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Found 10 papers, 3 papers with code
Reliable Model Watermarking: Defending Against Theft without Compromising on Evasion
With the rise of Machine Learning as a Service (MLaaS) platforms, safeguarding the intellectual property of deep learning models is becoming paramount.
EmMixformer: Mix transformer for eye movement recognition
To this end, we propose a mixed block consisting of three modules, transformer, attention Long short-term memory (attention LSTM), and Fourier transformer.
Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedule
As a modern ensemble technique, Deep Forest (DF) employs a cascading structure to construct deep models, providing stronger representational power compared to traditional decision forests.
A Dynamic Mode Decomposition Approach for Decentralized Spectral Clustering of Graphs
Our proposed method uses the existing wave equation clustering algorithm that is based on propagating waves through the graph.
DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models
For this purpose, we create a Chinese dataset namely DuQM which contains natural questions with linguistic perturbations to evaluate the robustness of question matching models.
An Analysis of COVID-19 Knowledge Graph Construction and Applications
The construction and application of knowledge graphs have seen a rapid increase across many disciplines in recent years.
Dual MINE-based Neural Secure Communications under Gaussian Wiretap Channel
The security constraints of this method is constructed only with the input and output signal samples of the legal and eavesdropper channels and benefit that training the encoder is completely independent of the decoder.
Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training
Modern deep neural network (DNN) training jobs use complex and heterogeneous software/hardware stacks.
Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics
Natural spatiotemporal processes can be highly non-stationary in many ways, e. g. the low-level non-stationarity such as spatial correlations or temporal dependencies of local pixel values; and the high-level variations such as the accumulation, deformation or dissipation of radar echoes in precipitation forecasting.
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TBD: Benchmarking and Analyzing Deep Neural Network Training
Our primary goal in this work is to break this myopic view by (i) proposing a new benchmark for DNN training, called TBD (TBD is short for Training Benchmark for DNNs), that uses a representative set of DNN models that cover a wide range of machine learning applications: image classification, machine translation, speech recognition, object detection, adversarial networks, reinforcement learning, and (ii) by performing an extensive performance analysis of training these different applications on three major deep learning frameworks (TensorFlow, MXNet, CNTK) across different hardware configurations (single-GPU, multi-GPU, and multi-machine).
