Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research

Building the Big Society

Papers are a contribution to the debate and set out the authors ’ views only Localism and the Big Societ

(Machine) Learning to Do More with Less

Determining the best method for training a machine learning algorithm is critical to maximizing its ability to classify data. In this paper, we compare the standard "fully supervised" approach (that relies on knowledge of event-by-event truth-level labels) with a recent proposal that instead utilizes class ratios as the only discriminating information provided during training. This so-called "weakly supervised" technique has access to less information than the fully supervised method and yet is still able to yield impressive discriminating power. In addition, weak supervision seems particularly well suited to particle physics since quantum mechanics is incompatible with the notion of mapping an individual event onto any single Feynman diagram. We examine the technique in detail -- both analytically and numerically -- with a focus on the robustness to issues of mischaracterizing the training samples. Weakly supervised networks turn out to be remarkably insensitive to systematic mismodeling. Furthermore, we demonstrate that the event level outputs for weakly versus fully supervised networks are probing different kinematics, even though the numerical quality metrics are essentially identical. This implies that it should be possible to improve the overall classification ability by combining the output from the two types of networks. For concreteness, we apply this technology to a signature of beyond the Standard Model physics to demonstrate that all these impressive features continue to hold in a scenario of relevance to the LHC.Comment: 32 pages, 12 figures. Example code is provided at https://github.com/bostdiek/PublicWeaklySupervised . v3: Version published in JHEP, discussion adde

Undocumented Migrants in Resistance against Detention: Comparative Observations on Germany and France

Although the immigration policies of Germany and France share a similarly restrictive approach, the manner in which migrants protest against such policies and resist against their implementation is strikingly different. This is particularly obvious for undocumented migrants. In France, collective action of undocumented migrants has received increasing public attention over the last two decades, and detention centres have been a foremost target of such action. Resistance against detention prior to deportation culminated in achieving the closure of the country's biggest detention centre in 2008. To the contrary, undocumented migrants have hardly ever protested against their condition in Germany. Although collective action against immigration policies has reached a new level with the “Refugee Tent Action” occupying public space in Berlin and elsewhere since 2012, it continues to focus mainly on the living conditions of asylum seekers, not undocumented migrants. This discrepancy may be explained with the existence of different institutional conditions for collective action, i.e. such political opportunity structures that refer to state regulations and measures. A comparative analysis of these conditions shows that weaker resistance against immigration detention in Germany may be due to the existence of comparably more repressive and controlling immigration laws, a flexible toleration status that provides its holders with basic social security, and the scarcity of options for legalisation. The combination of harsh repression and little prospect for legalisation makes resistance appear much riskier. The risks are greater yet for holders of a toleration status since its delivery is, to some extent, subject to administrative discretion. The toleration status thus tends to divide the people susceptible to engage in collective action. The knowledge of these differences may help undocumented migrants and their supporters in both countries to develop more effective strategies of resistance against restrictive policies

A Dataset for Movie Description

Descriptive video service (DVS) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed DVS, which is temporally aligned to full length HD movies. In addition we also collected the aligned movie scripts which have been used in prior work and compare the two different sources of descriptions. In total the Movie Description dataset contains a parallel corpus of over 54,000 sentences and video snippets from 72 HD movies. We characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing DVS to scripts, we find that DVS is far more visual and describes precisely what is shown rather than what should happen according to the scripts created prior to movie production

Rethinking the International Monetary System: an overview

Monetary policy ; International finance