Search Results for author: Tianqi Chen

Found 24 papers, 10 papers with code

RAILS: A Robust Adversarial Immune-inspired Learning System

1 code implementation27 Jun 2021 Ren Wang, Tianqi Chen, Stephen Lindsly, Cooper Stansbury, Alnawaz Rehemtulla, Indika Rajapakse, Alfred Hero

Adversarial attacks against deep neural networks (DNNs) are continuously evolving, requiring increasingly powerful defense strategies.

Adversarial Defense Image Classification

ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense

1 code implementation27 Jun 2021 Ren Wang, Tianqi Chen, Philip Yao, Sijia Liu, Indika Rajapakse, Alfred Hero

K-Nearest Neighbor (kNN)-based deep learning methods have been applied to many applications due to their simplicity and geometric interpretability.

Immuno-mimetic Deep Neural Networks (Immuno-Net)

no code implementations27 Jun 2021 Ren Wang, Tianqi Chen, Stephen Lindsly, Cooper Stansbury, Indika Rajapakse, Alfred Hero

This immuno-mimetic model leads to a new computational biology framework for robustification of deep neural networks against adversarial attacks.

Image Classification

Automated Backend-Aware Post-Training Quantization

no code implementations27 Mar 2021 Ziheng Jiang, Animesh Jain, Andrew Liu, Josh Fromm, Chengqian Ma, Tianqi Chen, Luis Ceze

Quantization is a key technique to reduce the resource requirement and improve the performance of neural network deployment.

Quantization

RAILS: A Robust Adversarial Immune-inspired Learning System

no code implementations18 Dec 2020 Ren Wang, Tianqi Chen, Stephen Lindsly, Alnawaz Rehemtulla, Alfred Hero, Indika Rajapakse

RAILS incorporates an Adaptive Immune System Emulation (AISE), which emulates in silico the biological mechanisms that are used to defend the host against attacks by pathogens.

Adversarial Defense Image Classification

Cortex: A Compiler for Recursive Deep Learning Models

no code implementations2 Nov 2020 Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry

Optimizing deep learning models is generally performed in two steps: (i) high-level graph optimizations such as kernel fusion and (ii) low level kernel optimizations such as those found in vendor libraries.

Dynamic Tensor Rematerialization

1 code implementation ICLR 2021 Marisa Kirisame, Steven Lyubomirsky, Altan Haan, Jennifer Brennan, Mike He, Jared Roesch, Tianqi Chen, Zachary Tatlock

Checkpointing enables the training of deep learning models under restricted memory budgets by freeing intermediate activations from memory and recomputing them on demand.

Relay: A High-Level Compiler for Deep Learning

no code implementations17 Apr 2019 Jared Roesch, Steven Lyubomirsky, Marisa Kirisame, Logan Weber, Josh Pollock, Luis Vega, Ziheng Jiang, Tianqi Chen, Thierry Moreau, Zachary Tatlock

Using these extension mechanisms, Relay supports a unified compiler that can target a variety of hardware platforms.

ADARES: Adaptive Resource Management for Virtual Machines

no code implementations5 Dec 2018 Ignacio Cano, Lequn Chen, Pedro Fonseca, Tianqi Chen, Chern Cheah, Karan Gupta, Ramesh Chandra, Arvind Krishnamurthy

Our large-scale analysis confirms that VMs are often misconfigured, either overprovisioned or underprovisioned, and that this problem is pervasive across a wide range of private clusters.

Multi-Armed Bandits Transfer Learning

Automating Generation of Low Precision Deep Learning Operators

no code implementations25 Oct 2018 Meghan Cowan, Thierry Moreau, Tianqi Chen, Luis Ceze

To date, none of the popular deep learning directly support low precision operators, partly due to a lack of optimized low precision libraries.

Relay: A New IR for Machine Learning Frameworks

no code implementations26 Sep 2018 Jared Roesch, Steven Lyubomirsky, Logan Weber, Josh Pollock, Marisa Kirisame, Tianqi Chen, Zachary Tatlock

Machine learning powers diverse services in industry including search, translation, recommendation systems, and security.

Recommendation Systems

A Hardware-Software Blueprint for Flexible Deep Learning Specialization

no code implementations11 Jul 2018 Thierry Moreau, Tianqi Chen, Luis Vega, Jared Roesch, Eddie Yan, Lianmin Zheng, Josh Fromm, Ziheng Jiang, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance while sacrificing flexibility.

Code Generation Object Classification +1

Learning to Optimize Tensor Programs

no code implementations NeurIPS 2018 Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

Efficient implementations of tensor operators, such as matrix multiplication and high dimensional convolution, are key enablers of effective deep learning systems.

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

1 code implementation12 Feb 2018 Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.

Training Deep Nets with Sublinear Memory Cost

5 code implementations21 Apr 2016 Tianqi Chen, Bing Xu, Chiyuan Zhang, Carlos Guestrin

In the extreme case, our analysis also shows that the memory consumption can be reduced to O(log n) with as little as O(n log n) extra cost for forward computation.

XGBoost: A Scalable Tree Boosting System

23 code implementations9 Mar 2016 Tianqi Chen, Carlos Guestrin

In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges.

Dimensionality Reduction General Classification +1

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

2 code implementations3 Dec 2015 Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, Zheng Zhang

This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion.

Dimensionality Reduction General Classification

Net2Net: Accelerating Learning via Knowledge Transfer

3 code implementations18 Nov 2015 Tianqi Chen, Ian Goodfellow, Jonathon Shlens

Our Net2Net technique accelerates the experimentation process by instantaneously transferring the knowledge from a previous network to each new deeper or wider network.

Transfer Learning

A Complete Recipe for Stochastic Gradient MCMC

no code implementations NeurIPS 2015 Yi-An Ma, Tianqi Chen, Emily B. Fox

That is, any continuous Markov process that provides samples from the target distribution can be written in our framework.

Empirical Evaluation of Rectified Activations in Convolutional Network

2 code implementations5 May 2015 Bing Xu, Naiyan Wang, Tianqi Chen, Mu Li

In this paper we investigate the performance of different types of rectified activation functions in convolutional neural network: standard rectified linear unit (ReLU), leaky rectified linear unit (Leaky ReLU), parametric rectified linear unit (PReLU) and a new randomized leaky rectified linear units (RReLU).

General Classification Image Classification

A Parallel and Efficient Algorithm for Learning to Match

no code implementations22 Oct 2014 Jingbo Shang, Tianqi Chen, Hang Li, Zhengdong Lu, Yong Yu

In this paper, we tackle this challenge with a novel parallel and efficient algorithm for feature-based matrix factorization.

Link Prediction

Stochastic Gradient Hamiltonian Monte Carlo

5 code implementations17 Feb 2014 Tianqi Chen, Emily B. Fox, Carlos Guestrin

Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining distant proposals with high acceptance probabilities in a Metropolis-Hastings framework, enabling more efficient exploration of the state space than standard random-walk proposals.

Efficient Exploration

Semi-Supervised Technical Term Tagging With Minimal User Feedback

no code implementations LREC 2012 Behrang QasemiZadeh, Paul Buitelaar, Tianqi Chen, Georgeta Bordea

In this paper, we address the problem of extracting technical terms automatically from an unannotated corpus.

Dependency Parsing Language Modelling +2

Feature-Based Matrix Factorization

no code implementations11 Sep 2011 Tianqi Chen, Zhao Zheng, Qiuxia Lu, Weinan Zhang, Yong Yu

Recommender system has been more and more popular and widely used in many applications recently.

Recommendation Systems

Cannot find the paper you are looking for? You can Submit a new open access paper.