12 research outputs found
Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks
Representation learning methods have revolutionized machine learning on
networks by converting discrete network structures into continuous domains.
However, dynamic networks that evolve over time pose new challenges. To address
this, dynamic representation learning methods have gained attention, offering
benefits like reduced learning time and improved accuracy by utilizing temporal
information.
T-batching is a valuable technique for training dynamic network models that
reduces training time while preserving vital conditions for accurate modeling.
However, we have identified a limitation in the training loss function used
with t-batching. Through mathematical analysis, we propose two alternative loss
functions that overcome these issues, resulting in enhanced training
performance.
We extensively evaluate the proposed loss functions on synthetic and
real-world dynamic networks. The results consistently demonstrate superior
performance compared to the original loss function. Notably, in a real-world
network characterized by diverse user interaction histories, the proposed loss
functions achieved more than 26.9% enhancement in Mean Reciprocal Rank (MRR)
and more than 11.8% improvement in Recall@10. These findings underscore the
efficacy of the proposed loss functions in dynamic network modeling.Comment: 29 pages, 10 figures, 4 tables, Submitted to Information Science
Predicting Subjective Features from Questions on QA Websites using BERT
Community Question-Answering websites, such as StackOverflow and Quora,
expect users to follow specific guidelines in order to maintain content
quality. These systems mainly rely on community reports for assessing contents,
which has serious problems such as the slow handling of violations, the loss of
normal and experienced users' time, the low quality of some reports, and
discouraging feedback to new users. Therefore, with the overall goal of
providing solutions for automating moderation actions in Q&A websites, we aim
to provide a model to predict 20 quality or subjective aspects of questions in
QA websites. To this end, we used data gathered by the CrowdSource team at
Google Research in 2019 and a fine-tuned pre-trained BERT model on our problem.
Based on the evaluation by Mean-Squared-Error (MSE), the model achieved a value
of 0.046 after 2 epochs of training, which did not improve substantially in the
next ones. Results confirm that by simple fine-tuning, we can achieve accurate
models in little time and on less amount of data.Comment: 5 pages, 4 figures, 2 table
On a Bounded Budget Network Creation Game
We consider a network creation game in which each player (vertex) has a fixed
budget to establish links to other players. In our model, each link has unit
price and each agent tries to minimize its cost, which is either its local
diameter or its total distance to other players in the (undirected) underlying
graph of the created network. Two versions of the game are studied: in the MAX
version, the cost incurred to a vertex is the maximum distance between the
vertex and other vertices, and in the SUM version, the cost incurred to a
vertex is the sum of distances between the vertex and other vertices. We prove
that in both versions pure Nash equilibria exist, but the problem of finding
the best response of a vertex is NP-hard. We take the social cost of the
created network to be its diameter, and next we study the maximum possible
diameter of an equilibrium graph with n vertices in various cases. When the sum
of players' budgets is n-1, the equilibrium graphs are always trees, and we
prove that their maximum diameter is Theta(n) and Theta(log n) in MAX and SUM
versions, respectively. When each vertex has unit budget (i.e. can establish
link to just one vertex), the diameter of any equilibrium graph in either
version is Theta(1). We give examples of equilibrium graphs in the MAX version,
such that all vertices have positive budgets and yet the diameter is
Omega(sqrt(log n)). This interesting (and perhaps counter-intuitive) result
shows that increasing the budgets may increase the diameter of equilibrium
graphs and hence deteriorate the network structure. Then we prove that every
equilibrium graph in the SUM version has diameter 2^O(sqrt(log n)). Finally, we
show that if the budget of each player is at least k, then every equilibrium
graph in the SUM version is k-connected or has diameter smaller than 4.Comment: 28 pages, 3 figures, preliminary version appeared in SPAA'1
Machine Learning Methods in Algorithmic Trading: An Experimental Evaluation of Supervised Learning Techniques for Stock Price
In the dynamic world of financial markets, accurate price predictions are essential for informed decision-making. This research proposal outlines a comprehensive study aimed at forecasting stock and currency prices using state-of-the-art Machine Learning (ML) techniques. By delving into the intricacies of models such as Transformers, LSTM, Sim- ple RNN, NHits, and NBeats, we seek to contribute to the realm of financial forecasting, offering valuable insights for investors, financial analysts, and researchers. This article provides an in-depth overview of our methodology, data collection process, model im- plementations, evaluation metrics, and potential applications of our research findings. The research indicates that NBeats and NHits models exhibit superior performance in financial forecasting tasks, especially with limited data, while Transformers require more data to reach full potential. Our findings offer insights into the strengths of different ML techniques for financial prediction, highlighting specialized models like NBeats and NHits as top performers - thus informing model selection for real-world applications