616 research outputs found
Sherlock: Scalable Fact Learning in Images
We study scalable and uniform understanding of facts in images. Existing
visual recognition systems are typically modeled differently for each fact type
such as objects, actions, and interactions. We propose a setting where all
these facts can be modeled simultaneously with a capacity to understand
unbounded number of facts in a structured way. The training data comes as
structured facts in images, including (1) objects (e.g., ), (3) actions (e.g., ). Each fact has a semantic
language view (e.g., ) and a visual view (an image with this
fact). We show that learning visual facts in a structured way enables not only
a uniform but also generalizable visual understanding. We propose and
investigate recent and strong approaches from the multiview learning literature
and also introduce two learning representation models as potential baselines.
We applied the investigated methods on several datasets that we augmented with
structured facts and a large scale dataset of more than 202,000 facts and
814,000 images. Our experiments show the advantage of relating facts by the
structure by the proposed models compared to the designed baselines on
bidirectional fact retrieval.Comment: Jan 7 Updat
Support Vector Regression for Non-Stationary Time Series
The difficulty associated with building forecasting models for non-stationary and volatile data has necessitated the development and application of new sophisticated techniques that can handle such data. Interestingly, there are a lot of real-world phenomena where data that are “difficult to analyze” are generated. One of these is the stock market where data series generated are often hard to forecast because of their peculiar characteristics. In particular, the stock market has been referred to as a complex environment and financial time series forecasting is often tagged as the most challenging application of time series forecasting.
In this study, a novel approach known as Support Vector Regression (SVR) for forecasting non-stationary time series was adopted and the feasibility of applying this method to five financial time series was examined. Prior to implementing the SVR algorithm, three different methods of transformation namely Relative Difference in Percentages (RDP), Z-score and Natural Logarithm transformations were applied to the data series and the best prediction results obtained along with the associated transformation technique was presented. Our study indicated that the Z-score transformation is the best scaling method for financial time series, exhibiting superior performance than the other two transformations on the basis of five different performance measures.
To determine the optimum values of the SVR parameters, a cross-validation method was implemented. For this purpose, the value of C and ε was varied from 5 to 100, and 0.001 and 0.1 respectively. The cross-validation method, though computationally expensive, is better than other proposed techniques for determining the values of these parameters.
Another highlight of this study is the comparison of the SVR results to that obtained using 5-day Simple Moving Averages (SMA). The SMA was selected as a comparative method because it has been identified as the most popular quantitative forecasting method used by US corporations. Discussions with financial analysts also suggest that the SMA is one of the widely used in the financial industry. The popularity of the SMA can be explained by the fact that it is easy and cheap to use and it produces forecasts that can be easily interpreted by econometricians and other interested practitioners
A Two-stage Architecture for Stock Price Forecasting by Integrating Self-Organizing Map and Support Vector Regression
Stock price prediction has attracted much attention from both practitioners and researchers. However, most studies in this area ignored the non-stationary nature of stock price series. That is, stock price series do not exhibit identical statistical properties at each point of time. As a result, the relationships between stock price series and their predictors are quite dynamic. It is challenging for any single artificial technique to effectively address this problematic characteristics in stock price series. One potential solution is to hybridize different artificial techniques. Towards this end, this study employs a two-stage architecture for better stock price prediction. Specifically, the self-organizing map (SOM) is first used to decompose the whole input space into regions where data points with similar statistical distributions are grouped together, so as to contain and capture the non-stationary property of financial series. After decomposing heterogeneous data points into several homogenous regions, support vector regression (SVR) is applied to forecast financial indices. The proposed technique is empirically tested using stock price series from seven major financial markets. The results show that the performance of stock price prediction can be significantly enhanced by using the two-stage architecture in comparison with a single SVR model
Private Semi-supervised Knowledge Transfer for Deep Learning from Noisy Labels
Deep learning models trained on large-scale data have achieved encouraging
performance in many real-world tasks. Meanwhile, publishing those models
trained on sensitive datasets, such as medical records, could pose serious
privacy concerns. To counter these issues, one of the current state-of-the-art
approaches is the Private Aggregation of Teacher Ensembles, or PATE, which
achieved promising results in preserving the utility of the model while
providing a strong privacy guarantee. PATE combines an ensemble of "teacher
models" trained on sensitive data and transfers the knowledge to a "student"
model through the noisy aggregation of teachers' votes for labeling unlabeled
public data which the student model will be trained on. However, the knowledge
or voted labels learned by the student are noisy due to private aggregation.
Learning directly from noisy labels can significantly impact the accuracy of
the student model.
In this paper, we propose the PATE++ mechanism, which combines the current
advanced noisy label training mechanisms with the original PATE framework to
enhance its accuracy. A novel structure of Generative Adversarial Nets (GANs)
is developed in order to integrate them effectively. In addition, we develop a
novel noisy label detection mechanism for semi-supervised model training to
further improve student model performance when training with noisy labels. We
evaluate our method on Fashion-MNIST and SVHN to show the improvements on the
original PATE on all measures
Private Set Generation with Discriminative Information
Differentially private data generation techniques have become a promising
solution to the data privacy challenge -- it enables sharing of data while
complying with rigorous privacy guarantees, which is essential for scientific
progress in sensitive domains. Unfortunately, restricted by the inherent
complexity of modeling high-dimensional distributions, existing private
generative models are struggling with the utility of synthetic samples.
In contrast to existing works that aim at fitting the complete data
distribution, we directly optimize for a small set of samples that are
representative of the distribution under the supervision of discriminative
information from downstream tasks, which is generally an easier task and more
suitable for private training. Our work provides an alternative view for
differentially private generation of high-dimensional data and introduces a
simple yet effective method that greatly improves the sample utility of
state-of-the-art approaches.Comment: NeurIPS 2022, 19 page
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