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09/07/2023
Large Language Models as Optimizers
Optimization is ubiquitous. While derivative-based algorithms have been ...
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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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05/17/2023
DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining
The mixture proportions of pretraining data domains (e.g., Wikipedia, bo...
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05/15/2023
Symbol tuning improves in-context learning in language models
We present symbol tuning - finetuning language models on in-context inpu...
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02/13/2023
Symbolic Discovery of Optimization Algorithms
We present a method to formulate algorithm discovery as program search, ...
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02/10/2023
Unified Functional Hashing in Automatic Machine Learning
The field of Automatic Machine Learning (AutoML) has recently attained i...
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02/08/2023
Noise2Music: Text-conditioned Music Generation with Diffusion Models
We introduce Noise2Music, where a series of diffusion models is trained ...
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02/03/2023
PyGlove: Efficiently Exchanging ML Ideas as Code
The increasing complexity and scale of machine learning (ML) has led to ...
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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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10/20/2022
Transcending Scaling Laws with 0.1
Scaling language models improves performance but comes with significant ...
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10/19/2022
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR
Data augmentation is a ubiquitous technique used to provide robustness t...
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03/23/2022
Revisiting Multi-Scale Feature Fusion for Semantic Segmentation
It is commonly believed that high internal resolution combined with expe...
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02/21/2022
Transformer Quality in Linear Time
We revisit the design choices in Transformers, and propose methods to ad...
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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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11/19/2021
Combined Scaling for Zero-shot Transfer Learning
We present a combined scaling method called BASIC that achieves 85.7 zer...
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09/27/2021
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition
We summarize the results of a host of efforts using giant automatic spee...
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09/17/2021
Primer: Searching for Efficient Transformers for Language Modeling
Large Transformer models have been central to recent advances in natural...
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09/13/2021
STraTA: Self-Training with Task Augmentation for Better Few-shot Learning
Despite their recent successes in tackling many NLP tasks, large-scale p...
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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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08/25/2021
Multi-Task Self-Training for Learning General Representations
Despite the fast progress in training specialized models for various tas...
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06/09/2021
CoAtNet: Marrying Convolution and Attention for All Data Sizes
Transformers have attracted increasing interests in computer vision, but...
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05/17/2021
Pay Attention to MLPs
Transformers have become one of the most important architectural innovat...
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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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02/11/2021
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
Pre-trained representations are becoming crucial for many NLP and percep...
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01/21/2021
PyGlove: Symbolic Programming for Automated Machine Learning
Neural networks are sensitive to hyper-parameter and architecture choice...
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01/08/2021
Evolving Reinforcement Learning Algorithms
We propose a method for meta-learning reinforcement learning algorithms ...
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01/05/2021
AutoDropout: Learning Dropout Patterns to Regularize Deep Networks
Neural networks are often over-parameterized and hence benefit from aggr...
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12/15/2020
Pre-Training Transformers as Energy-Based Cloze Models
We introduce Electric, an energy-based cloze model for representation le...
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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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11/09/2020
Towards Domain-Agnostic Contrastive Learning
Despite recent success, most contrastive self-supervised learning method...
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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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06/25/2020
Smooth Adversarial Training
It is commonly believed that networks cannot be both accurate and robust...
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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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06/05/2020
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
With the success of language pretraining, it is highly desirable to deve...
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05/19/2020
Improved Noisy Student Training for Automatic Speech Recognition
Recently, a semi-supervised learning method known as "noisy student trai...
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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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04/02/2020
Improving 3D Object Detection through Progressive Population Based Augmentation
Data augmentation has been widely adopted for object detection in 3D poi...
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03/23/2020
Meta Pseudo Labels
Many training algorithms of a deep neural network can be interpreted as ...
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03/23/2020
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
Masked language modeling (MLM) pre-training methods such as BERT corrupt...
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03/06/2020
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
Machine learning research has advanced in multiple aspects, including mo...
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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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12/11/2019
SpecAugment on Large Scale Datasets
Recently, SpecAugment, an augmentation scheme for automatic speech recog...
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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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12/02/2019
MnasFPN: Learning Latency-aware Pyramid Architecture for Object Detection on Mobile Devices
Despite the blooming success of architecture search for vision tasks in ...
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11/21/2019
Adversarial Examples Improve Image Recognition
Adversarial examples are commonly viewed as a threat to ConvNets. Here w...
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11/20/2019
EfficientDet: Scalable and Efficient Object Detection
Model efficiency has become increasingly important in computer vision. I...
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11/11/2019
