This paper presents a new approach to robotic manipulation, focusing on bin-picking. It leverages category-agnostic instance segmentation and simulation-based training for real-world application. The method can handle objects regardless of…
Continue readingThe article introduces DSNet, a Disentangled Siamese Network with neutral calibration for speech emotion recognition (SER). DSNet aims to address the challenges in SER, such as the unconscious encoding of…
Continue readingThe paper proposes a method to enhance the performance of a personalized voice activity detection (VAD) model in adverse conditions using self-supervised pretraining on a large unlabelled dataset. The model…
Continue readingThe article introduces a new loss function for speaker recognition with deep neural networks, based on Jeffreys Divergence. This divergence, added to the cross-entropy loss function, allows for maximizing the…
Continue readingThis research paper investigates the application of deep learning-based clinical coding approaches in outpatient settings. It addresses the lack of research in this area despite its distinct challenges and potential…
Continue readingThe article discusses peer-to-peer deep learning algorithms that allow distributed devices to collaboratively train deep neural networks without exchanging raw data or relying on a central server. The authors identify…
Continue readingThis academic article delves into Neural finite-state transducers (NFSTs), an expressive family of neurosymbolic sequence transduction models. The authors discuss the challenges of imputing the latent alignment path that explains…
Continue readingThe paper presents a novel algorithm, Federated Learning with Accumulated Regularized Embeddings (FLARE), which addresses challenges in decentralized machine learning, specifically in federated learning (FL). The algorithm uses sparse training…
Continue readingThis academic article introduces PK-NCLI, a novel method aimed at improving the quality of responses generated by conversational agents leveraging AI and deep learning. The method achieves this by accurately…
Continue readingThe authors of the paper tackled the problems of image classifiers by generating images that optimize a classifier-derived objective. They analyzed the behavior and decisions of image classifiers by visual…
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