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08/07/2023
Simple synthetic data reduces sycophancy in large language models
Sycophancy is an undesirable behavior where models tailor their response...
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01/31/2023
The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
We study the design decisions of publicly available instruction tuning m...
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11/03/2022
Inverse scaling can become U-shaped
Although scaling language models improves performance on a range of task...
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12/13/2021
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Scaling language models with more data, compute and parameters has drive...
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09/03/2021
Finetuned Language Models Are Zero-Shot Learners
This paper explores a simple method for improving the zero-shot learning...
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04/01/2021
EfficientNetV2: Smaller Models and Faster Training
This paper introduces EfficientNetV2, a new family of convolutional netw...
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12/13/2020
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
Building instance segmentation models that are data-efficient and can ha...
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10/20/2020
Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition
We employ a combination of recent developments in semi-supervised learni...
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06/11/2020
Rethinking Pre-training and Self-training
Pre-training is a dominant paradigm in computer vision. For example, sup...
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06/05/2020
AutoHAS: Differentiable Hyper-parameter and Architecture Search
Neural Architecture Search (NAS) has achieved significant progress in pu...
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04/22/2020
Chip Placement with Deep Reinforcement Learning
In this work, we present a learning-based approach to chip placement, on...
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04/06/2020
Evolving Normalization-Activation Layers
Normalization layers and activation functions are critical components in...
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01/27/2020
Towards a Human-like Open-Domain Chatbot
We present Meena, a multi-turn open-domain chatbot trained end-to-end on...
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12/10/2019
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
Convolutional neural networks typically encode an input image into a ser...
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09/30/2019
RandAugment: Practical data augmentation with no separate search
Recent work has shown that data augmentation has the potential to signif...
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07/22/2019
MixNet: Mixed Depthwise Convolutional Kernels
Depthwise convolution is becoming increasingly popular in modern efficie...
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06/26/2019
Learning Data Augmentation Strategies for Object Detection
Data augmentation is a critical component of training deep learning mode...
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05/28/2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Convolutional Neural Networks (ConvNets) are commonly developed at a fix...
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05/06/2019
Searching for MobileNetV3
We present the next generation of MobileNets based on a combination of c...
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04/29/2019
Unsupervised Data Augmentation
Despite its success, deep learning still needs large labeled datasets to...
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04/18/2019
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
We present SpecAugment, a simple data augmentation method for speech rec...
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04/10/2019
Soft Conditional Computation
Conditional computation aims to increase the size and accuracy of a netw...
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01/30/2019
The Evolved Transformer
Recent works have highlighted the strengths of the Transformer architect...
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01/09/2019
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Transformer networks have a potential of learning longer-term dependency...
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11/16/2018
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
GPipe is a scalable pipeline parallelism library that enables learning o...
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07/31/2018
MnasNet: Platform-Aware Neural Architecture Search for Mobile
Designing convolutional neural networks (CNN) models for mobile devices ...
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05/24/2018
AutoAugment: Learning Augmentation Policies from Data
In this paper, we take a closer look at data augmentation for images, an...
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05/23/2018
Do Better ImageNet Models Transfer Better?
Transfer learning has become a cornerstone of computer vision with the a...
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04/23/2018
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
Current end-to-end machine reading and question answering (Q&A) models a...
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02/05/2018
Regularized Evolution for Image Classifier Architecture Search
The effort devoted to hand-crafting image classifiers has motivated the ...
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11/01/2017
Don't Decay the Learning Rate, Increase the Batch Size
It is common practice to decay the learning rate. Here we show one can u...
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07/21/2017
