As their size increases, Large Languages Models (LLMs) are natural candi...
We present Generalized LoRA (GLoRA), an advanced approach for universal
...
We present ImageBind, an approach to learn a joint embedding across six
...
Treatment planning for chronic diseases is a critical task in medical
ar...
Introduced by Hinton et al. in 2012, dropout has stood the test of time ...
Driven by improved architectures and better representation learning
fram...
Incorrectly sized balloon catheters can lead to increased post-surgical
...
The "Roaring 20s" of visual recognition began with the introduction of V...
This paper explores the feasibility of finding an optimal sub-model from...
We present MSeg, a composite dataset that unifies semantic segmentation
...
Anytime inference requires a model to make a progression of predictions ...
Recommender systems, which analyze users' preference patterns to suggest...
In supervised learning, smoothing label/prediction distribution in neura...
Meta-learning has become a popular framework for few-shot learning in re...
Recent work has shown that convolutional networks can be substantially
d...
Automatically extracting Protein-Protein Interactions (PPI) from biomedi...
Chemical-disease relation (CDR) extraction is significantly important to...
Automatically extracting the relationships between chemicals and disease...
Background: Automatic extraction of chemical-disease relations (CDR) fro...
Protein-protein interaction (PPI) extraction from published scientific
l...
Deep Reinforcement Learning (Deep RL) has been receiving increasingly mo...
Recent progress in image recognition has stimulated the deployment of vi...
We introduce a general approach, called test-time training, for improvin...
This work aims to solve the challenging few-shot object detection proble...
Current knowledge distillation methods require full training data to dis...
Network pruning is widely used for reducing the heavy computational cost...
We propose Deeply Supervised Object Detectors (DSOD), an object detectio...
The deployment of deep convolutional neural networks (CNNs) in many real...
We present Deeply Supervised Object Detector (DSOD), a framework that ca...
Recent work has shown that convolutional networks can be substantially
d...
Very deep convolutional networks with hundreds of layers have led to
sig...