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06/20/2023
A Simple and Effective Pruning Approach for Large Language Models
As their size increases, Large Languages Models (LLMs) are natural candi...
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06/13/2023
One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning
We present Generalized LoRA (GLoRA), an advanced approach for universal ...
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05/09/2023
ImageBind: One Embedding Space To Bind Them All
We present ImageBind, an approach to learn a joint embedding across six ...
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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...
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03/02/2023
Dropout Reduces Underfitting
Introduced by Hinton et al. in 2012, dropout has stood the test of time ...
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01/02/2023
ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
Driven by improved architectures and better representation learning fram...
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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 ...
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01/10/2022
A ConvNet for the 2020s
The "Roaring 20s" of visual recognition began with the introduction of V...
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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...
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12/27/2021
MSeg: A Composite Dataset for Multi-domain Semantic Segmentation
We present MSeg, a composite dataset that unifies semantic segmentation ...
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04/01/2021
Confidence Adaptive Anytime Pixel-Level Recognition
Anytime inference requires a model to make a progression of predictions ...
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01/05/2021
Contrastive Learning for Recommender System
Recommender systems, which analyze users' preference patterns to suggest...
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03/11/2020
Rethinking Image Mixture for Unsupervised Visual Representation Learning
In supervised learning, smoothing label/prediction distribution in neura...
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03/09/2020
A New Meta-Baseline for Few-Shot Learning
Meta-learning has become a popular framework for few-shot learning in re...
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01/08/2020
Convolutional Networks with Dense Connectivity
Recent work has shown that convolutional networks can be substantially d...
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01/07/2020
Leveraging Prior Knowledge for Protein-Protein Interaction Extraction with Memory Network
Automatically extracting Protein-Protein Interactions (PPI) from biomedi...
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01/02/2020
Chemical-induced Disease Relation Extraction with Dependency Information and Prior Knowledge
Chemical-disease relation (CDR) extraction is significantly important to...
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12/23/2019
Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction
Automatically extracting the relationships between chemicals and disease...
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12/23/2019
Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction
Background: Automatic extraction of chemical-disease relations (CDR) fro...
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12/11/2019
Improving Neural Protein-Protein Interaction Extraction with Knowledge Selection
Protein-protein interaction (PPI) extraction from published scientific l...
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10/21/2019
Regularization Matters in Policy Optimization
Deep Reinforcement Learning (Deep RL) has been receiving increasingly mo...
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10/21/2019
Transferable Recognition-Aware Image Processing
Recent progress in image recognition has stimulated the deployment of vi...
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09/29/2019
Test-Time Training for Out-of-Distribution Generalization
We introduce a general approach, called test-time training, for improvin...
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12/05/2018
Few-shot Object Detection via Feature Reweighting
This work aims to solve the challenging few-shot object detection proble...
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12/05/2018
Knowledge Distillation from Few Samples
Current knowledge distillation methods require full training data to dis...
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10/11/2018
Rethinking the Value of Network Pruning
Network pruning is widely used for reducing the heavy computational cost...
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09/25/2018
Object Detection from Scratch with Deep Supervision
We propose Deeply Supervised Object Detectors (DSOD), an object detectio...
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08/22/2017
Learning Efficient Convolutional Networks through Network Slimming
The deployment of deep convolutional neural networks (CNNs) in many real...
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08/03/2017
DSOD: Learning Deeply Supervised Object Detectors from Scratch
We present Deeply Supervised Object Detector (DSOD), a framework that ca...
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08/25/2016
Densely Connected Convolutional Networks
Recent work has shown that convolutional networks can be substantially d...
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03/30/2016
