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The Loss Surface of XOR Artificial Neural Networks
Training an artificial neural network involves an optimization process over the landscape defined by the cost (loss) as a function of the network parameters.
Fixed Points of Belief Propagation -- An Analysis via Polynomial Homotopy Continuation
Moreover, we show that this fixed point gives a good approximation, and the NPHC method is able to obtain this fixed point.
Perspective: Energy Landscapes for Machine Learning
Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences.
A Collection of Challenging Optimization Problems in Science, Engineering and Economics
Function optimization and finding simultaneous solutions of a system of nonlinear equations (SNE) are two closely related and important optimization problems.
The loss surface of deep linear networks viewed through the algebraic geometry lens
By using the viewpoint of modern computational algebraic geometry, we explore properties of the optimization landscapes of the deep linear neural network models.
Towards Robust Deep Neural Networks
We investigate the topics of sensitivity and robustness in feedforward and convolutional neural networks.
Machine Learning Fund Categorizations
Given the surge in popularity of mutual funds (including exchange-traded funds (ETFs)) as a diversified financial investment, a vast variety of mutual funds from various investment management firms and diversification strategies have become available in the market.
Machine learning the real discriminant locus
This article views locating the real discriminant locus as a supervised classification problem in machine learning where the goal is to determine classification boundaries over the parameter space, with the classes being the number of real solutions.
Fund2Vec: Mutual Funds Similarity using Graph Learning
Identifying similar mutual funds with respect to the underlying portfolios has found many applications in financial services ranging from fund recommender systems, competitors analysis, portfolio analytics, marketing and sales, etc.
Investor Behavior Modeling by Analyzing Financial Advisor Notes: A Machine Learning Perspective
With the help of natural language processing (NLP) we analyze an unstructured (textual) dataset of financial advisors' summary notes, taken after every investor conversation, to gain first ever insights into advisor-investor interactions.
Learning Mutual Fund Categorization using Natural Language Processing
Categorization of mutual funds or Exchange-Traded-funds (ETFs) have long served the financial analysts to perform peer analysis for various purposes starting from competitor analysis, to quantifying portfolio diversification.
Supervised similarity learning for corporate bonds using Random Forest proximities
Financial literature consists of ample research on similarity and comparison of financial assets and securities such as stocks, bonds, mutual funds, etc.
Learning Embedded Representation of the Stock Correlation Matrix using Graph Machine Learning
In particular, the algorithm compresses the network into a lower dimensional continuous space, called an embedding, where pairs of nodes that are identified as similar by the algorithm are placed closer to each other.
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications.
Quantifying Outlierness of Funds from their Categories using Supervised Similarity
Mutual fund categorization has become a standard tool for the investment management industry and is extensively used by allocators for portfolio construction and manager selection, as well as by fund managers for peer analysis and competitive positioning.
Company Similarity using Large Language Models
Identifying companies with similar profiles is a core task in finance with a wide range of applications in portfolio construction, asset pricing and risk attribution.
Towards reducing hallucination in extracting information from financial reports using Large Language Models
For a financial analyst, the question and answer (Q\&A) segment of the company financial report is a crucial piece of information for various analysis and investment decisions.
Towards Enhanced Local Explainability of Random Forests: a Proximity-Based Approach
We initiate a novel approach to explain the out of sample performance of random forest (RF) models by exploiting the fact that any RF can be formulated as an adaptive weighted K nearest-neighbors model.
