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Found 6 papers, 2 papers with code
A Brief Review of Hypernetworks in Deep Learning
They offer a new way to design and train neural networks, and they have the potential to improve the performance of deep learning models on a variety of tasks.
Attenuating Random Noise in Seismic Data by a Deep Learning Approach
In the geophysical field, seismic noise attenuation has been considered as a critical and long-standing problem, especially for the pre-stack data processing.
Generative Inpainting Network Applications on Seismic Image Compression and Non-Uniform Sampling
The use of deep learning models as priors for compressive sensing tasks presents new potential for inexpensive seismic data acquisition.
Enhanced Seismic Imaging with Predictive Neural Networks for Geophysics
In addition, in each iteration step, the probability cubes of salt bodies and inclusions inferred from the proposed networks can be used as a regularization term within the FWI forward modelling, which may result in an improved velocity model estimation while the output of seismic migration can be utilized as an input of the 3D neural network for subsequent iterations.
Vector and Line Quantization for Billion-scale Similarity Search on GPUs
The inverted index structure is amenable to GPU-based implementations, and the state-of-the-art systems such as Faiss are able to exploit the massive parallelism offered by GPUs.
Kill Two Birds With One Stone: Boosting Both Object Detection Accuracy and Speed With adaptive Patch-of-Interest Composition
Object detection is an important yet challenging task in video understanding & analysis, where one major challenge lies in the proper balance between two contradictive factors: detection accuracy and detection speed.
