Jonathon Shlens

research

∙ 04/10/2023

On Robustness in Multimodal Learning

Multimodal learning is defined as learning over multiple heterogeneous i...

0 Brandon McKinzie, et al. ∙

research

∙ 10/18/2022

Perceptual Grouping in Vision-Language Models

Recent advances in zero-shot image recognition suggest that vision-langu...

3 Kanchana Ranasinghe, et al. ∙

research

∙ 07/30/2021

Soft Calibration Objectives for Neural Networks

Optimal decision making requires that classifiers produce uncertainty es...

0 Archit Karandikar, et al. ∙

research

∙ 06/15/2021

Scene Transformer: A unified multi-task model for behavior prediction and planning

Predicting the future motion of multiple agents is necessary for plannin...

7 Jiquan Ngiam, et al. ∙

research

∙ 04/20/2021

Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset

As autonomous driving systems mature, motion forecasting has received in...

27 Scott Ettinger, et al. ∙

research

∙ 03/23/2021

Scaling Local Self-Attention For Parameter Efficient Visual Backbones

Self-attention has the promise of improving computer vision systems due ...

0 Ashish Vaswani, et al. ∙

research

∙ 03/13/2021

Revisiting ResNets: Improved Training and Scaling Strategies

Novel computer vision architectures monopolize the spotlight, but the im...

0 Irwan Bello, et al. ∙

research

∙ 03/02/2021

Pseudo-labeling for Scalable 3D Object Detection

To safely deploy autonomous vehicles, onboard perception systems must wo...

0 Benjamin Caine, et al. ∙

research

∙ 03/01/2021

Scalable Scene Flow from Point Clouds in the Real World

Autonomous vehicles operate in highly dynamic environments necessitating...

0 Philipp Jund, et al. ∙

research

∙ 01/27/2021

Bottleneck Transformers for Visual Recognition

We present BoTNet, a conceptually simple yet powerful backbone architect...

83 Aravind Srinivas, et al. ∙

research

∙ 12/15/2020

Mitigating bias in calibration error estimation

Building reliable machine learning systems requires that we correctly un...

9 Rebecca Roelofs, et al. ∙

research

∙ 05/20/2020

Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation

Supervised learning in large discriminative models is a mainstay for mod...

14 Liang-Chieh Chen, et al. ∙

research

∙ 05/20/2020

Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation

Supervised learning in large discriminative models is a mainstay for mod...

7 Liang-Chieh Chen, et al. ∙

research

∙ 05/04/2020

Streaming Object Detection for 3-D Point Clouds

Autonomous vehicles operate in a dynamic environment, where the speed wi...

6 Wei Han, et al. ∙

research

∙ 04/02/2020

Improving 3D Object Detection through Progressive Population Based Augmentation

Data augmentation has been widely adopted for object detection in 3D poi...

1 Shuyang Cheng, et al. ∙

research

∙ 02/07/2020

Revisiting Spatial Invariance with Low-Rank Local Connectivity

Convolutional neural networks are among the most successful architecture...

3 Gamaleldin F. Elsayed, et al. ∙

research

∙ 12/10/2019

Scalability in Perception for Autonomous Driving: Waymo Open Dataset

The research community has increasing interest in autonomous driving res...

0 Pei Sun, et al. ∙

research

∙ 09/30/2019

RandAugment: Practical data augmentation with no separate search

Recent work has shown that data augmentation has the potential to signif...

26 Ekin D. Cubuk, et al. ∙

research

∙ 08/29/2019

StarNet: Targeted Computation for Object Detection in Point Clouds

LiDAR sensor systems provide high resolution spatial information about t...

0 Jiquan Ngiam, et al. ∙

research

∙ 06/26/2019

Learning Data Augmentation Strategies for Object Detection

Data augmentation is a critical component of training deep learning mode...

4 Barret Zoph, et al. ∙

research

∙ 06/21/2019

A Fourier Perspective on Model Robustness in Computer Vision

Achieving robustness to distributional shift is a longstanding and chall...

3 Dong Yin, et al. ∙

research

∙ 06/13/2019

Stand-Alone Self-Attention in Vision Models

Convolutions are a fundamental building block of modern computer vision ...

0 Prajit Ramachandran, et al. ∙

research

∙ 06/12/2019

Visual Wake Words Dataset

The emergence of Internet of Things (IoT) applications requires intellig...

0 Aakanksha Chowdhery, et al. ∙

research

∙ 06/08/2019

Using learned optimizers to make models robust to input noise

State-of-the art vision models can achieve superhuman performance on ima...

0 Luke Metz, et al. ∙

research

∙ 04/22/2019

Attention Augmented Convolutional Networks

Convolutional networks have been the paradigm of choice in many computer...

30 Irwan Bello, et al. ∙

research

∙ 04/04/2019

A Learned Representation for Scalable Vector Graphics

Dramatic advances in generative models have resulted in near photographi...

0 Raphael Gontijo Lopes, et al. ∙

research

∙ 03/03/2019

Accelerating Training of Deep Neural Networks with a Standardization Loss

A significant advance in accelerating neural network training has been t...

0 Jasmine Collins, et al. ∙

research

∙ 09/11/2018

Searching for Efficient Multi-Scale Architectures for Dense Image Prediction

The design of neural network architectures is an important component for...

2 Liang-Chieh Chen, et al. ∙

research

∙ 05/23/2018

Do Better ImageNet Models Transfer Better?

Transfer learning has become a cornerstone of computer vision with the a...

0 Simon Kornblith, et al. ∙

research

∙ 03/16/2018

A dataset and architecture for visual reasoning with a working memory

A vexing problem in artificial intelligence is reasoning about events th...

0 Guangyu Robert Yang, et al. ∙

research

∙ 12/02/2017

Progressive Neural Architecture Search

We propose a method for learning CNN structures that is more efficient t...

0 Chenxi Liu, et al. ∙

research

∙ 11/28/2017

Recurrent Segmentation for Variable Computational Budgets

State-of-the-art systems for semantic image segmentation utilize feed-fo...

0 Lane McIntosh, et al. ∙

research

∙ 07/21/2017

Learning Transferable Architectures for Scalable Image Recognition

Developing neural network image classification models often requires sig...

0 Barret Zoph, et al. ∙

research

∙ 05/19/2017

PixColor: Pixel Recursive Colorization

We propose a novel approach to automatically produce multiple colorized ...

0 Sergio Guadarrama, et al. ∙

research

∙ 05/18/2017

Exploring the structure of a real-time, arbitrary neural artistic stylization network

In this paper, we present a method which combines the flexibility of the...

0 Golnaz Ghiasi, et al. ∙

research

∙ 02/02/2017

YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video

We introduce a new large-scale data set of video URLs with densely-sampl...

0 Esteban Real, et al. ∙

research

∙ 02/02/2017

Pixel Recursive Super Resolution

We present a pixel recursive super resolution model that synthesizes rea...

0 Ryan Dahl, et al. ∙

research

∙ 10/30/2016

Conditional Image Synthesis With Auxiliary Classifier GANs

Synthesizing high resolution photorealistic images has been a long-stand...

0 Augustus Odena, et al. ∙

research

∙ 10/24/2016

A Learned Representation For Artistic Style

The diversity of painting styles represents a rich visual vocabulary for...

0 Vincent Dumoulin, et al. ∙

research

∙ 03/14/2016

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

TensorFlow is an interface for expressing machine learning algorithms, a...

0 Martín Abadi, et al. ∙

research

∙ 12/02/2015

Rethinking the Inception Architecture for Computer Vision

Convolutional networks are at the core of most state-of-the-art computer...

0 Christian Szegedy, et al. ∙

research

∙ 11/18/2015

Adversarial Autoencoders

In this paper, we propose the "adversarial autoencoder" (AAE), which is ...

0 Alireza Makhzani, 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

∙ 12/23/2014

Deep Networks With Large Output Spaces

Deep neural networks have been extremely successful at various image, sp...

0 Sudheendra Vijayanarasimhan, et al. ∙

research

∙ 12/20/2014

Explaining and Harnessing Adversarial Examples

Several machine learning models, including neural networks, consistently...

0 Ian J. Goodfellow, et al. ∙

research

∙ 04/11/2014

A Tutorial on Independent Component Analysis

Independent component analysis (ICA) has become a standard data analysis...

0 Jonathon Shlens, et al. ∙

research

∙ 04/08/2014

Notes on Generalized Linear Models of Neurons

Experimental neuroscience increasingly requires tractable models for ana...

0 Jonathon Shlens, et al. ∙

research

∙ 04/03/2014

A Tutorial on Principal Component Analysis

Principal component analysis (PCA) is a mainstay of modern data analysis...

0 Jonathon Shlens, et al. ∙

research

∙ 12/19/2013

Using Web Co-occurrence Statistics for Improving Image Categorization

Object recognition and localization are important tasks in computer visi...

0 Samy Bengio, et al. ∙

Jonathon Shlens

Featured Co-authors