Deep Learning Approaches for Auditory Perception in Robotics

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Planning multicontact motions in a receding horizon fashion requires a value function to guide the planning with respect to the future, e.g., building momentum to traverse large obstacles. Traditionally, the value function is approximated by computing traj ...

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

Training data mixtures greatly impact the generalization performance of large language models. Existing domain reweighting methods often rely on costly weight computations and require retraining when new data is introduced. To this end, we introduce a flex ...

Biohybrid systems in which robotic lures interact with animals have become compelling tools for probing and identifying the mechanisms underlying collective animal behavior. One key challenge lies in the transfer of social interaction models from simulatio ...

Animals exhibit remarkable locomotion skills despite significant sensorimotor delays and operating in uncertain environments. Moreover, mammals acquire these skills within minutes of birth. From the Cambrian explosion to the present day, vertebrate motor c ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

Web search engines process billions of queries daily, making the balance between computational efficiency and ranking quality crucial. While neural ranking models have shown impressive performance, their computational costs, particularly in feature extract ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

Understanding the structure of real data is paramount in advancing modern deep-learning methodologies. Natural data such as images are believed to be composed of features organized in a hierarchical and combinatorial manner, which neural networks capture d ...

Social insects, such as ants, termites, and honeybees, have evolved sophisticated societies where collaboration and division of labor enhance survival of the whole colony, and are thus considered “superorganisms”. Historically, studying behaviors involving ...

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

What changes with age? Results in reinforcement learning (RL) are mixed. Some studies found deteriorated RL performance in older participants compared to younger controls whereas other studies did not. Daniel et al. (J Neurosci 40(5):1084–1096, 2020. 10.15 ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

Dataset corresponding to the following manuscript: Slagter, A., Setyadji, J.A., Vogt, E.L. et al. Nanoindentation Hardness and Modulus of Al2O3–SiO2–CaO and MnO–SiO2–FeO Inclusions in Iron. Metall Mater Trans A (2024). https://doi.org/10.1007/s11661-024-0 ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Deep Learning Approaches for Auditory Perception in Robotics

Chattez avec Graph Search

Online Multicontact Receding Horizon Planning via Value Function Approximation

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Random matrix methods for high-dimensional machine learning models

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Cost-Efficiency Trade-offs for Neural Cascade Rankers in Web Search

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A phase transition in diffusion models reveals the hierarchical nature of data

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Fair Decentralized Learning

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Generalization of Scaled Deep ResNets in the Mean-Field Regime

Nanoindentation hardness and modulus of Al2O3-SiO2-CaO and MnO-SiO2-FeO inclusions in iron

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Aggregating Spatial and Photometric Context for Photometric Stereo