This paper optimizes AI inference on edge devices with reduced communication and computation costs.
problem Efficiently performing AI inference on resource-constrained edge devices with reduced communication and computation costs.
method A three-step framework for effective inference: model split point selection, communication-aware model compression, and task-oriented encoding of intermediate features.
result Our proposed framework achieves a better trade-off and significantly reduces inference latency compared to baseline methods.
A hybrid neural network optimizes AI deployment on edge and cloud for energy efficiency.
problem Energy and resource constraints in edge devices for deep learning models.
method Conditionally deep hybrid neural network with quantized layers at edge and full-precision layers at cloud.
result Early classification at the edge reduces energy consumption by 5.5x on CIFAR-10 dataset.
pAElla detects malware in DCs/SCs with high accuracy.
problem Real-time malware detection in high-resolution data centers.
method AI-powered edge computing with IoT-based monitoring and Power Spectral Density.
result pAElla achieves F1-score close to 1 with low false alarm and malware miss rates.
Survey examines data quality challenges in edge ML.
problem Data quality issues in edge ML due to limited resources and decentralized data.
method Provides a comprehensive survey of existing literature on data quality in edge ML.
result No comprehensive survey of data quality in edge ML exists.
Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical so…
Paper proposes FMore to incentivize edge nodes in federated learning with MEC.
problem Incentivizing edge nodes in federated learning with MEC resources.
method Multi-dimensional procurement auction with K winners.
result FMore improves model accuracy and reduces training rounds for AI tasks.
Sherpa.ai framework combines federated learning and differential privacy for edge AI services.
problem Protecting data privacy in edge AI services.
method Holistic federated learning and differential privacy approach with methodological guidelines.
result Demonstrated through classification and regression use cases.
Next generation of embedded Information and Communication Technology (ICT) systems are collaborative systems able to perform autonomous tasks. The remarkable expansion of the embedded ICT market, together with the rise and breakthroughs of Artificial Intelligence (AI), have put the focus on the Edge as it stands as one…
Foresight Arena benchmarks AI forecasting on real-world markets, isolating predictive edge.
problem Evaluating AI forecasting ability in real-world markets is challenging due to overfitting, centralized trust, and conflated metrics.
method Permissionless, on-chain benchmark using probabilistic forecasts, commit-reveal protocol, and smart contracts.
result Demonstrates the need for 350 predictions to reliably distinguish agents of different skill levels.
Unified AI detection framework for various artifacts.
problem Effective oversight and regulation of AI deployment.
method Unified detection framework based on Mahalanobis distance scores (MDS).
result Efficient and robust estimation of covariance matrix for positive samples.
The recent advent of `Internet of Things' (IOT) has increased the demand for enabling AI-based edge computing. This has necessitated the search for efficient implementations of neural networks in terms of both computations and storage. Although extreme quantization has proven to be a powerful tool to achieve significan…
On-device federated learning updates edge models by exchanging trained results.
problem Limited training data at edge devices due to model drift.
method OS-ELM for sequential training and autoencoder for anomaly detection, combined with federated learning.
result The proposed approach produces a merged model as accurately as traditional methods with lower costs.
Neural network compression methods have enabled deploying large models on emerging edge devices with little cost, by adapting already-trained models to the constraints of these devices. The rapid development of AI-capable edge devices with limited computation and storage requires streamlined methodologies that can effi…
Cross-dimensional neural networks improve AI in Catan game.
problem Challenging to build AI agents for Catan game using RL.
method Introduced cross-dimensional neural networks to handle game complexities.
result RL agent outperforms best heuristic agent in Catan.
This paper introduces Probability Engineering to improve deep learning models.
problem Challenges in traditional probabilistic modeling for AI applications.
method Treats learned probability distributions as engineering artifacts and actively modifies them.
result Improves robustness, efficiency, adaptability, and trustworthiness of deep learning models.
This paper formalizes AI safety using hypothesis testing in GenAI.
problem Ensuring safety of generative AI tools that create realistic content.
method Formalization of computational safety through hypothesis testing and signal processing.
result Demonstrates how AI safety can be assessed quantitatively using mathematical frameworks.
AI models assess psychological risks in currency trading.
problem Identifying psychological risks in currency traders.
method Developed a decision tree model to identify patterns in historical data.
result Enhanced decision-making through real-time alerts.
FIVES generates high-order interactive features efficiently and effectively.
problem Automating the generation of high-order interactive features in tabular data.
method Formulates interactive feature generation as edge search on a feature graph, using a GNN and adjacency tensor.
result FIVES outperforms state-of-the-art methods in various datasets and real-world applications.
AIS corrects rollout-training mismatch in quantized RL, improving speed and stability.
problem Rollout-training mismatch in quantized RL causes bias and training collapse.
method Adaptive Importance Sampling (AIS) adjusts gradient correction per batch.
result AIS matches BF16 baseline on most tasks while improving speed.
This thesis tackles NILM challenges with a new dataset and efficient edge deployment techniques.
problem Limited datasets and high computational power for NILM deployment.
method Developed an interoperable data collection framework and introduced model compression techniques.
result Efficient edge deployment of NILM models for global scalability and sustainability.
AI predicts stock winners with 2.43 Sharpe ratio, but returns are highly concentrated.
problem Predicting stock returns with AI, focusing on identifying top winners.
method Deployed a state-of-the-art LLM to autonomously search the web for stock attractiveness, avoiding look-ahead bias.
result AI can generate alpha by identifying top winners, but returns are highly concentrated.
FedML aims to improve FL research by providing a library and benchmark.
problem Inconsistent FL algorithm development and performance comparison.
method FedML offers an open research library and benchmark supporting diverse computing paradigms and flexible API design.
result FedML facilitates fair algorithm comparison and development in federated learning.
The paper investigates AI robustness through experiments and statistical analysis.
problem Inaccurate AI predictions can lead to safety and adoption issues.
method Design of experiments framework to study AI classification robustness.
result AI algorithms' robustness is influenced by various factors.
The paper analyzes the pricing of a new compute futures asset.
problem Uncertainty in AI adoption and pricing of compute capital.
method An asset-pricing framework for compute futures, including synthetic futures pricing.
result Preliminary evidence suggests a positive compute risk premium.
Increase Alpha uses deep learning to predict stock movements efficiently.
problem Market inefficiencies and hidden patterns in financial data.
method Curated feed-forward and recurrent networks with 800 U.S. equities.
result Robust and stable performance with high Sharpe ratio and low drawdown.
AI models outperform simple rules in cross-asset futures timing, especially with lower transaction costs.
problem Optimizing cross-asset portfolio weights using traditional forecasting and optimization methods.
method End-to-end AI policies that map market states directly to portfolio weights, trained on CME futures using a differentiable Sharpe ratio loss function.
result Transformer-based AI policies outperform simple rules and equal weighting, trading less and matching or exceeding equal weighting through moderate transaction costs.
Polynomial chaos surrogates quantify epistemic uncertainty in AI-driven scientific models.
problem Uncertainty in reward estimates hinders interpretability in sequential generative models.
method Fit polynomial chaos expansions to trained models to propagate epistemic uncertainty and quantify sensitivity.
result Interpretable decomposition of reward components driving generative decisions.
Edge computing tackles dynamic data in IIoT with incremental learning.
problem Latency and bandwidth limitations in IoT devices.
method Incremental learning applied to edge-computing systems for continual learning.
result Reduces catastrophic forgetting and provides efficient real-time quality control.
This research bridges binary and spiking neural networks for efficient on-chip AI.
problem Reducing compute requirements in machine learning frameworks.
method Training Spiking Neural Networks in extreme quantization regime and utilizing standard training techniques for conversion.
result Training Spiking Neural Networks in extreme quantization regime achieves near full precision accuracies.
AI models solved the Kaczmarz algorithm's worst-case complexity.
problem Finding the worst-case complexity of the Kaczmarz algorithm.
method Combining AI models to analyze the Kaczmarz algorithm's performance.
result Discovered the worst-case complexity of the Kaczmarz algorithm.
AI and HPC help screen millions of molecules for SARS-CoV-2 treatments.
problem Finding effective treatments for SARS-CoV-2.
method AI and HPC enable screening of large molecule datasets.
result Data release of 23 datasets with 4.2 billion molecules.
AI progress measured by reduced compute needed to reach past performance.
problem Quantifying algorithmic progress in AI.
method Analysis of floating-point operations required to train neural networks.
result Algorithmic efficiency doubled every 16 months over 7 years.
This paper investigates a paradigm for offering artificial intelligence as a service (AI-aaS) on software-defined infrastructures (SDIs). The increasing complexity of networking and computing infrastructures is already driving the introduction of automation in networking and cloud computing management systems. Here we …
Study compares AI models for stock price prediction using financial news.
problem Predicting stock price movements using financial news.
method Used FinBERT, GPT-4, and Logistic Regression for sentiment analysis and prediction.
result Logistic Regression outperformed FinBERT and GPT-4, achieving 81.83% accuracy.
AI boosts study of rare weather extremes with lower costs.
problem Difficulty in studying rare weather events due to limited data and models.
method Coupling AI forecasts with physics models using rare-event algorithms.
result Efficiently characterizes very rare events like once-per-millennium heatwaves.
Mathematical conditions and practical computations for adversarial robustness measures are established.
problem Existence, uniqueness, and scalability of adversarial robustness measures for AI classifiers.
method Formulated and proven mathematical conditions for existence, uniqueness, and explicit analytical computation of minimal adversarial paths and distances. Practical computation demonstrated on various AI tools and synthetic benchmarks.
result Explicit mathematical conditions and practical computations for adversarial robustness measures are established.
Article evaluates AI security threats and proposes multiple measures.
problem Threats to AI integrity and security.
method Literature review, analysis of AI supply chain, discussion of mitigations.
result Multiple protective measures are necessary for AI security.
Optimized neural networks for Edge TPU achieve high accuracy in real-time image classification.
problem Designing neural networks for hardware accelerators to achieve optimal performance.
method Hardware-aware neural architecture search and model customization for Edge TPU.
result Improved accuracy-latency tradeoff on Pixel 4's Edge TPU compared to existing models.
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.
Advanced AI model predicts stock movements post earnings reports.
problem Inaccurate stock predictions from earnings reports.
method Fine-tuned LLMs with QLoRA compression, integrating financial and market data.
result Significantly improved predictive accuracy compared to benchmarks.
AIS method improves estimation of RBM partition function with reduced computational cost.
problem Efficiently estimating partition function of RBMs for large systems.
method Annealed Importance Sampling (AIS) with optimized initialization.
result Good estimation of partition function Z with reduced computational cost.
This paper compares LSTM, GRU, and Transformer models for stock price prediction.
problem Improving stock price prediction accuracy in fast-paced financial markets.
method Training models on Tesla stock data from 2015 to 2024, comparing LSTM, GRU, and Transformer.
result LSTM model achieved 94% accuracy in predicting stock prices.
CovidSens uses social media to track COVID-19 spread.
problem Accurate and timely dissemination of COVID-19 information.
method Social sensing to analyze online user data.
result Real-time COVID-19 spread tracking system.
Markov random fields (MRFs) are difficult to evaluate as generative models because computing the test log-probabilities requires the intractable partition function. Annealed importance sampling (AIS) is widely used to estimate MRF partition functions, and often yields quite accurate results. However, AIS is prone to ov…
Reinforcement Learning AI commonly uses reward/penalty signals that are objective and explicit in an environment -- e.g. game score, completion time, etc. -- in order to learn the optimal strategy for task performance. However, Human-AI interaction for such AI agents should include additional reinforcement that is impl…
A simpler edge-based discretization method without dual volumes.
problem Efficiently computing edge-based discretization vectors without forming dual volumes.
method Directly compute edge-midpoint vectors and reduce dual volume formation.
result Significant reduction in computing time for tetrahedral grids.
The problem of replicating the flexibility of human common-sense reasoning has captured the imagination of computer scientists since the early days of Alan Turing's foundational work on computation and the philosophy of artificial intelligence. In the intervening years, the idea of cognition as computation has emerged …
Coded Federated Learning speeds up training in edge computing networks.
problem Slow convergence in Federated Learning due to heterogeneity and stochastic fluctuations.
method Exploiting statistical properties of compute and communication delays, distributed kernel embedding, and random Fourier features.
result Significant performance gains for CodedFedL in distributed non-linear regression and classification problems.