PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers

Multiple instance learning (MIL) was a weakly supervised learning approach that sought to assign binary class labels to collections of instances known as bags. However, due to their weak supervision nature, the MIL methods were susceptible to overfitting and required assistance in developing comprehensive representations of target instances. While regularization typically effectively combated overfitting, its integration with the MIL model has been frequently overlooked in prior studies. Meanwhile, current regularization methods for MIL have shown limitations in their capacity to uncover a diverse array of representations. In this study, we delve into the realm of regularization within the MIL model, presenting a novel approach in the form of a Progressive Dropout Layer (PDL). We aim to not only address overfitting but also empower the MIL model in uncovering intricate and impactful feature representations. The proposed method was orthogonal to existing MIL methods and could be easily integrated into them to boost performance. Our extensive evaluation across a range of MIL benchmark datasets demonstrated that the incorporation of the PDL into multiple MIL methods not only elevated their classification performance but also augmented their potential for weakly-supervised feature localizations.Comment: The code is available in https://github.com/ChongQingNoSubway/PD

Embedding sustainability in risk management: The impact of environmental, social, and governance ratings on corporate financial risk

This study investigates the effect of corporate social and environmental evaluation on investors’ risk perception to explore the potential market risk for public companies that adopt a sustainable and responsible corporate strategy. We referred to the triple corporate assessment according to environmental, social, and governance (ESG) criteria to check whether ESG factors—meant to direct firms toward social and environmental needs—improve corporate market performance or trigger, among investors, a perception of “window dressing.” In doing so, we tested the impact of corporate social performance—proxied by an ESG assessment—on corporate financial risk using double risk measurement. We conducted a five-year longitudinal study (fiscal years 2014–2018) of 222 companies listed on the Standard & Poor’s index. The empirical findings show higher investor uncertainty regarding corporate sustainability performance, probably due to the misalignment of objectives between investors and investees. Indeed, an overall ESG assessment corresponds to higher systematic risk for firms, and a corporate environmental rating has an upward effect on the same risk dimension

A GRASP-Tabu Heuristic Approach to Territory Design for Pickup and Delivery Operations for Large-Scale Instances

Weaddressalogisticsdistrictingproblemfacedbyaparcelcompanywhoseoperationsconsistofpickingupanddeliveringpackages overaserviceregion.Thedistrictingprocessaimstofindapartitionoftheserviceregionintodeliveryandcollectionzonesthat may be served by a single vehicle that departs from a central depot. Criteria to be optimized are to balance workload content among the districts and to create districts of compact shape. A solution approach based on a hybrid procedure that combines elements of GRASP and Tabu Search (TS) is proposed to solve large-scale instances. Numerical experimentation is performed consideringdifferentinstancesizesandtypes.Resultsshowthattheproposedsolutionapproachisabletosolvelarge-scaleinstances inreasonablecomputationaltimeswithgoodqualityofthesolutionsobtained.Todeterminethequalityofthesolutions,resultsare comparedwithCPLEXsolutionsandwiththecurrentrealsolutiontohighlightthebenefitsoftheproposedapproach.Conclusions andrecommendationsforfurtherresearchareprovided

SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR MITIGATING GENERATIVE MACHINE LEARNING MODEL HALLUCINATION

Systems, methods, and computer program products are provided for mitigating generative model such as a large language model (LLM) hallucination. The system includes a processor configured to receive a query and input the query to a first generative model. The processor is also configured to determine candidate responses based on an output of the first generative model, generate embeddings based on the query, and retrieve data from an embedding-indexed data store based on the embeddings. The processor is further configured to input the data to a second generative model, generate a summary based on an output of the second generative model, and input the data and the candidate responses to a third generative model. The processor is further configured to produce filtered responses based on an output of the third generative model, generate a response based on the summary and the filtered responses, and transmit the response

Jointly Learning Word Embeddings and Latent Topics

Word embedding models such as Skip-gram learn a vector-space representation for each word, based on the local word collocation patterns that are observed in a text corpus. Latent topic models, on the other hand, take a more global view, looking at the word distributions across the corpus to assign a topic to each word occurrence. These two paradigms are complementary in how they represent the meaning of word occurrences. While some previous works have already looked at using word embeddings for improving the quality of latent topics, and conversely, at using latent topics for improving word embeddings, such "two-step'' methods cannot capture the mutual interaction between the two paradigms. In this paper, we propose STE, a framework which can learn word embeddings and latent topics in a unified manner. STE naturally obtains topic-specific word embeddings, and thus addresses the issue of polysemy. At the same time, it also learns the term distributions of the topics, and the topic distributions of the documents. Our experimental results demonstrate that the STE model can indeed generate useful topic-specific word embeddings and coherent latent topics in an effective and efficient way

Domain-adaptive Message Passing Graph Neural Network

Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels, draws increasing attention recently. To address CNNC, we propose a domain-adaptive message passing graph neural network (DM-GNN), which integrates graph neural network (GNN) with conditional adversarial domain adaptation. DM-GNN is capable of learning informative representations for node classification that are also transferrable across networks. Firstly, a GNN encoder is constructed by dual feature extractors to separate ego-embedding learning from neighbor-embedding learning so as to jointly capture commonality and discrimination between connected nodes. Secondly, a label propagation node classifier is proposed to refine each node's label prediction by combining its own prediction and its neighbors' prediction. In addition, a label-aware propagation scheme is devised for the labeled source network to promote intra-class propagation while avoiding inter-class propagation, thus yielding label-discriminative source embeddings. Thirdly, conditional adversarial domain adaptation is performed to take the neighborhood-refined class-label information into account during adversarial domain adaptation, so that the class-conditional distributions across networks can be better matched. Comparisons with eleven state-of-the-art methods demonstrate the effectiveness of the proposed DM-GNN