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Found 11 papers, 2 papers with code
Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation
To fairly evaluate the performance, we also propose three specific evaluation metrics.
3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection
3D visual grounding aims to locate the referred target object in 3D point cloud scenes according to a free-form language description.
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare
Deep neural networks (DNNs) have been broadly adopted in health risk prediction to provide healthcare diagnoses and treatments.
FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Update
Federated learning aims to protect users' privacy while performing data analysis from different participants.
TransRefer3D: Entity-and-Relation Aware Transformer for Fine-Grained 3D Visual Grounding
Existing works usually adopt dynamic graph networks to indirectly model the intra/inter-modal interactions, making the model difficult to distinguish the referred object from distractors due to the monolithic representations of visual and linguistic contents.
FedSiam: Towards Adaptive Federated Semi-Supervised Learning
In this paper, we focus on designing a general framework FedSiam to tackle different scenarios of federated semi-supervised learning, including four settings in the labels-at-client scenario and two setting in the labels-at-server scenario.
A Benchmark Dataset for Understandable Medical Language Translation
In this paper, we introduce MedLane -- a new human-annotated Medical Language translation dataset, to align professional medical sentences with layperson-understandable expressions.
SparseTrain: Exploiting Dataflow Sparsity for Efficient Convolutional Neural Networks Training
In this paper, \textit{SparseTrain} is proposed to accelerate CNN training by fully exploiting the sparsity.
Accelerating CNN Training by Pruning Activation Gradients
Sparsification is an efficient approach to accelerate CNN inference, but it is challenging to take advantage of sparsity in training procedure because the involved gradients are dynamically changed.
Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets
The inverse mapping of GANs'(Generative Adversarial Nets) generator has a great potential value. Hence, some works have been developed to construct the inverse function of generator by directly learning or adversarial learning. While the results are encouraging, the problem is highly challenging and the existing ways of training inverse models of GANs have many disadvantages, such as hard to train or poor performance. Due to these reasons, we propose a new approach based on using inverse generator ($IG$) model as encoder and pre-trained generator ($G$) as decoder of an AutoEncoder network to train the $IG$ model.
