and Application with Convolutional Neural Networks

This paper presents a theoretical analysis of multi-view embedding -- feature embedding that can be learned from unlabeled data through the task of predicting one view from another. We prove its usefulness in supervised learning under certain conditions. The result explains the effectiveness of some existing methods such as word embedding. Based on this theory, we propose a new semi-supervised learning framework that learns a multi-view embedding of small text regions with convolutional neural networks. The method derived from this framework outperforms state-of-the-art methods on sentiment classification and topic categorization.
Weakly Supervised Memory Networks

In this paper we introduce a variant of Memory Networks that needs significantly less supervision to perform question and answering tasks. The original model requires that the sentences supporting the answer be explicitly indicated during training. In contrast, our approach only requires the answer to the question during training. We apply the model to the synthetic bAbI tasks, showing that our approach is competitive with the supervised approach, particularly when trained on a sufficiently large amount of data. Furthermore, it decisively beats other weakly supervised approaches based on LSTMs. The approach is quite general and can potentially be applied to many other tasks that require capturing long-term dependencies.
How to Train Your DRAGAN

Generative Adversarial Networks have emerged as an effective technique for estimating data distributions. The basic setup consists of two deep networks playing against each other in a zero-sum game setting. However, it is not understood if the networks reach an equilibrium eventually and what dynamics makes this possible. The current GAN training procedure, which involves simultaneous gradient descent, lacks a clear game-theoretic justification in the literature. In this paper, we introduce regret minimization as a technique to reach equilibrium in games and use this to justify the success of simultaneous GD in GANs. In addition, we present a hypothesis that mode collapse, which is a common occurrence in GAN training, happens due to the existence of spurious local equilibria in non-convex games. Motivated by these insights, we develop an algorithm called DRAGAN that is fast, simple to implement and achieves competitive performance in a stable fashion across different architectures (150 random setups), datasets (MNIST, CIFAR-10, and CelebA), and divergence measures with almost no hyperparameter tuning. We show significant improvements over the recently proposed Wasserstein GAN variants.
The New Data and New Challenges in Multimedia Research

We present the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), the largest public multimedia collection that has ever been released. The dataset contains a total of 100 million media objects, of which approximately 99.2 million are photos and 0.8 million are videos, all of which carry a Creative Commons license. Each media object in the dataset is represented by several pieces of metadata, e.g. Flickr identifier, owner name, camera, title, tags, geo, media source. The collection provides a comprehensive snapshot of how photos and videos were taken, described, and shared over the years, from the inception of Flickr in 2004 until early 2014. In this article we explain the rationale behind its creation, as well as the implications the dataset has for science, research, engineering, and development. We further present several new challenges in multimedia research that can now be expanded upon with our dataset.
C3D: Generic Features for Video Analysis

Videos have become ubiquitous due to the ease of capturing and sharing via social platforms like Youtube, Facebook, Instagram, and others. The computer vision community has tried to tackle various video analysis problems independently. As a consequence, even though some really good hand-crafted features have been proposed there is a lack of generic features for video analysis. On the other hand, the image domain has progressed rapidly by using features from deep convolutional networks. These deep features are proving to be generic and perform well on variety of image tasks. In this work we propose Convolution 3D (C3D) feature, a generic spatio-temporal feature obtained by training a deep 3-dimensional convolutional network on a large annotated video dataset comprising objects, scenes, actions, and other frequently occurring concepts. We show that by using spatio-temporal convolutions the trained features encapsulate appearance and motion cues and perform well on different video classification tasks. C3D has three main advantages. First, it is generic: achieving state-of-the-art results on object recognition, scene classification, sport classification, and action similarity labeling in videos. Second, it is compact: obtaining better accuracy than best hand-crafted features and best deep image features with a lower dimensional feature descriptor. Third, it is efficient to compute: $91$ times faster than current hand-crafted features, and two orders of magnitude faster than current deep-learning based video classification methods.
Deep Learning of Grammatically-Interpretable Representations Through Question-Answering

We introduce an architecture in which internal representations— learned by end-to-end optimization in a deep neural network performing a textual question-answering task—can be interpreted using basic concepts from linguistic theory. This interpretability comes at a cost of only a few percentage-point reduction in accuracy relative to the original model on which the new one is based (BiDAF [1]). The internal representation that is interpreted is a Tensor Product Representation: for each input word, the model selects a symbol to encode the word, and a role in which to place the symbol, and binds the two together. The selection is via soft attention. The overall interpretation is built from interpretations of the symbols, as recruited by the trained model, and interpretations of the roles as used by the model. We find support for our initial hypothesis that symbols can be interpreted as lexical-semantic word meanings, while roles can be interpreted as approximations of grammatical roles (or categories) such as subject, wh-word, determiner, etc. Through extremely detailed, fine-grained analysis, we find specific correspondences between the learned roles and parts of speech as assigned by a standard parser [2], and find several discrepancies in the model’s favor. In this sense, the model learns significant aspects of grammar, after having been exposed solely to linguistically unannotated text, questions, and answers: no prior linguistic knowledge is given to the model. What is given is the means ∗This work was carried out while PS was on leave from Johns Hopkins University. LD is currently at Citadel. 1 ar X iv :1 70 5. 08 43 2v 1 [ cs . C L ] 2 3 M ay 2 01 7 to represent using symbols and roles and an inductive bias favoring use of these in an approximately discrete manner.
Synthetic Spoken Data for Neural Machine Translation

In this paper, we introduce a novel approach to generate synthetic data for training Neural Machine Translation systems. The proposed approach transforms a given parallel corpus between a written language and a target language to a parallel corpus between a spoken dialect variant and the target language. Our approach is language independent and can be used to generate data for any variant of the source language such as slang or spoken dialect or even for a different language that is closely related to the source language. The proposed approach is based on local embedding projection of distributed representations which utilizes monolingual embeddings to transform parallel data across language variants. We report experimental results on Levantine to English translation using Neural Machine Translation. We show that the generated data can improve a very large scale system by more than 2.8 Bleu points using synthetic spoken data which shows that it can be used to provide a reliable translation system for a spoken dialect that does not have sufficient parallel data.
A Neuro-fuzzy Approach for Predicting Load Peak Profile

Load forecasting has many applications for power systems, including energy purchasing and generation, load switching, contract evaluation, and infrastructure development. Load forecasting is a complex mathematical process characterized by random data and a multitude of input variables.To solve load forecasting, two different approaches are used, the traditional and the intelligent one.Intelligent systems have proved their efficiency in load forecasting domain. Adaptive neuro-fuzzy inference systems (ANFIS) are a combination of two intelligent techniques where we can get neural networks and fuzzy logics advantages simultaneously. In this paper, we will forecast night load peak of Algerian power system using multivariate input adaptive neuro-fuzzy inference system (ANFIS) introducing the effect of the temperature and type of the day as input variables.
Fast, simple and accurate handwritten digit classification using extreme learning machines with shaped input-weights

Deep networks have inspired a renaissance in neural network use, and are becoming the default option for difficult tasks on large datasets. In this report we show that published deep network results on the MNIST handwritten digit dataset can straightforwardly be replicated (error rates below 1%, without use of any distortions) with shallow 'Extreme Learning Machine' (ELM) networks, with a very rapid training time (~10 minutes). When we used distortions of the training set we obtained error rates below 0.6%. To achieve this performance, we introduce several methods for enhancing ELM implementation, which individually and in combination can significantly improve performance, to the point where it is nearly indistinguishable from deep network performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random 'receptive field' sampling of the input ensures the input weight matrix is sparse, with about 90 percent of weights equal to zero, which is a potential advantage for hardware implementations. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST suggest that the ease of use and accuracy of ELM should cause it to be given greater consideration as an alternative to deep networks applied to more challenging datasets.
PatternNet and PatternLRP - Improving the interpretability of neural networks

Deep learning has significantly advanced the state of the art in machine learning. However, neural networks are often considered black boxes. There is significant effort to develop techniques that explain a classifier's decisions. Although some of these approaches have resulted in compelling visualisations, there is a lack of theory of what is actually explained. Here we present an analysis of these methods and formulate a quality criterion for explanation methods. On this ground, we propose an improved method that may serve as an extension for existing back-projection and decomposition techniques.
Towards Real World Human Parsing: Multiple-Human Parsing in the Wild

The recent progress of human parsing techniques has been largely driven by the availability of rich data resources. In this work, we demonstrate some critical discrepancies between the current benchmark datasets and the real world human parsing scenarios. For instance, all the human parsing datasets only contain one person per image, while usually multiple persons appear simultaneously in a realistic scene. It is more practically demanded to simultaneously parse multiple persons, which presents a greater challenge to modern human parsing methods. Unfortunately, absence of relevant data resources severely impedes the development of multiple-human parsing methods. To facilitate future human parsing research, we introduce the Multiple-Human Parsing (MHP) dataset, which contains multiple persons in a real world scene per single image. The MHP dataset contains various numbers of persons (from 2 to 16) per image with 18 semantic classes for each parsing annotation. Persons appearing in the MHP images present sufficient variations in pose, occlusion and interaction. To tackle the multiple-human parsing problem, we also propose a novel Multiple-Human Parser (MH-Parser), which considers both the global context and local cues for each person in the parsing process. The model is demonstrated to outperform the naive "detect-and-parse" approach by a large margin, which will serve as a solid baseline and help drive the future research in real world human parsing.
On the Lifetime Distribution of Consecutive -out-of-n: F System

A consecutive -out-of: F system is a system of components which fails iff consecutive components fail. In this paper, we consider the lifetime of consecutive -out-of: F systems and provide some stochastic ordering results. It is shown that the usual stochastic ordering is preserved under the formation of consecutive -out-of: F systems of -independent, nonidentical, and exchangeable components. A counter example is also given to show that the mean residual life ordering is not, in general, preserved by a consecutive -out-of: F system.
Write in Style: A guide to good English

Part One: Engage Brain and Ear Before Writing 1. Disasters 2. Triumphs Part Two: Foundations 3. Introduction 4. Bone Structure 5. Joints Part Three: Style 6. Introduction: Style Versus Fashion 7. Fight the Flab 8. Voice Interlude: Writing and Computers Part Four: Tailor-Made 9. Introduction 10. Letters 11. Essays 12. Articles 13. Reviews 14. Reports 15. Minutes 16. Precis and Summary 17. Reportage Part Five: Grammar Primer 18. An Outline Menu 19. Inflections 20. Syntax 21. Parts of Speech (Advanced) 22. Punctuation in Speech and Quotation 23. Spelling and Confusibles
Shaping Pulses to Control Multi-Stable Biological Systems

In this paper, we present a framework for shaping pulses to control biological systems. By shaping a pulse we mean computing its magnitude and length, which results in reaching the desired control objective. An advantage of using pulses for biological control problems is that pulses can easily be implemented in a laboratory setting by scientists. Firstly, we consider the problem of switching between steady states in a multi-stable system. We show that it is possible to estimate the pulses fulfilling the objective, if the trajectories of the controlled system can be bounded from above and below by the trajectories of monotone systems. This result can be generalised to systems with uncertain parameter values under some mild assumptions on the set of admissible parameters. Hence, even with infinite sampling time (that is open-loop control) some robustness guarantees can be provided. Secondly, we consider a problem of inducing oscillatory behaviour in an eight species generalised repressilator, which is a monotone system. In this case, the problem reduces to keeping the trajectories in a neighbourhood of an unstable equilibrium. The problem is solved by creating an event-based control strategy, where an event is triggered by the trajectories leaving the prescribed neighbourhood. After the event is triggered a pulse is applied, which steers the trajectories towards the unstable equilibrium. Potentially, this approach can also be applicable to systems, which are bounded from above and below by monotone ones.
Can Partial Strong Labels Boost Multi-label Object Recognition?

Convolutional neural networks (CNN) have shown great performance as a global representation for object recognition. However, for multi-label images that contain multiple objects from different categories, scales and locations, single CNN features might not be be optimal. To enhance the robustness and discriminative power of CNN features for multi-label object recognition problem, we propose a multi-view multi-instance framework. This framework transforms the multi-label classification problem into a multi-class multi-instance learning problem by extracting object proposals from images. A multi-view pipeline is then applied to generate a two-view representation of each proposal by exploiting two levels of labels in multi-label recognition problem. The proposed framework not only has the flexibility of utilizing both weak and strong labels or just weak labels, but also holds the generalization ability to boost the performance of unseen categories by available strong labels. Our framework is extensively compared with state-of-the-art hand-crafted feature based and CNN based methods on two multi-label benchmark datasets. The experimental results validate the discriminative power and generalization ability of the proposed framework. When combined with a very-deep network, we can achieve state-of-the-art results in both datasets.
Learning local shape descriptors with view-based convolutional networks

Figure 1: We present a view-based convolutional network that produces local, point-based shape descriptors. The network is trained such that geometrically and semantically similar points across different 3D shapes are embedded close to each other in descriptor space (left). Our produced descriptors are quite generic — they can be used in a variety of shape analysis applications, including dense matching, prediction of human affordance regions, partial scan-to-shape matching, and shape segmentation (right).
HR-CTC: A Large Human Resource Corpus for Text Classification

With the rapid development of online recruitment, a large amount of job postings enables a new paradigm for studying the national economic state. However, there are two problems when using these data for national economic research. First, there is a mismatch between the job descriptions and the job titles. Second, these titles differ from the category names in the national economic research standards. To map job postings with similar job descriptions but different titles to the same category of the national economic research standards, this paper introduces a text classification corpus named HR-CTC. A novel method to reduce the influence of human subjectivity is proposed to construct the corpus. The experimental results show that the proposed method outperforms manual categorization in accuracy evaluation criteria by a 47.08% increase. To verify the value of this corpus for text classification research and to provide a baseline for further research, we implement five methods of deep learning for text classification and achieve promising results.