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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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02/13/2023
Symbolic Discovery of Optimization Algorithms
We present a method to formulate algorithm discovery as program search, ...
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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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04/28/2022
Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting–Full Version
A variety of real-world applications rely on far future information to m...
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12/16/2021
Automated Deep Learning: Neural Architecture Search Is Not the End
Deep learning (DL) has proven to be a highly effective approach for deve...
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11/05/2021
Recognizing Vector Graphics without Rasterization
In this paper, we consider a different data format for images: vector gr...
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08/30/2021
Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision
The energy consumption of deep learning models is increasing at a breath...
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02/17/2021
Rethinking Co-design of Neural Architectures and Hardware Accelerators
Neural architectures and hardware accelerators have been two driving for...
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02/03/2021
Isometric Propagation Network for Generalized Zero-shot Learning
Zero-shot learning (ZSL) aims to classify images of an unseen class only...
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01/25/2021
Supervision by Registration and Triangulation for Landmark Detection
We present Supervision by Registration and Triangulation (SRT), an unsup...
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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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08/28/2020
NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size
Neural architecture search (NAS) has attracted a lot of attention and ha...
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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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01/02/2020
NAS-Bench-102: Extending the Scope of Reproducible Neural Architecture Search
Neural architecture search (NAS) has achieved breakthrough success in a ...
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10/13/2019
One-Shot Neural Architecture Search via Self-Evaluated Template Network
Neural architecture search (NAS) aims to automate the search procedure o...
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10/10/2019
Searching for A Robust Neural Architecture in Four GPU Hours
Conventional neural architecture search (NAS) approaches are based on re...
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08/06/2019
Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection
Facial landmark detection aims to localize the anatomically defined poin...
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05/23/2019
Network Pruning via Transformable Architecture Search
Network pruning reduces the computation costs of an over-parameterized n...
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03/23/2019
Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification
Prevailing deep convolutional neural networks (CNNs) for person re-IDent...
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08/22/2018
Progressive Deep Neural Networks Acceleration via Soft Filter Pruning
This paper proposed a Progressive Soft Filter Pruning method (PSFP) to p...
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08/21/2018
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks
This paper proposed a Soft Filter Pruning (SFP) method to accelerate the...
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07/03/2018
Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors
In this paper, we present supervision-by-registration, an unsupervised a...
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03/12/2018
Style Aggregated Network for Facial Landmark Detection
Recent advances in facial landmark detection achieve success by learning...
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09/22/2017
EraseReLU: A Simple Way to Ease the Training of Deep Convolution Neural Networks
For most state-of-the-art architectures, Rectified Linear Unit (ReLU) be...
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07/22/2017
PatchShuffle Regularization
This paper focuses on regularizing the training of the convolutional neu...
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06/26/2017
Few-shot Object Detection
In this paper, we study object detection using a large pool of unlabeled...
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03/25/2017
