Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy

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Deep learning has achieved remarkable success in various challenging tasks such as generating images from natural language or engaging in lengthy conversations with humans. The success in practice stems from the ability to successfully train massive neural ...

The impressive pace of advance of quantum technology calls for robust and scalable techniques for the characterization and validation of quantum hardware. Quantum process tomography, the reconstruction of an unknown quantum channel from measurement data, r ...

Object-centric learning has gained significant attention over the last years as it can serve as a powerful tool to analyze complex scenes as a composition of simpler entities. Well-established tasks in computer vision, such as object detection or instance ...

In the last decade, deep neural networks have achieved tremendous success in many fields of machine learning. However, they are shown vulnerable against adversarial attacks: well-designed, yet imperceptible, perturbations can make the state-of-the-art deep ...

Training convolutional neural networks (CNNs) for very high-resolution images requires a large quantity of high-quality pixel-level annotations, which is extremely labor-intensive and time-consuming to produce. Moreover, professional photograph interpreter ...

We propose a pre-training strategy called Multi-modal Multi-task Masked Autoencoders (MultiMAE). It differs from standard Masked Autoencoding in two key aspects: I) it can optionally accept additional modalities of information in the input besides the RGB ...

We characterize the solution of a broad class of convex optimization problems that address the reconstruction of a function from a finite number of linear measurements. The underlying hypothesis is that the solution is decomposable as a finite sum of compo ...

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A major challenge in the common approach of hot water generation in residential houses lies in the highly stochastic nature of domestic hot water (DHW) demand. Learning hot water use behavior enables water heating systems to continuously adapt to the stoch ...

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Over the past few years, there have been fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. The amount of annotated data drastically increased and supervised deep discriminative models exceed ...

Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy

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A Theory of Finite-Width Neural Networks: Generalization, Scaling Laws, and the Loss Landscape

Quantum process tomography with unsupervised learning and tensor networks

Unsupervised Visual Entity Abstraction towards 2D and 3D Compositional Models

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks

Semantic Segmentation of Remote Sensing Images With Sparse Annotations

MultiMAE: Multi-modal Multi-task Masked Autoencoders

Convex optimization in sums of Banach spaces

The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms

Adaptive hot water production based on Supervised Learning

The value of human data annotation for machine learning based anomaly detection in environmental systems

Deep Generative Models and Applications