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Found 17 papers, 6 papers with code
RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering
The current paradigm, Retrieval-Augmented Generation (RAG), acquires expertise medical knowledge through large-scale corpus retrieval and uses this knowledge to guide a general-purpose large language model (LLM) for generating answers.
EM-DARTS: Hierarchical Differentiable Architecture Search for Eye Movement Recognition
First, we define a supernet and propose a global and local alternate Neural Architecture Search method to search the optimal architecture alternately with a differentiable neural architecture search.
Efficient and Effective Model Extraction
Model extraction aims to create a functionally similar copy from a machine learning as a service (MLaaS) API with minimal overhead, typically for illicit profit or as a precursor to further attacks, posing a significant threat to the MLaaS ecosystem.
Relax DARTS: Relaxing the Constraints of Differentiable Architecture Search for Eye Movement Recognition
Eye movement biometrics is a secure and innovative identification method.
A Survey on Mixup Augmentations and Beyond
As Deep Neural Networks have achieved thrilling breakthroughs in the past decade, data augmentations have garnered increasing attention as regularization techniques when massive labeled data are unavailable.
SUMix: Mixup with Semantic and Uncertain Information
Mixup data augmentation approaches have been applied for various tasks of deep learning to improve the generalization ability of deep neural networks.
StarLKNet: Star Mixup with Large Kernel Networks for Palm Vein Identification
To enable CNNs to capture comprehensive feature representations from palm-vein images, we explored the effect of convolutional kernel size on the performance of palm-vein identification networks and designed LaKNet, a network leveraging large kernel convolution and gating mechanism.
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).
