Paper proposes AI solutions for social problems in low-income areas.
problem Effective preventive measures against complex social problems in low-income regions.
method Design framework using data-driven machine learning for adaptive solutions.
result Illustrated design approach with survey data from India.
Deep-learning CNN automates Cu alloy grain size evaluation.
problem Automating the evaluation of Cu alloy grain size to reduce labor and improve accuracy.
method Deep-learning convolutional neural network (CNN) for automated image acquisition and processing.
result Achieved 91.1% classification accuracy on Cu alloy sub-images.
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.…
Study identifies what healthcare tasks can and should be automated.
problem Undesired consequences of automation in healthcare.
method Quantitative analysis using machine learning models trained on practitioner and expert ratings.
result Developed an analytical tool (Automatability-Desirability Matrix) for policymakers and leaders.
The paper proposes a test for trading intelligence similar to the Turing Test.
problem Determining if trading programs exhibit intelligent behavior.
method Develops a test based on the Turing Test for financial trading programs.
result Introduces a method to assess trading intelligence in financial markets.
AI helps in drug discovery with understandable explanations.
problem Understanding the complex models behind AI-generated drugs.
method Explainable AI methods to interpret deep learning models.
result Improved interpretability of AI-generated drug properties.
Future autonomous systems need reliable world models and complex action sequences.
problem Current automated systems lack reliable world models and complex action sequences.
method Introduce energy-based and latent variable models combined in a hierarchical joint embedding predictive architecture (H-JEPA).
result Combining energy-based and latent variable models in H-JEPA can lead to reliable world models and complex action sequences.
ADS automates data preparation for ML/AI, reducing human effort.
problem Manual and time-consuming data preparation for ML/AI.
method Data-driven approach using statistics and ML.
result ADS automates data exploration and processing steps.
AutoAIViz visualizes AutoAI's model generation process, improving user trust.
problem Limited transparency in AutoAI systems leads to lack of user understanding and trust.
method Developed and evaluated AutoAIViz, an experimental system that visualizes AutoAI's model generation process.
result AutoAIViz helps users complete data science tasks and increases their understanding, improving trust in AutoAI systems.
Research designs an AI system to classify malware under adversarial conditions.
problem Adversarial attacks on malware classification algorithms.
method Machine learning-based intelligent systems approach.
result Robust malware classification model under adversarial conditions.
Painless Activation Steering automates post-training for LMs without manual intervention.
problem Manual post-training methods are time-consuming and labor-intensive.
method Painless Activation Steering (PAS) is a fully automated approach that requires no manual intervention.
result PAS reliably improves performance for behavior tasks but not for intelligence-oriented tasks.
Seq2Seq models perform well in generating If-Then programs from natural language.
problem Creating If-Then programs for business process automation without technical expertise.
method Modeling If-Then programs as a sequence learning task using Seq2Seq approaches.
result Seq2Seq models can effectively generate If-Then programs from natural language.
Survey on automating geometry problem solving with large models.
problem Automating geometric problem solving with spatial understanding and logical reasoning.
method Synthesizes GPS advancements through benchmark construction, parsing, and reasoning paradigms.
result Unified analytical paradigm and emerging opportunities identified.
Automated VOC classification using embedded machine learning.
problem Classifying volatile organic compounds (VOCs) from electronic nose data.
method Embedded machine learning algorithm on Arduino Uno for VOC detection.
result 99% classification accuracy achieved in embedded solution.
Paper proposes verifier engineering for improving foundation models.
problem Challenges in providing effective supervision signals for foundation models.
method Leverages automated verifiers to perform verification tasks and deliver feedback.
result Verifier engineering can enhance foundation models' capabilities.
AutoML enhances credit decisions with XAI for better transparency.
problem Transparency in AI-driven financial decisions.
method Combining AutoML and XAI (SHAP) for credit scoring.
result Improved efficiency and accuracy in credit decisions with enhanced transparency.
Generative AI agents improve ERP systems by automating complex financial tasks.
problem Static, rule-based workflows limit adaptability and intelligence in ERP systems.
method Introducing Generative Business Process AI Agents (GBPAs) that integrate generative AI with business process modeling and multi-agent orchestration.
result GBPAs achieve up to 40% reduction in processing time and 94% drop in error rate.
Automated meta-learning improves model performance on small datasets.
problem Improving machine learning model performance on limited data.
method Gradient-based meta-learning combined with automated neural architecture search.
result Automatically found meta-learner achieved 74.65% accuracy on 5-shot 5-way Mini-ImageNet, 11.54% better than MAML.
DBOT uses AI to automate long-term stock valuation.
problem Automating long-term stock valuation using AI.
method DBOT uses generative AI to reason about company valuations.
result DBOT can value any publicly traded company and is comparable to Aswath Damodaran.
Researchers develop PAIN to improve self-driving safety through adversarial training.
problem Overfitting and poor generalizability of neural networks in self-driving vehicles.
method PAIN combines adversarial training in CARLA simulation to generate edge cases.
result Trained self-driving vehicles are more resilient to environmental uncertainty and less prone to collisions.
Survey of 1000 NAS papers, automating neural architecture design.
problem Designing high-performing neural architectures for various tasks.
method Taxonomy of search spaces, algorithms, and speedup techniques.
result NAS has surpassed human-designed architectures on many tasks.
This paper proposes AI-based solutions for optimizing semiconductor manufacturing processes.
problem Optimizing semiconductor manufacturing processes with advanced analytics.
method Evolutionary Computing and Deep Learning algorithms for feature selection and neural networks.
result Advanced algorithm for intelligent feature selection in semiconductor manufacturing.
Paper explores AIaaS on SDI, offering services in smart sectors.
problem Complexity of infrastructures driving AIaaS.
method Proposes architectural scheme based on SDIs with AI-aaS applications.
result Experimental results for three AI-aaS applications.
This paper improves transportation efficiency by teaching automated vehicles to cooperate.
problem Improving efficiency and safety of transportation systems with automated vehicles.
method Multi-agent graph reinforcement learning with attention mechanism.
result Automated vehicles can achieve better performance when learning to cooperate with each other.
Automated model predicts C. elegans escape behavior.
problem Complex escape behavior of C. elegans due to rising temperature.
method Phenomenological modeling using Sir Isaac dynamical inference platform.
result Inferred model accurately predicts worm behavior and is biologically interpretable.
CAI automates extraction and validation of corporate GHG emission metrics.
problem Manual extraction of corporate GHG emission metrics is labor-intensive and error-prone.
method CAI uses LLMs to automate extraction and validation of metrics from corporate disclosures.
result CAI improves data collection efficiency and accuracy by automating the process.
A learning-based approach optimizes automated lane changes with mixed-integer optimization and machine learning.
problem Traditional motion planning methods are inefficient and lack generalization capability.
method Mixed-Integer Quadratic Problem (MIQP) for optimal trajectories, supervised learning for fast decision-making.
result The proposed model outperforms existing motion planning methods in optimality, efficiency, and generalization.
AI helps simplify complex ship finance processes.
problem Complexity in ship finance due to data and regulatory requirements.
method Integrates large language models for document comprehension, information extraction, and workflow automation.
result AI-assisted systems can support maritime finance professionals in managing complex information and reporting requirements.
VERAFI improves financial AI by verifying calculations and compliance.
problem Financial AI systems generate errors and violations during reasoning.
method VERAFI combines dense retrieval, reranking, and automated reasoning policies.
result VERAFI achieves 94.7% factual correctness, 81% relative improvement.
Mango automates hyperparameter tuning for large-scale ML training.
problem Manual hyperparameter tuning is tedious and inefficient for large-scale machine learning.
method Parallel hyperparameter tuning with intelligent search strategies and flexible abstractions.
result Mango achieves comparable performance to Hyperopt while supporting distributed computing.
RAG-IT automates financial analysis using LLMs and specialized datasets.
problem Manual financial analysis is time-consuming and requires expertise.
method Retrieval-Augmented Instruction Tuning (RAG-IT) fine-tunes an LLM for financial tasks.
result RAG-IT improves financial report generation performance compared to commercial systems.
Survey of techniques for automating hyperparameter optimization in machine learning.
problem Finding optimal hyperparameters for machine learning algorithms.
method Review of various hyperparameter optimization techniques.
result Unified treatment of hyperparameter optimization techniques.
Deep learning improves automated detection of epileptic seizures.
problem Automated detection of epileptic seizures using traditional methods is limited.
method Deep learning techniques for feature extraction and classification.
result Deep learning enhances accuracy in diagnosing epileptic seizures.
This thesis aims to automate deep neural network design for efficiency and complexity reduction.
problem Manual design of deep neural networks is inefficient and complex.
method Examines and proposes automated approaches to neural network design.
result Creation of less complex models with good performance through automation.
DRL automates stock market trading with a 2.68 Sharpe Ratio.
problem Automating profitable trades in the stock market.
method Formulated as a POMDP, solved with TD3 algorithm.
result 2.68 Sharpe Ratio on unseen data.
AI framework for automated policy-making connects with econometrics and social choice.
problem Improving government and economic policy-making through AI.
method Social Environment Design framework integrating Reinforcement Learning, EconCS, and Computational Social Choice.
result Promotes ethical and responsible decision making by solving open problems.
FinTech uses data science and AI to transform finance.
problem Transforming finance with data science and AI.
method DSAI techniques including complex system methods, quantitative methods, etc.
result DSAI enables smart FinTech for various financial sectors.
Machine learning identifies physical laws from data, but discrepancies remain.
problem Discovering universal physical laws from data alone is challenging.
method Sparse Identification of Nonlinear Dynamics (SINDy) method to identify governing equations.
result Measurement noise and secondary physical mechanisms obscure the underlying law of gravitation.
AI uses statistical PQRS workflow for human-machine collaboration.
problem Integrating AI with statistical principles for better results.
method PQRS workflow integrating statistical concepts with AI.
result Reproducibility and interpretability of AI algorithms and results.
Deep Reinforcement Learning automates match-3 game testing.
problem Reducing human effort in testing match-3 video games.
method Dueling Deep Q-Network paradigm applied to Jelly Juice game.
result The network outperforms random player and adapts to game difficulty.
Deep learning improves material recognition in construction monitoring.
problem Varying illuminations and low accuracy rates in material classification.
method Deep learning methods, including convolutional neural networks, were used to classify and recognize materials in construction sites.
result Achieved 97.35% accuracy rate for material classification.
Quant 4.0 uses AI to automate, explain, and incorporate knowledge in investment.
problem Limitations of deep learning in quant investment.
method Automated AI, Explainable AI, Knowledge-driven AI.
result Improves investment decision-making through automation, interpretability, and prior knowledge integration.
Gait patterns reveal emotions, offering a non-invasive method for automated recognition.
problem Automated emotion recognition from gait patterns.
method Data collection, preprocessing, and classification techniques.
result Gait patterns can indicate different emotion states, making them a promising source for emotion detection.
QTNet uses deep reinforcement learning to automate trading strategies.
problem Handling noisy and high-frequency financial data, balancing exploration and exploitation.
method QTNet employs deep reinforcement learning (DRL) with imitative learning to autonomously formulate trading strategies.
result QTNet demonstrates proficiency in extracting robust market features and adaptability to diverse conditions.
Survey of AI in quant finance, from deep learning to LLMs.
problem Improving predictive modeling and automation in asset management.
method Exploring AI contributions to quant investment pipeline, from human-crafted features to LLMs.
result AI has enabled scalable modeling and autonomous agents in quant finance.
Deep learning improves jet discrimination in particle physics.
problem Automating quark/gluon jet discrimination in collider physics.
method Convolutional neural networks trained on color-enhanced jet images.
result Deep networks outperform traditional jet variables in discrimination.
Deep learning aids in autism diagnosis and rehabilitation using neuroimaging data.
problem Challenges in automated detection and rehabilitation of ASD using neuroimaging data.
method Deep learning techniques applied to neuroimaging data for ASD diagnosis and rehabilitation.
result Deep learning improves accuracy in ASD diagnosis and rehabilitation.
New analysis reveals diverse problem-solving behaviors in machine learning models.
problem Understanding and evaluating the diverse problem-solving behaviors of machine learning models.
method Spectral Relevance Analysis to characterize and validate machine learning models.
result Standard performance metrics fail to distinguish diverse problem-solving behaviors.