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Deep semantic-alignment hashing for unsupervised cross-modal retrieval
Deep hashing methods have achieved tremendous success in cross-modal retrieval, due to
its low storage consumption and fast retrieval speed. In real cross-modal retrieval …
its low storage consumption and fast retrieval speed. In real cross-modal retrieval …
Accelerated quality-diversity for robotics through massive parallelism
Quality-Diversity (QD) algorithms are a well-known approach to generate large collections of
diverse and high-quality policies. However, QD algorithms are also known to be data …
diverse and high-quality policies. However, QD algorithms are also known to be data …
Secure aggregation for federated learning in flower
Federated Learning (FL) allows parties to learn a shared prediction model by delegating the
training computation to clients and aggregating all the separately trained models on the …
training computation to clients and aggregating all the separately trained models on the …
Hostility detection in hindi leveraging pre-trained language models
Hostile content on social platforms is ever increasing. This has led to the need for proper
detection of hostile posts so that appropriate action can be taken to tackle them. Though a lot …
detection of hostile posts so that appropriate action can be taken to tackle them. Though a lot …
DeepCuts: a deep learning optimization framework for versatile GPU workloads
W Jung, TT Dao, J Lee - Proceedings of the 42nd ACM SIGPLAN …, 2021 - dl.acm.org
Widely used Deep Learning (DL) frameworks, such as TensorFlow, PyTorch, and MXNet,
heavily rely on the NVIDIA cuDNN for performance. However, using cuDNN does not always …
heavily rely on the NVIDIA cuDNN for performance. However, using cuDNN does not always …
Revisiting bag of words document representations for efficient ranking with transformers
Modern transformer-based information retrieval models achieve state-of-the-art performance
across various benchmarks. The self-attention of the transformer models is a powerful …
across various benchmarks. The self-attention of the transformer models is a powerful …
Explorable mesh deformation subspaces from unstructured 3d generative models
Exploring variations of 3D shapes is a time-consuming process in traditional 3D modeling
tools. Deep generative models of 3D shapes often feature continuous latent spaces that can …
tools. Deep generative models of 3D shapes often feature continuous latent spaces that can …
Optimizing deep learning inference via global analysis and tensor expressions
Optimizing deep neural network (DNN) execution is important but becomes increasingly
difficult as DNN complexity grows. Existing DNN compilers cannot effectively exploit …
difficult as DNN complexity grows. Existing DNN compilers cannot effectively exploit …
[HTML][HTML] Fast uncertainty quantification of spent nuclear fuel with neural networks
The accurate calculation and uncertainty quantification of the characteristics of spent nuclear
fuel (SNF) play a crucial role in ensuring the safety, efficiency, and sustainability of nuclear …
fuel (SNF) play a crucial role in ensuring the safety, efficiency, and sustainability of nuclear …
Development and assessment of a reactor system prognosis model with physics-guided machine learning
Autonomous control systems provide recommendations to help operators in decision-
making during plant operations ranging from normal operation to accident management. An …
making during plant operations ranging from normal operation to accident management. An …