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05/24/2023
Flan-MoE: Scaling Instruction-Finetuned Language Models with Sparse Mixture of Experts
The explosive growth of language models and their applications have led ...
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05/20/2023
Lifelong Language Pretraining with Distribution-Specialized Experts
Pretraining on a large-scale corpus has become a standard method to buil...
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10/16/2022
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices
In this paper, we propose a data-model-hardware tri-design framework for...
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05/11/2022
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis
Advanced deep neural networks (DNNs), designed by either human or AutoML...
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02/24/2022
Auto-scaling Vision Transformers without Training
This work targets automated designing and scaling of Vision Transformers...
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12/17/2021
A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation
This work presents a simple vision transformer design as a strong baseli...
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08/26/2021
Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics
This work targets designing a principled and unified training-free frame...
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07/16/2021
DANCE: DAta-Network Co-optimization for Efficient Segmentation Model Training and Inference
Semantic segmentation for scene understanding is nowadays widely demande...
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04/06/2021
Contrastive Syn-to-Real Generalization
Training on synthetic data can be beneficial for label or data-scarce sc...
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03/23/2021
Learning to Optimize: A Primer and A Benchmark
Learning to optimize (L2O) is an emerging approach that leverages machin...
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02/23/2021
Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective
Neural Architecture Search (NAS) has been explosively studied to automat...
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02/22/2021
Sandwich Batch Normalization
We present Sandwich Batch Normalization (SaBN), an embarrassingly easy i...
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07/14/2020
Automated Synthetic-to-Real Generalization
Models trained on synthetic images often face degraded generalization to...
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06/15/2020
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
The compression of Generative Adversarial Networks (GANs) has lately dra...
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12/23/2019
FasterSeg: Searching for Faster Real-time Semantic Segmentation
We present FasterSeg, an automatically designed semantic segmentation ne...
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12/17/2019
In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation
The comparative losses (typically, triplet loss) are appealing choices f...
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11/26/2019
Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification
Many real-world applications, such as city-scale traffic monitoring and ...
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08/03/2019
ABD-Net: Attentive but Diverse Person Re-Identification
Attention mechanism has been shown to be effective for person re-identif...
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05/15/2019
