b'Tianqi Chen'

research

∙ 09/14/2023

Beta Diffusion

We introduce beta diffusion, a novel generative modeling method that int...

0 Mingyuan Zhou, et al. ∙

research

∙ 05/28/2023

Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling

Learning to denoise has emerged as a prominent paradigm to design state-...

0 Tianqi Chen, et al. ∙

research

∙ 05/17/2023

ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time

Dynamic control flow is an important technique often used to design expr...

0 Pratik Fegade, et al. ∙

research

∙ 02/20/2023

Bayesian subtyping for multi-state brain functional connectome with application on adolescent brain cognition

Converging evidence indicates that the heterogeneity of cognitive profil...

0 Tianqi Chen, et al. ∙

research

∙ 02/08/2023

ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines

Batching has a fundamental influence on the efficiency of deep neural ne...

0 Siyuan Chen, et al. ∙

research

∙ 08/25/2022

SONAR: Joint Architecture and System Optimization Search

There is a growing need to deploy machine learning for different tasks o...

0 Elias Jääsaari, et al. ∙

research

∙ 07/11/2022

SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning

Sparse tensors are rapidly becoming critical components of modern deep l...

10 Zihao Ye, et al. ∙

research

∙ 07/09/2022

TensorIR: An Abstraction for Automatic Tensorized Program Optimization

Deploying deep learning models on various devices has become an importan...

0 Siyuan Feng, et al. ∙

research

∙ 05/26/2022

Tensor Program Optimization with Probabilistic Programs

Automatic optimization for tensor programs becomes increasingly importan...

0 Junru Shao, et al. ∙

research

∙ 01/27/2022

Bayesian network mediation analysis with application to brain functional connectome

Brain functional connectome, the collection of interconnected neural cir...

0 Yize Zhao, et al. ∙

research

∙ 11/01/2021

Collage: Automated Integration of Deep Learning Backends

Strong demands for efficient deployment of Deep Learning (DL) applicatio...

0 Byungsoo Jeon, et al. ∙

research

∙ 10/19/2021

The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding

There is often variation in the shape and size of input data used for de...

0 Pratik Fegade, et al. ∙

research

∙ 08/15/2021

Deep Adversarially-Enhanced k-Nearest Neighbors

Recent works have theoretically and empirically shown that deep neural n...

0 Ren Wang, et al. ∙

research

∙ 06/27/2021

ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense

K-Nearest Neighbor (kNN)-based deep learning methods have been applied t...

19 Ren Wang, et al. ∙

research

∙ 06/27/2021

Immuno-mimetic Deep Neural Networks (Immuno-Net)

Biomimetics has played a key role in the evolution of artificial neural ...

0 Ren Wang, et al. ∙

research

∙ 03/27/2021

Automated Backend-Aware Post-Training Quantization

Quantization is a key technique to reduce the resource requirement and i...

5 Ziheng Jiang, et al. ∙

research

∙ 12/18/2020

RAILS: A Robust Adversarial Immune-inspired Learning System

Adversarial attacks against deep neural networks are continuously evolvi...

0 Ren Wang, et al. ∙

research

∙ 11/02/2020

Cortex: A Compiler for Recursive Deep Learning Models

Optimizing deep learning models is generally performed in two steps: (i)...

0 Pratik Fegade, et al. ∙

research

∙ 06/17/2020

Dynamic Tensor Rematerialization

Checkpointing enables training deep learning models under restricted mem...

102 Marisa Kirisame, et al. ∙

research

∙ 04/17/2019

Relay: A High-Level Compiler for Deep Learning

Frameworks for writing, compiling, and optimizing deep learning (DL) mod...

0 Jared Roesch, et al. ∙

research

∙ 04/17/2019

Relay: A High-Level IR for Deep Learning

Frameworks for writing, compiling, and optimizing deep learning (DL) mod...

0 Jared Roesch, et al. ∙

research

∙ 12/05/2018

ADARES: Adaptive Resource Management for Virtual Machines

Virtual execution environments allow for consolidation of multiple appli...

0 Ignacio Cano, et al. ∙

research

∙ 10/25/2018

Automating Generation of Low Precision Deep Learning Operators

State of the art deep learning models have made steady progress in the f...

0 Meghan Cowan, et al. ∙

research

∙ 09/26/2018

Relay: A New IR for Machine Learning Frameworks

Machine learning powers diverse services in industry including search, t...

0 Jared Roesch, et al. ∙

research

∙ 07/11/2018

VTA: An Open Hardware-Software Stack for Deep Learning

Hardware acceleration is an enabler for ubiquitous and efficient deep le...

6 Thierry Moreau, et al. ∙

research

∙ 05/21/2018

Learning to Optimize Tensor Programs

We introduce a learning-based framework to optimize tensor programs for ...

0 Tianqi Chen, et al. ∙

research

∙ 02/12/2018

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

There is an increasing need to bring machine learning to a wide diversit...

0 Tianqi Chen, et al. ∙

research

∙ 02/12/2018

TVM: End-to-End Optimization Stack for Deep Learning

Scalable frameworks, such as TensorFlow, MXNet, Caffe, and PyTorch drive...

0 Tianqi Chen, et al. ∙

research

∙ 12/03/2015

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

MXNet is a multi-language machine learning (ML) library to ease the deve...

0 Tianqi Chen, et al. ∙

research

∙ 11/18/2015

Net2Net: Accelerating Learning via Knowledge Transfer

We introduce techniques for rapidly transferring the information stored ...

0 Tianqi Chen, et al. ∙

research

∙ 06/15/2015

A Complete Recipe for Stochastic Gradient MCMC

Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous...

0 Yi-an Ma, et al. ∙

research

∙ 05/05/2015

Empirical Evaluation of Rectified Activations in Convolutional Network

In this paper we investigate the performance of different types of recti...

0 Bing Xu, et al. ∙

research

∙ 10/22/2014

A Parallel and Efficient Algorithm for Learning to Match

Many tasks in data mining and related fields can be formalized as matchi...

0 Jingbo Shang, et al. ∙

research

∙ 02/17/2014

Stochastic Gradient Hamiltonian Monte Carlo

Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for d...

0 Tianqi Chen, et al. ∙

research

∙ 09/11/2011

Feature-Based Matrix Factorization

Recommender system has been more and more popular and widely used in man...

0 Tianqi Chen, et al. ∙

Tianqi Chen

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