297 citations · 390 across the 13 of their papers we have counts for
13 papers
Generalized Neighborhood Attention: Multi-dimensional Sparse Attention at the Speed of Light
Ali Hassani, Fengzhe Zhou, Aditya Kane +13
Many sparse attention mechanisms such as Neighborhood Attention have typically failed to consistently deliver speedup over the self attention baseline. This is largely due to the l…
Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control
NVIDIA, :, Hassan Abu Alhaija +38
We introduce Cosmos-Transfer, a conditional world generation model that can generate world simulations based on multiple spatial control inputs of various modalities such as segmen…
Faster Neighborhood Attention: Reducing the O(n^2) Cost of Self Attention at the Threadblock Level
Ali Hassani, Wen-Mei Hwu, Humphrey Shi
Neighborhood attention reduces the cost of self attention by restricting each token's attention span to its nearest neighbors. This restriction, parameterized by a window size and…
OneFormer: One Transformer to Rule Universal Image Segmentation
Jitesh Jain, Jiachen Li, MangTik Chiu +3
Universal Image Segmentation is not a new concept. Past attempts to unify image segmentation in the last decades include scene parsing, panoptic segmentation, and, more recently, n…
Efficient Image Generation with Variadic Attention Heads
Steven Walton, Ali Hassani, Xingqian Xu +2
While the integration of transformers in vision models have yielded significant improvements on vision tasks they still require significant amounts of computation for both training…
AdaFocusV3: On Unified Spatial-temporal Dynamic Video Recognition
Yulin Wang, Yang Yue, Xinhong Xu +6
Recent research has revealed that reducing the temporal and spatial redundancy are both effective approaches towards efficient video recognition, e.g., allocating the majority of c…
Dilated Neighborhood Attention Transformer
Ali Hassani, Humphrey Shi
Transformers are quickly becoming one of the most heavily applied deep learning architectures across modalities, domains, and tasks. In vision, on top of ongoing efforts into plain…
DiSparse: Disentangled Sparsification for Multitask Model Compression
Xinglong Sun, Ali Hassani, Zhangyang Wang +2
Despite the popularity of Model Compression and Multitask Learning, how to effectively compress a multitask model has been less thoroughly analyzed due to the challenging entanglem…
Neighborhood Attention Transformer
Ali Hassani, Steven Walton, Jiachen Li +2
We present Neighborhood Attention (NA), the first efficient and scalable sliding-window attention mechanism for vision. NA is a pixel-wise operation, localizing self attention (SA)…
ConvMLP: Hierarchical Convolutional MLPs for Vision
Jiachen Li, Ali Hassani, Steven Walton +1
MLP-based architectures, which consist of a sequence of consecutive multi-layer perceptron blocks, have recently been found to reach comparable results to convolutional and transfo…
Escaping the Big Data Paradigm with Compact Transformers
Ali Hassani, Steven Walton, Nikhil Shah +3
With the rise of Transformers as the standard for language processing, and their advancements in computer vision, there has been a corresponding growth in parameter size and amount…
Text Mining using Nonnegative Matrix Factorization and Latent Semantic Analysis
Ali Hassani, Amir Iranmanesh, Najme Mansouri
Text clustering is arguably one of the most important topics in modern data mining. Nevertheless, text data require tokenization which usually yields a very large and highly sparse…
DISCERN: Diversity-based Selection of Centroids for k-Estimation and Rapid Non-stochastic Clustering
Ali Hassani, Amir Iranmanesh, Mahdi Eftekhari +1
One of the applications of center-based clustering algorithms such as K-Means is partitioning data points into K clusters. In some examples, the feature space relates to the underl…