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PublicationThe Procurement Initiative Pulse Check Q2/2024(University of St.Gallen, 2024-07-15)With our Q2/2024 Pulse Check, we aim to gauge the current sentiment within the procurement and supply chain environment, focusing specifically on the role of raw materials procurement and the management of supplier price increases. Based on our pulse check survey, this report provides an overview of the current state in procurement and future trajectories.
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PublicationWorking beyond Neoliberalism: Organizing Alternativity through an Ethics of Care( 2024-07-05)The urgency to reimagine neoliberal capitalism challenges scholars to explore how we can organize alternatively. In this paper, we argue that a feminist ethics of care, which stresses our fundamental interdependence and responsibility to particular others, provides a novel relational understanding of the organization and maintenance of alternativity. Relying on an ethnography of a campaign and consultancy cooperative, we show how an ethical adherence to flourishing, vulnerability, and solidarity informs an alternative coordination, valuation, and orientation of work. We theorize how the cooperative maintains this alternativity through the organization of a ‘deliberative-responsive space’. Departing from scholarship that focuses on principles and practices in alternative organizing, our study emphasizes the organization of alternativity as an ongoing ethical responsiveness to different needs, capacities, and perspectives. We underline that a relational ethics and the responsive organizing it calls for provides a more situative and dynamic approach to dealing with plurality and conflict that frequently degenerate alternative organizations.
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PublicationTowards Scalable and Versatile Weight Space Learning(PMLR 235, 2024, 2024-07-23)Learning representations of well-trained neural network models holds the promise to provide an understanding of the inner workings of those models. However, previous work has either faced limitations when processing larger networks or was task-specific to either discriminative or generative tasks. This paper introduces the SANE approach to weight-space learning. SANE overcomes previous limitations by learning task-agnostic representations of neural networks that are scalable to larger models of varying architectures and that show capabilities beyond a single task. Our method extends the idea of hyper-representations towards sequential processing of subsets of neural network weights, thus allowing one to embed larger neural networks as a set of tokens into the learned representation space. SANE reveals global model information from layer-wise embeddings, and it can sequentially generate unseen neural network models, which was unattainable with previous hyper-representation learning methods. Extensive empirical evaluation demonstrates that SANE matches or exceeds state-of-the art performance on several weight representation learning benchmarks, particularly in initialization for new tasks and larger ResNet architectures.Type:Volume:
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PublicationCollaborative Advantage: Innovate, scale and transform to thrive in a volatile world(Palgrave Macmillan, 2024)
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