arXiv research

New approach for large-scale distributed learning systems that improve generalization performance.

FinGPT democratizes financial data for LLMs, enabling innovation.

Special p-forms are forms which have components φ_{μ_1...μ_p} equal to +1,-1 or 0 in some orthonormal basis. A p-form φ\in Λ^p R^d is called democratic if the set of nonzero components {φ_{μ_1...μ_p}} is symmetric under the transitive action of a subgroup of O(d,Z) on the indices {1,...,d}. Knowledge of these symmetry …

Machine Learning and Artificial Intelligence are considered an integral part of the Fourth Industrial Revolution. Their impact, and far-reaching consequences, while acknowledged, are yet to be comprehended. These technologies are very specialized, and few organizations and select highly trained professionals have the w…

The democratization of machine learning (ML) has led to ML-based machine vision systems for autonomous driving, traffic monitoring, and video surveillance. However, true democratization cannot be achieved without greatly simplifying the process of collecting groundtruth for training and testing these systems. This grou…

FinGPT is an open-source financial LLM for democratizing financial data.

Paper proposes a new AI design philosophy for distributed learning.

The paper examines how algorithmic classification affects behavior and proposes democratizing stakes to mitigate predatory practices.

AI could democratize education but risks inequality.

LLMs help less-resourced researchers access costly data.

New method reveals true causal functions in nonlinear time series, not just scores.

The hyperbolic structure of equilateral pentagons is mapped to a tiling of the hyperbolic plane.

Proposes a transfer learning framework to improve U.S. election prediction models.

Scikit-multiflow is a multi-output/multi-label and stream data mining framework for the Python programming language. Conceived to serve as a platform to encourage democratization of stream learning research, it provides multiple state of the art methods for stream learning, stream generators and evaluators. scikit-mult…

Anti-ELAB protests affected Hong Kong firms' stock prices, especially those linked to pan-democrats.

Reincarnating RL reuses prior work to accelerate RL progress.

LLM4Causal democratizes causal reasoning via fine-tuned LLMs.

Paper proposes forecast-necessity testing for accurate causal interpretation in nonlinear time-series models.

Study shows how high-budget agents can manipulate prediction markets.

Object detection is a computer vision field that has applications in several contexts ranging from biomedicine and agriculture to security. In the last years, several deep learning techniques have greatly improved object detection models. Among those techniques, we can highlight the YOLO approach, that allows the const…

Single neural network predicts ImageNet model parameters for faster training.

We explain the meaning of local symmetries in physics.

Developing active inference agents for edge devices with limited resources.

In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code. Ludwig implements a novel approach to deep learning model building based on two main abstractions: data types and declarative confi…

A new cost-frugal HPO method controls training cost during optimization.

FinRobot opens-source AI for financial tasks, breaking down complex problems.

Amharic is the official language of the Federal Democratic Republic of Ethiopia. There are lots of historic Amharic and Ethiopic handwritten documents addressing various relevant issues including governance, science, religious, social rules, cultures and art works which are very reach indigenous knowledge. The Amharic …

Paper examines how adversarial ML attacks and defenses impact civil liberties and human rights.

Creating efficient deep neural networks involves repetitive manual optimization of the topology and the hyperparameters. This human intervention significantly inhibits the process. Recent publications propose various Neural Architecture Search (NAS) algorithms that automate this work. We have applied a customized NAS a…

Improved memory efficiency for deep neural networks.

Amazon SageMaker AMT automates machine learning model tuning.

Financial system being the place of metting capital flows (equality between saving and investment), a volatility of capital flows can destroy the robustness and good working of financial system, it means subvert financial stability. The same a weak financial system, few regulated and bad manage can exacerbate volatilit…

HotNAS reduces AI search time from hundreds of GPU hours to less than 3 GPU hours.

Deep learning has recently seen rapid development and received significant attention due to its state-of-the-art performance on previously-thought hard problems. However, because of the internal complexity and nonlinear structure of deep neural networks, the underlying decision making processes for why these models are…

A new CNN approach for time series forecasting outperforms traditional RNNs.

While the Machine Learning (ML) landscape is evolving rapidly, there has been a relative lag in the development of the "learning systems" needed to enable broad adoption. Furthermore, few such systems are designed to support the specialized requirements of scientific ML. Here we present the Data and Learning Hub for sc…

Conversion of raw data into insights and knowledge requires substantial amounts of effort from data scientists. Despite breathtaking advances in Machine Learning (ML) and Artificial Intelligence (AI), data scientists still spend the majority of their effort in understanding and then preparing the raw data for ML/AI. Th…

Single model learns physics from diverse data.

General equilibrium equations in economics play the same role with many-body Newtonian equations in physics. Accordingly, each solution of the general equilibrium equations can be regarded as a possible microstate of the economic system. Since Arrow's Impossibility Theorem and Rawls' principle of social fairness will p…

LeanML reduces machine learning project waste by estimating best performance without training models.

A scalable system detects price anomalies in online marketplaces to improve customer experience.

TBIP uses texts to quantify lawmakers' political positions.

SmallML predicts customer churn for SMEs with small data, improving accuracy by 24.2 points.

During the Great Recession, Democrats in the United States argued that government spending could be utilized to "grease the wheels" of the economy in order to create wealth and to increase employment; Republicans, on the other hand, contended that government spending is wasteful and discouraged investment, thereby incr…

Using the new data from the OECD-WTO world network of economic activities we construct the Google matrix $G$ of this directed network and perform its detailed analysis. The network contains 58 countries and 37 activity sectors for years 1995 and 2008. The construction of $G$, based on Markov chain transitions, treats a…

Non-experts have long made important contributions to machine learning (ML) by contributing training data, and recent work has shown that non-experts can also help with feature engineering by suggesting novel predictive features. However, non-experts have only contributed features to prediction tasks already posed by e…

Efficient search methods can outperform random search on challenging tasks.

FairLangProc simplifies fairness in NLP models for Python users.