Graph Neural Networks (GNNs) have proved to be an effective representation learning framework for graph-structured data, and have achieved state-of-the-art performance on many practical predictive tasks. Among the variants of GNNs, Graph Attention Networks (GATs) improve the performance of many grap...
attention_cpu_base.h gqa_attention_base.h group_query_attention.cc group_query_attention_helper.h rotary_helper.h cpu_contrib_kernels.cc sparse sparse_attention.cc sparse_attention.h sparse_attention_base.h sparse_attention_helper.h cuda/sparse sparse_attention.cc core/graph/con...
SparseTIR is a tensor-level compiler for sparse/irregular operators in Deep Learning. The design goal of SparseTIR is to provide a general programming abstraction that can cover both sparse and irregular (e.g. Ragged Tensors) workloads in Deep Learning including Graph Neural Networks, Sparse ...
First, the Sparse Directed Interac- tion (SDI) and Motion Tendency (MT) are learned from the spatial and temporal graph inputs using self-attention mech- anism and asymmetric convolution networks, respectively. Then, subsequent sparse spatial and temporal Graph Convo- ...
We release our code with two new efficient modules used in the architecture: Sparse Fea- ture Pulling, designed for the effective extraction of features from images to BeV, and Submanifold Attention, which en- ables efficient temporal modeling. The code is available at https://github.com/v...
An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-independent, identically distributed way. Further, synaptic plasticity in the brain depends only on information local to syn...
《RANet: Ranking Attention Network for Fast Video Object Segmentation》(ICCV 2019) GitHub: O网页链接《Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model》 (ICCV 2019) GitHub: O网页链接《RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution...
Conventional Vector Autoregressive (VAR) modelling methods applied to high dimensional neural time series data result in noisy solutions that are dense or have a large number of spurious coefficients. This reduces the speed and accuracy of auxiliary comp
The graph above demonstrates the performance of our kernel for a hybrid mask scenario, where half of the attention heads utilize a dense mask and the other half employ a streaming mask. For token-level streaming masks, we allocate 64 sink tokens and 256 local tokens. For block-level streaming...
🐛 Describe the bug Bellow is full reproduce code, I get quite high error. The mask in question is block-context like this (left context will be limited at some point) I do an assert between the reference mask and flex attention mask to m...