b'Zhuang Liu'

research

∙ 06/20/2023

A Simple and Effective Pruning Approach for Large Language Models

As their size increases, Large Languages Models (LLMs) are natural candi...

0 Mingjie Sun, et al. ∙

research

∙ 06/13/2023

One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning

We present Generalized LoRA (GLoRA), an advanced approach for universal ...

0 Arnav Chavan, et al. ∙

research

∙ 05/09/2023

ImageBind: One Embedding Space To Bind Them All

We present ImageBind, an approach to learn a joint embedding across six ...

0 Rohit Girdhar, et al. ∙

research

∙ 04/25/2023

A optimization framework for herbal prescription planning based on deep reinforcement learning

Treatment planning for chronic diseases is a critical task in medical ar...

0 Kuo Yang, et al. ∙

research

∙ 03/02/2023

Dropout Reduces Underfitting

Introduced by Hinton et al. in 2012, dropout has stood the test of time ...

0 Zhuang Liu, et al. ∙

research

∙ 01/02/2023

ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Driven by improved architectures and better representation learning fram...

0 Sanghyun Woo, et al. ∙

research

∙ 07/25/2022

Lumen Shape Reconstruction using a Soft Robotic Balloon Catheter and Electrical Impedance Tomography

Incorrectly sized balloon catheters can lead to increased post-surgical ...

0 James Avery, et al. ∙

research

∙ 01/10/2022

A ConvNet for the 2020s

The "Roaring 20s" of visual recognition began with the introduction of V...

12 Zhuang Liu, et al. ∙

research

∙ 01/03/2022

Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space

This paper explores the feasibility of finding an optimal sub-model from...

17 Arnav Chavan, et al. ∙

research

∙ 12/27/2021

MSeg: A Composite Dataset for Multi-domain Semantic Segmentation

We present MSeg, a composite dataset that unifies semantic segmentation ...

6 John Lambert, et al. ∙

research

∙ 04/01/2021

Confidence Adaptive Anytime Pixel-Level Recognition

Anytime inference requires a model to make a progression of predictions ...

0 Zhuang Liu, et al. ∙

research

∙ 01/05/2021

Contrastive Learning for Recommender System

Recommender systems, which analyze users' preference patterns to suggest...

0 Zhuang Liu, et al. ∙

research

∙ 03/11/2020

Rethinking Image Mixture for Unsupervised Visual Representation Learning

In supervised learning, smoothing label/prediction distribution in neura...

8 Zhiqiang Shen, et al. ∙

research

∙ 03/09/2020

A New Meta-Baseline for Few-Shot Learning

Meta-learning has become a popular framework for few-shot learning in re...

11 Yinbo Chen, et al. ∙

research

∙ 01/08/2020

Convolutional Networks with Dense Connectivity

Recent work has shown that convolutional networks can be substantially d...

24 Gao Huang, et al. ∙

research

∙ 01/07/2020

Leveraging Prior Knowledge for Protein-Protein Interaction Extraction with Memory Network

Automatically extracting Protein-Protein Interactions (PPI) from biomedi...

0 Huiwei Zhou, et al. ∙

research

∙ 01/02/2020

Chemical-induced Disease Relation Extraction with Dependency Information and Prior Knowledge

Chemical-disease relation (CDR) extraction is significantly important to...

0 Huiwei Zhou, et al. ∙

research

∙ 12/23/2019

Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction

Automatically extracting the relationships between chemicals and disease...

0 Huiwei Zhou, et al. ∙

research

∙ 12/23/2019

Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction

Background: Automatic extraction of chemical-disease relations (CDR) fro...

0 Huiwei Zhou, et al. ∙

research

∙ 12/11/2019

Improving Neural Protein-Protein Interaction Extraction with Knowledge Selection

Protein-protein interaction (PPI) extraction from published scientific l...

0 Huiwei Zhou, et al. ∙

research

∙ 10/21/2019

Regularization Matters in Policy Optimization

Deep Reinforcement Learning (Deep RL) has been receiving increasingly mo...

21 Zhuang Liu, et al. ∙

research

∙ 10/21/2019

Transferable Recognition-Aware Image Processing

Recent progress in image recognition has stimulated the deployment of vi...

0 Zhuang Liu, et al. ∙

research

∙ 09/29/2019

Test-Time Training for Out-of-Distribution Generalization

We introduce a general approach, called test-time training, for improvin...

8 Yu Sun, et al. ∙

research

∙ 12/05/2018

Few-shot Object Detection via Feature Reweighting

This work aims to solve the challenging few-shot object detection proble...

0 Bingyi Kang, et al. ∙

research

∙ 12/05/2018

Knowledge Distillation from Few Samples

Current knowledge distillation methods require full training data to dis...

0 Tianhong Li, et al. ∙

research

∙ 10/11/2018

Rethinking the Value of Network Pruning

Network pruning is widely used for reducing the heavy computational cost...

0 Zhuang Liu, et al. ∙

research

∙ 09/25/2018

Object Detection from Scratch with Deep Supervision

We propose Deeply Supervised Object Detectors (DSOD), an object detectio...

4 Zhiqiang Shen, et al. ∙

research

∙ 08/22/2017

Learning Efficient Convolutional Networks through Network Slimming

The deployment of deep convolutional neural networks (CNNs) in many real...

0 Zhuang Liu, et al. ∙

research

∙ 08/03/2017

DSOD: Learning Deeply Supervised Object Detectors from Scratch

We present Deeply Supervised Object Detector (DSOD), a framework that ca...

0 Zhiqiang Shen, et al. ∙

research

∙ 08/25/2016

Densely Connected Convolutional Networks

Recent work has shown that convolutional networks can be substantially d...

0 Gao Huang, et al. ∙

research

∙ 03/30/2016

Deep Networks with Stochastic Depth

Very deep convolutional networks with hundreds of layers have led to sig...

0 Gao Huang, et al. ∙

Zhuang Liu

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