3 citations · 5 across the 6 of their papers we have counts for
6 papers
Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling
Tianqi Chen, Mingyuan Zhou
Learning to denoise has emerged as a prominent paradigm to design state-of-the-art deep generative models for natural images. How to use it to model the distributions of both conti…
Bayesian subtyping for multi-state brain functional connectome with application on adolescent brain cognition
Tianqi Chen, Chichun Tan, Hongyu Zhao +3
Converging evidence indicates that the heterogeneity of cognitive profiles may arise through detectable alternations in brain functions. Particularly, brain functional connectivity…
ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines
Siyuan Chen, Pratik Fegade, Tianqi Chen +2
Batching has a fundamental influence on the efficiency of deep neural network (DNN) execution. However, for dynamic DNNs, efficient batching is particularly challenging as the data…
TensorIR: An Abstraction for Automatic Tensorized Program Optimization
Siyuan Feng, Bohan Hou, Hongyi Jin +8
Deploying deep learning models on various devices has become an important topic. The wave of hardware specialization brings a diverse set of acceleration primitives for multi-dimen…
A Parallel and Efficient Algorithm for Learning to Match
Jingbo Shang, Tianqi Chen, Hang Li +2
Many tasks in data mining and related fields can be formalized as matching between objects in two heterogeneous domains, including collaborative filtering, link prediction, image t…
Flow Decomposition Reveals Dynamical Structure of Markov Process
Jianghong Shi, Tianqi Chen, Bo Yuan +1
Markov process is widely applied in almost all aspects of literature, especially important for understanding non-equilibrium processes. We introduce a decomposition to general Mark…