stp2y

arXiv:2405.18458v1 Announce Type: new Abstract: Physical neural networks (PNNs) are emerging paradigms for neural network acceleration due to their high-bandwidth, in-propagation analogue processing. Despite the advantages of PNN for inference, training remains a challenge. The imperfect information of the physical transformation means the failure of conventional gradient-based updates from backpropagation (BP). Here, we present the asymmetrical training (AT) method, which treats the PNN structure as a grey box. AT performs training while only knowing the last layer output and neuron topological connectivity of a deep neural network structure, not requiring information about the physical control-transformation mapping. We experimentally demonstrated the…

[Submitted on 8 Apr 2024] View a PDF of the paper titled GHOST: Grounded Human Motion Generation with Open Vocabulary Scene-and-Text Contexts, by Zolt'an 'A. Milacski and 4 other authors View PDF Abstract:The connection between our 3D surroundings and the descriptive language that characterizes them would be well-suited for localizing and generating human motion in context but for one problem. The complexity introduced by multiple modalities makes capturing this connection challenging with a fixed set of descriptors. Specifically, closed vocabulary scene encoders, which require learning text-scene associations from scratch, have been favored in the literature, often resulting in inaccurate motion…

arXiv:2405.18448v1 Announce Type: new Abstract: This research aims to classify numerical values extracted from medical documents across seven distinct physiological categories, employing CamemBERT-bio. Previous studies suggested that transformer-based models might not perform as well as traditional NLP models in such tasks. To enhance CamemBERT-bio's performances, we introduce two main innovations: integrating keyword embeddings into the model and adopting a number-agnostic strategy by excluding all numerical data from the text. The implementation of label embedding techniques refines the attention mechanisms, while the technique of using a `numerical-blind' dataset aims to bolster context-centric learning. Another key component of our research is determining…