118 research outputs found
Fixed-point Factorized Networks
In recent years, Deep Neural Networks (DNN) based methods have achieved
remarkable performance in a wide range of tasks and have been among the most
powerful and widely used techniques in computer vision. However, DNN-based
methods are both computational-intensive and resource-consuming, which hinders
the application of these methods on embedded systems like smart phones. To
alleviate this problem, we introduce a novel Fixed-point Factorized Networks
(FFN) for pretrained models to reduce the computational complexity as well as
the storage requirement of networks. The resulting networks have only weights
of -1, 0 and 1, which significantly eliminates the most resource-consuming
multiply-accumulate operations (MACs). Extensive experiments on large-scale
ImageNet classification task show the proposed FFN only requires one-thousandth
of multiply operations with comparable accuracy
Estimating Marginal Hazard Ratios by Simultaneously Using A Set of Propensity Score Models: A Multiply Robust Approach
The inverse probability weighted Cox model is frequently used to estimate marginal hazard ratios. Its validity requires a crucial condition that the propensity score model is correctly specified. To provide protection against misspecification of the propensity score model, we propose a weighted estimation method rooted in empirical likelihood theory. The proposed estimator is multiply robust in that it is guaranteed to be consistent when a set of postulated propensity score models contains a correctly specified model. Our simulation studies demonstrate satisfactory finite sample performance of the proposed method in terms of consistency and efficiency. We apply the proposed method to compare the risk of postoperative hospitalization between sleeve gastrectomy and Roux-en-Y gastric bypass using data from a large medical claims and billing database.We further extend the development to multi-site studies to enable each site to postulate multiple site-specific propensity score models
Total thyroidectomy may be more reasonable as initial surgery in unilateral multifocal papillary thyroid microcarcinoma: a single-center experience
The ethics statement of our study by the Ethics Committee of Jilin University affiliated First Hospital. (DOC 58 kb
Feature Distilled Tracking
Feature extraction and representation is one of the most important components for fast, accurate, and robust visual tracking. Very deep convolutional neural networks (CNNs) provide effective tools for feature extraction with good generalization ability. However, extracting features using very deep CNN models needs high performance hardware due to its large computation complexity, which prohibits its extensions in real-time applications. To alleviate this problem, we aim at obtaining small and fast-to-execute shallow models based on model compression for visual tracking. Specifically, we propose a small feature distilled network (FDN) for tracking by imitating the intermediate representations of a much deeper network. The FDN extracts rich visual features with higher speed than the original deeper network. To further speed-up, we introduce a shift-and-stitch method to reduce the arithmetic operations, while preserving the spatial resolution of the distilled feature maps unchanged. Finally, a scale adaptive discriminative correlation filter is learned on the distilled feature for visual tracking to handle scale variation of the target. Comprehensive experimental results on object tracking benchmark datasets show that the proposed approach achieves 5x speed-up with competitive performance to the state-of-the-art deep trackers
Portal Vein Thrombosis in Liver Cirrhosis
In liver cirrhosis, portal vein thrombosis (PVT), which is defined as thrombosis that occurs within the main portal vein and intrahepatic portal branches, is one of the most common complications. High incidence of PVT in the setting of liver cirrhosis is mainly due to hypercoagulable state and altered dynamic of blood flow in the portal vein. The clinical manifestations of PVT are variable among different patients, so the diagnosis of PVT is mainly dependent on the imaging examinations, like ultrasound, computed tomography and magnetic resonance imaging. The overall goal of treatment for PVT can be summarized as reducing risk factors of PVT, thus to prevent further expansion of thrombus and maintain portal patency and prevent and treat the symptoms of PVT by anticoagulants, local thrombolysis, transjugular intrahepatic portosystemic shunt and/or surgery. In future, due to the progress in vascular imaging and innovation in clinical anti-thrombotic drug, PVT could be prevented and cured effectively
One Fits All:Power General Time Series Analysis by Pretrained LM
Although we have witnessed great success of pre-trained models in natural
language processing (NLP) and computer vision (CV), limited progress has been
made for general time series analysis. Unlike NLP and CV where a unified model
can be used to perform different tasks, specially designed approach still
dominates in each time series analysis task such as classification, anomaly
detection, forecasting, and few-shot learning. The main challenge that blocks
the development of pre-trained model for time series analysis is the lack of a
large amount of data for training. In this work, we address this challenge by
leveraging language or CV models, pre-trained from billions of tokens, for time
series analysis. Specifically, we refrain from altering the self-attention and
feedforward layers of the residual blocks in the pre-trained language or image
model. This model, known as the Frozen Pretrained Transformer (FPT), is
evaluated through fine-tuning on all major types of tasks involving time
series. Our results demonstrate that pre-trained models on natural language or
images can lead to a comparable or state-of-the-art performance in all main
time series analysis tasks, as illustrated in Figure 1. We also found both
theoretically and empirically that the self-attention module behaviors
similarly to principle component analysis (PCA), an observation that helps
explains how transformer bridges the domain gap and a crucial step towards
understanding the universality of a pre-trained transformer.The code is
publicly available at https://github.com/DAMO-DI-ML/One_Fits_All.Comment: Neurips 2023 Spotligh
One Fits All: Universal Time Series Analysis by Pretrained LM and Specially Designed Adaptors
Despite the impressive achievements of pre-trained models in the fields of
natural language processing (NLP) and computer vision (CV), progress in the
domain of time series analysis has been limited. In contrast to NLP and CV,
where a single model can handle various tasks, time series analysis still
relies heavily on task-specific methods for activities such as classification,
anomaly detection, forecasting, and few-shot learning. The primary obstacle to
developing a pre-trained model for time series analysis is the scarcity of
sufficient training data. In our research, we overcome this obstacle by
utilizing pre-trained models from language or CV, which have been trained on
billions of data points, and apply them to time series analysis. We assess the
effectiveness of the pre-trained transformer model in two ways. Initially, we
maintain the original structure of the self-attention and feedforward layers in
the residual blocks of the pre-trained language or image model, using the
Frozen Pre-trained Transformer (FPT) for time series analysis with the addition
of projection matrices for input and output. Additionally, we introduce four
unique adapters, designed specifically for downstream tasks based on the
pre-trained model, including forecasting and anomaly detection. These adapters
are further enhanced with efficient parameter tuning, resulting in superior
performance compared to all state-of-the-art methods.Our comprehensive
experimental studies reveal that (a) the simple FPT achieves top-tier
performance across various time series analysis tasks; and (b) fine-tuning the
FPT with the custom-designed adapters can further elevate its performance,
outshining specialized task-specific models.Comment: this article draws heavily from arXiv:2302.1193
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