A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Theoretical study shows AI models can recover from contaminated training data.
In this paper, we consider the problem of machine teaching, the inverse problem of machine learning. Different from traditional machine teaching which views the learners as batch algorithms, we study a new paradigm where the learner uses an iterative algorithm and a teacher can feed examples sequentially and intelligen…
Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe. Rapidly advancing vehicular communication and edge cloud computation technologies provide key enablers for smart traffic …
POCAII optimizes hyperparameters with a new approach, showing superior performance.
Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows -- by modifying the data pre-processing, model training, and post-processing steps -- via trial-and-error to achieve the desired model performance. Existing work on accelerating machine learni…
SUMER updates deployed models with new data, improving performance.
Theoretical model for iterative user discovery in recommender systems.
RE enhances DL by learning model behavior, enabling iterative self-improvement.
IGANI uses iterative GANs to improve traffic data imputation.
Crowdsourcing platforms emerged as popular venues for purchasing human intelligence at low cost for large volume of tasks. As many low-paid workers are prone to give noisy answers, a common practice is to add redundancy by assigning multiple workers to each task and then simply average out these answers. However, to fu…
A new method uses heat diffusion to efficiently solve combinatorial optimization problems.
Traditionally, most complex intelligence architectures are extremely non-convex, which could not be well performed by convex optimization. However, this paper decomposes complex structures into three types of nodes: operators, algorithms and functions. Iteratively, propagating from node to node along edge, we prove tha…
The paper addresses human-like decision-making in multi-agent systems using bounded risk-sensitive Markov Games.
Paper presents a new training method for overparametrized neural networks that reduces time per iteration.
AI learns market manipulation through simulation, suggesting regulation.
Computational swarm intelligence consists of multiple artificial simple agents exchanging information while exploring a search space. Despite a rich literature in the field, with works improving old approaches and proposing new ones, the mechanism by which complex behavior emerges in these systems is still not well und…
Dynamic sample pruning speeds up spatio-temporal forecasting models.
Mathematical framework using Riemannian geometry for intelligence and consciousness.
Artificial intelligence has impacted many aspects of human life. This paper studies the impact of artificial intelligence on economic theory. In particular we study the impact of artificial intelligence on the theory of bounded rationality, efficient market hypothesis and prospect theory.
This work improves the convergence theory of diffusion models for generating samples from complex distributions.
BED-LLM uses Bayesian experimental design to improve LLMs' information gathering.
This thesis explores emergent intelligence in disordered systems like spin glasses and neural networks.
Self-play fine-tuning improves diffusion models for text-to-image generation.
SIM-Shapley improves SV approximation efficiency and stability.
This brief note highlights some basic concepts required toward understanding the evolution of machine learning and deep learning models. The note starts with an overview of artificial intelligence and its relationship to biological neuron that ultimately led to the evolution of todays intelligent models.
Quickly adapts fault diagnosis models for industrial machines.
As systems are getting more autonomous with the development of artificial intelligence, it is important to discover the causal knowledge from observational sensory inputs. By encoding a series of cause-effect relations between events, causal networks can facilitate the prediction of effects from a given action and anal…
Joint replacement is the most common inpatient surgical treatment in the US. We investigate the clinical pathway optimization for knee replacement, which is a sequential decision process from onset to recovery. Based on episodic claims from previous cases, we view the pathway optimization as an intelligence crowdsourci…
MixBoost generates synthetic instances to balance imbalanced datasets.
Preventing organizations from Cyber exploits needs timely intelligence about Cyber vulnerabilities and attacks, referred as threats. Cyber threat intelligence can be extracted from various sources including social media platforms where users publish the threat information in real time. Gathering Cyber threat intelligen…
Survey on AI math foundations, focusing on neural networks.
This research improves online learning by correcting for target shift in machine learning.
We discuss the objectives of automation equipped with non-trivial decision making, or creating artificial intelligence, in the financial markets and provide a possible alternative. Intelligence might be an unintended consequence of curiosity left to roam free, best exemplified by a frolicking infant. For this unintenti…
Accurate time series prediction over long future horizons is challenging and of great interest to both practitioners and academics. As a well-known intelligent algorithm, the standard formulation of Support Vector Regression (SVR) could be taken for multi-step-ahead time series prediction, only relying either on iterat…
The use of machine learning and intelligent systems has become an established practice in the realm of malware detection and cyber threat prevention. In an environment characterized by widespread accessibility and big data, the feasibility of malware classification without the use of artificial intelligence-based techn…
Context-awareness in smart mobile applications is a growing area of study, because of it's intelligence in the applications. In order to build context-aware intelligent applications, mining contextual behavioral rules of individual smartphone users utilizing their phone log data is the key. However, to mine these rules…
Intelligence emerges from stabilizing invariant cycles in memory.
Stochasticity is key for machine learning's robustness and generalizability.
This survey presents a review of state-of-the-art deep neural network architectures, algorithms, and systems in vision and speech applications. Recent advances in deep artificial neural network algorithms and architectures have spurred rapid innovation and development of intelligent vision and speech systems. With avai…
This paper tackles URLLC in 6G networks with deep learning.
Chess engines Stockfish and LCZero differ in their approach to solving endgame puzzles.
We are working to develop automated intelligent agents, which can act and react as learning machines with minimal human intervention. To accomplish this, an intelligent agent is viewed as a question-asking machine, which is designed by coupling the processes of inference and inquiry to form a model-based learning unit.…
Future autonomous systems need reliable world models and complex action sequences.
Symmetry principles help in creating better AI representations.
Transportation and traffic are currently undergoing a rapid increase in terms of both scale and complexity. At the same time, an increasing share of traffic participants are being transformed into agents driven or supported by artificial intelligence resulting in mixed-intelligence traffic. This work explores the impli…
The paper proposes an AI and IIoT framework for improved maintenance.
The paper shows how uncertainty quantification improves counterfactual explainability in AI.
Framework predicts melt pool geometry with AI, improving manufacturing quality.