7.3k citations · 9.4k across the 29 of their papers we have counts for
29 papers
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer
Kaize Ding, Albert Jiongqian Liang, Bryan Perrozi +6
Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval. Though recent deep learning methods can parti…
FIT: Far-reaching Interleaved Transformers
Ting Chen, Lala Li
We present FIT: a transformer-based architecture with efficient self-attention and adaptive computation. Unlike original transformers, which operate on a single sequence of data to…
Scalable Adaptive Computation for Iterative Generation
Allan Jabri, David Fleet, Ting Chen
Natural data is redundant yet predominant architectures tile computation uniformly across their input and output space. We propose the Recurrent Interface Networks (RINs), an atten…
A Generalist Framework for Panoptic Segmentation of Images and Videos
Ting Chen, Lala Li, Saurabh Saxena +2
Panoptic segmentation assigns semantic and instance ID labels to every pixel of an image. As permutations of instance IDs are also valid solutions, the task requires learning of hi…
Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning
Ting Chen, Ruixiang Zhang, Geoffrey Hinton
We present Bit Diffusion: a simple and generic approach for generating discrete data with continuous state and continuous time diffusion models. The main idea behind our approach i…
A Unified Sequence Interface for Vision Tasks
Ting Chen, Saurabh Saxena, Lala Li +3
While language tasks are naturally expressed in a single, unified, modeling framework, i.e., generating sequences of tokens, this has not been the case in computer vision. As a res…