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Found 15 papers, 1 papers with code
Medical Speech Symptoms Classification via Disentangled Representation
After combining the intent from two domains into a joint representation, the integrated intent representation is fed into a decision layer for classification.
Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection
State-of-the-art 3D object detectors are usually trained on large-scale datasets with high-quality 3D annotations.
Improved Speech Pre-Training with Supervision-Enhanced Acoustic Unit
Speech pre-training has shown great success in learning useful and general latent representations from large-scale unlabeled data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improved Self-Supervised Multilingual Speech Representation Learning Combined with Auxiliary Language Information
Multilingual end-to-end models have shown great improvement over monolingual systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Exploring General Intelligence of Program Analysis for Multiple Tasks
To this end, we explore a generic program embedding approach that aim at solving multiple program analysis tasks.
Squeezing SGD Parallelization Performance in Distributed Training Using Delayed Averaging
Mini-batch Stochastic Gradient Descent (SGD) requires workers to halt forward/backward propagations, to wait for gradients synchronized among all workers before the next batch of tasks.
3D Dental model segmentation with graph attentional convolution network
We have evaluated our network on a real-patient dataset of dental models acquired through 3D intraoral scanners, and experimental results show that our method outperforms state-of-the-art deep learning methods for 3D shape segmentation.
Predicting Daily Trading Volume via Various Hidden States
Predicting intraday trading volume plays an important role in trading alpha research.
GRAPHSPY: Fused Program Semantic-Level Embedding via Graph Neural Networks for Dead Store Detection
To this end, we propose a learning-aided approach to identify unnecessary memory operations intelligently with low overhead.
Phoebe: Reuse-Aware Online Caching with Reinforcement Learning for Emerging Storage Models
With data durability, high access speed, low power efficiency and byte addressability, NVMe and SSD, which are acknowledged representatives of emerging storage technologies, have been applied broadly in many areas.
Learning Forward Reuse Distance
Inspired by the high prediction accuracy, we propose a pseudo OPT policy and evaluate it upon 13 real-world storage workloads from Microsoft Research.
DaSGD: Squeezing SGD Parallelization Performance in Distributed Training Using Delayed Averaging
The state-of-the-art deep learning algorithms rely on distributed training systems to tackle the increasing sizes of models and training data sets.
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss
In this paper, we improve the robustness of DNNs by utilizing techniques of Distance Metric Learning.
Query-Efficient Black-Box Attack by Active Learning
To conduct black-box attack, a popular approach aims to train a substitute model based on the information queried from the target DNN.
The NNI Query-by-Example System for MediaEval 2015
This paper describes the system developed by the NNI team for the Query-by-Example Search on Speech Task (QUESST) in the MediaEval 2015 evaluation.
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