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Greatest papers with code

Infinite attention: NNGP and NTK for deep attention networks

ICML 2020 google/neural-tangents

There is a growing amount of literature on the relationship between wide neural networks (NNs) and Gaussian processes (GPs), identifying an equivalence between the two for a variety of NN architectures.

DEEP ATTENTION GAUSSIAN PROCESSES

Multi-scale self-guided attention for medical image segmentation

arXiv preprint 2019 sinAshish/Multi-Scale-Attention

In this paper we attempt to overcome these limitations with the proposed architecture, by capturing richer contextual dependencies based on the use of guided self-attention mechanisms.

ATTENTIVE SEGMENTATION NETWORKS BRAIN TUMOR SEGMENTATION DEEP ATTENTION

PREDATOR: Registration of 3D Point Clouds with Low Overlap

25 Nov 2020ShengyuH/OverlapPredator

We introduce PREDATOR, a model for pairwise point-cloud registration with deep attention to the overlap region.

DEEP ATTENTION POINT CLOUD REGISTRATION

Deep Attention Recurrent Q-Network

5 Dec 20155vision/DARQN

A deep learning approach to reinforcement learning led to a general learner able to train on visual input to play a variety of arcade games at the human and superhuman levels.

ATARI GAMES DEEP ATTENTION

Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks

23 Sep 2020utayao/Atten_Deep_MIL

We evaluated our methods on two large cancer whole slide images datasets and our results suggest that the proposed approach is more effective and suitable for large datasets and has better interpretability in locating important patterns and features that contribute to accurate cancer survival predictions.

DEEP ATTENTION MULTIPLE INSTANCE LEARNING WHOLE SLIDE IMAGES

RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans

4 Nov 2018RanSuLab/RAUNet-tumor-segmentation

Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes.

BRAIN TUMOR SEGMENTATION DEEP ATTENTION TUMOR SEGMENTATION

Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing

23 Dec 2020salesforce/TabularSemanticParsing

We present BRIDGE, a powerful sequential architecture for modeling dependencies between natural language questions and relational databases in cross-DB semantic parsing.

DEEP ATTENTION SEMANTIC PARSING TEXT-TO-SQL

Processing Megapixel Images with Deep Attention-Sampling Models

3 May 2019idiap/attention-sampling

We show that sampling from the attention distribution results in an unbiased estimator of the full model with minimal variance, and we derive an unbiased estimator of the gradient that we use to train our model end-to-end with a normal SGD procedure.

DEEP ATTENTION