Predicts language model performance from public models without training.
problem Understanding how language model performance varies with scale.
method Observational approach building scaling laws from publicly available models.
result Smooth, sigmoidal behavior of emergent phenomena is predictable from small models.
The paper adds explanation to predictive process monitoring.
problem Equipping predictive business process monitoring with explanation capabilities.
method Used game theory of Shapley Values to obtain robust explanations.
result First time explanations given in predictive business process monitoring.
New method predicts language model scaling risks with limited data.
problem Accurately predicting model capabilities and risks with limited data.
method Introduces a robust estimation framework using beta-binomial distribution and dynamic sampling strategy.
result More accurate predictions of rare risks and capabilities at lower computational cost.
Wind energy forecasting helps to manage power production, and hence, reduces energy cost. Deep Neural Networks (DNN) mimics hierarchical learning in the human brain and thus possesses hierarchical, distributed, and multi-task learning capabilities. Based on aforementioned characteristics, we report Deep Belief Network …
Language models can predict numeric values as strings.
problem Regression tasks with numeric predictions.
method Causal sequence decoding models trained for next-token prediction.
result Decoder-based heads perform as well as standard heads in numeric regression tasks.
In a given scenario, simultaneously and accurately predicting every possible interaction of traffic participants is an important capability for autonomous vehicles. The majority of current researches focused on the prediction of an single entity without incorporating the environment information. Although some approache…
Experts predict significant adoption of decentralized finance by 2034, with traditional finance adapting.
problem Adoption and integration of decentralized finance (DeFi) in financial services.
method Survey analysis using New Institutional Economics and Dynamic Capabilities Theory.
result Experts expect adoption of DeFi to rise from negligible to 43% by 2034, with traditional finance likely to embrace it.
Proposes a framework to identify and correct model-form errors in nonlinear systems.
problem Model-form errors in nonlinear dynamical systems due to unknown or approximated governing equations.
method Uses a hybrid approach combining machine learning and Bayesian filtering to estimate and correct model-form errors.
result Improves the predictive capability of known but approximate governing equations for nonlinear dynamical systems.
In surface mount technology (SMT), mounted components on soldered pads are subject to move during reflow process. This capability is known as self-alignment and is the result of fluid dynamic behaviour of molten solder paste. This capability is critical in SMT because inaccurate self-alignment causes defects such as ov…
This paper shows ARMs and EBMs are equivalent, revealing ARM lookahead capabilities.
problem Understanding the lookahead capabilities of next-token prediction models.
method Unified view of ARMs and EBMs, establishing a bijection and deriving equivalence.
result ARMs and EBMs are equivalent, revealing ARM lookahead capabilities.
Transformers can approximate posterior predictive distributions through in-context learning.
problem Bayesian prediction tasks, especially beyond point predictions.
method Gradient descent algorithm targeting posterior predictive mean and variance, followed by nonlinear mappings.
result Transformers can implement algorithms to approximate posterior predictive distributions.
SIP corrects model bias in Bayesian ML.
problem Model selection biases predictions in Bayesian ML.
method Sparse Implicit Processes (SIP) for flexible, trainable predictions.
result SIP provides better predictive distributions than initial models.
Proposes a neural network loss function for better uncertainty estimation.
problem Challenges in estimating predictive uncertainty of neural networks.
method Bayesian Validation Metric (BVM) framework with ensemble learning.
result Competitive and robust uncertainty estimation on in-distribution and out-of-distribution data.
Proposes a new network for accurate predictions and uncertainty estimation.
problem Uncertainty estimation in regression predictions without sacrificing accuracy.
method Decoupled two-stage training process with custom loss function.
result Reduces prediction error by 23-34% while maintaining 95% PICP.
Proposes CTSDG model for better vehicle intention prediction across domains.
problem Domain generalization for vehicle intention prediction in dynamic environments.
method Structural causal model with recurrent latent variable integration.
result Consistent improvement in prediction accuracy compared to state-of-the-art methods.
This paper describes recent development and test implementation of a continuous time recurrent neural network that has been configured to predict rates of change in securities. It presents outcomes in the context of popular technical analysis indicators and highlights the potential impact of continuous predictive capab…
This paper presents regression models obtained from a process of blind prediction of peptide binding affinity from provided descriptors for several distinct datasets as part of the 2006 Comparative Evaluation of Prediction Algorithms (COEPRA) contest. This paper finds that kernel partial least squares, a nonlinear part…
Model predicts political ideology using context vectors to mitigate bias and scarcity.
problem Scarcity and selection bias in political ideology prediction.
method Proposes a statistical model decomposing embeddings into context and position vectors, training an end-to-end model for deployment.
result Model can predict ideological labels even with minimal biased data, outperforming state-of-the-art methods.
ChatGPT enhances GNN for stock movement prediction.
problem Predicting stock movements using textual data.
method Integrates ChatGPT's graph inference into GNN for stock movement forecasting.
result Model outperforms state-of-the-art benchmarks in stock movement forecasting.
ChatGPT struggles in predicting stock movements, underperforming traditional methods.
problem Predicting stock market movements using ChatGPT.
method Zero-shot analysis of ChatGPT's multimodal stock prediction capabilities.
result ChatGPT underperforms traditional methods and state-of-the-art models in predicting stock movements.
Accurately predicting future behaviors of surrounding vehicles is an essential capability for autonomous vehicles in order to plan safe and feasible trajectories. The behaviors of others, however, are full of uncertainties. Both rational and irrational behaviors exist, and the autonomous vehicles need to be aware of th…
Well-established methods for the solution of stochastic partial differential equations (SPDEs) typically struggle in problems with high-dimensional inputs/outputs. Such difficulties are only amplified in large-scale applications where even a few tens of full-order model runs are impracticable. While dimensionality redu…
We applied machine learning to predict whether a gene is involved in axon regeneration. We extracted 31 features from different databases and trained five machine learning models. Our optimal model, a Random Forest Classifier with 50 submodels, yielded a test score of 85.71%, which is 4.1% higher than the baseline scor…
Framework for joint learning of tasks on dementia data with missing values.
problem Lack of multi-task learning, handling time-dependent data, and missing values in dementia forecasting.
method Proposes SSHIBA model using Bayesian variational inference for imputation and combined information from different views.
result SSHIBA model outperforms baselines in predicting diagnosis, ventricle volume, and clinical scores in dementia.
A new method learns complex dynamical systems from data efficiently.
problem Learning complex dynamical systems from large-scale data efficiently.
method Low-rank structured variational autoencoding framework for nonlinear Gaussian state-space models.
result Consistently demonstrates better predictive capabilities compared to other models.
DeepHazard uses neural networks to predict time-varying survival risks.
problem Traditional survival models assume proportional hazards and do not account for time-varying covariate information.
method DeepHazard is a neural network approach that models time-varying hazards without proportional hazards assumption.
result DeepHazard outperforms existing methods in predicting survival time, as shown by C-index metrics on real datasets.
ML Compass helps organizations choose AI models that balance utility, cost, and compliance.
problem Selecting AI models that meet user utility, deployment costs, and compliance requirements.
method Develops ML Compass, a framework for constrained optimization over a capability-cost frontier, using internal measures and empirical data.
result ML Compass produces deployment-aware recommendations that differ from capability-only rankings, clarifying trade-offs between capability, cost, and safety.
Geometric deep learning predicts knot invariants.
problem Predicting knot invariants from knot data.
method Constructing a functor from knots to graphs and using graph neural networks.
result High generalization capabilities demonstrated.
New model predicts multiple future trends from merchant transactions.
problem Predicting multiple future trends from merchant transaction history.
method Convolutional neural networks and encoder-decoder structure.
result Demonstrated effectiveness in predicting multiple future trends.
With few exceptions, the field of Machine Learning (ML) research has largely ignored the browser as a computational engine. Beyond an educational resource for ML, the browser has vast potential to not only improve the state-of-the-art in ML research, but also, inexpensively and on a massive scale, to bring sophisticate…
Ride sharing has important implications in terms of environmental, social and individual goals by reducing carbon footprints, fostering social interactions and economizing commuter costs. The ride sharing systems that are commonly available lack adaptive and scalable techniques that can simultaneously learn from the la…
New model predicts particle precipitation from magnetosphere to ionosphere.
problem Improving prediction of electron particle precipitation from magnetosphere to ionosphere.
method Compilation of new database, use of machine learning (ML) tools, neural network (PrecipNet).
result PrecipNet achieves >50% reduction in errors and better captures dynamic changes.
Cardea automates machine learning for EHRs, improving model building efficiency.
problem Lack of a trusted, open-source framework for automated machine learning in EHRs.
method Uses FHIR for data structure, AUTOML frameworks for feature engineering, model selection, and tuning, and an adaptive data assembler.
result Demonstrates framework's effectiveness on 5 prediction tasks, highlighting its flexibility and human competitiveness.
STanHop predicts multivariate time series with memory-enhanced capabilities.
problem Predicting multivariate time series with memory-enhanced capabilities.
method Sparse Tandem Hopfield Network (STanHop) with two external memory modules.
result STanHop outperforms dense Hopfield models in memory retrieval error.
Neural EKF improves structural dynamics prediction.
problem Accurately predicting structural response for health monitoring.
method Neural Extended Kalman Filter (Neural EKF) for learning dynamics.
result Significant predictive capabilities demonstrated on simulated and real-world data.
New regularization method corrects over-shrinkage in small data regression.
problem Over-shrinkage in small data regression leading to underfitting.
method Negative-capable ridge family that permits negative regularization.
result Negative regularization acts as controlled anti-shrinkage, increasing effective complexity.
Arctic coastal morphology is governed by multiple factors, many of which are affected by climatological changes. As the season length for shorefast ice decreases and temperatures warm permafrost soils, coastlines are more susceptible to erosion from storm waves. Such coastal erosion is a concern, since the majority of …
Study uses machine learning to predict nonlinear seismic brace behavior.
problem Predicting nonlinear seismic response of structural braces.
method State-of-the-art machine learning techniques, specifically LSTM, were used.
result LSTM method effectively captures nonlinear brace behavior.
Enhances ENet's prediction accuracy while maintaining uncertainty estimation.
problem Gradient shrinkage problem in ENet's loss function.
method Proposes a multi-task learning framework with a modified MSE loss function.
result Improves ENet's prediction accuracy without losing uncertainty estimation.
Deep learning predicts S&P 500 index direction.
problem Accurate stock price prediction remains challenging.
method Convolutional neural network model for S&P 500 index forecasting.
result Model achieves over 55% accuracy in predicting index direction.
BIG-bench benchmarks language models, revealing their strengths and weaknesses.
problem Understanding and quantifying the capabilities of large language models.
method Developed the BIG-bench benchmark with 204 tasks from various domains, evaluated across multiple model sizes and types.
result Model performance improves with scale but remains poor, with breakthrough behaviors often involving multiple steps.
Proposes oblique predictive clustering trees for faster, more efficient learning.
problem Learning time scales poorly with output space dimensionality and cannot exploit data sparsity.
method Design and implement oblique splits using linear combinations of features.
result Achieves performance on-par with state-of-the-art methods and is orders of magnitude faster.
It is generally accepted that all models are wrong -- the difficulty is determining which are useful. Here, a useful model is considered as one that is capable of combining data and expert knowledge, through an inversion or calibration process, to adequately characterize the uncertainty in predictions of interest. This…
LLMs can select predictive features without seeing training data.
problem Selecting the best features for prediction tasks.
method Zero-shot prompting LLMs with feature names and task descriptions.
result LLMs consistently identify predictive features across various mechanisms.
Enhances Transformers for better risk assessment in finance.
problem Transformer models lack sensitivity to extreme financial losses.
method Integrates Loss-at-Risk function with Value at Risk (VaR) and Conditional Value at Risk (CVaR).
result Improves risk prediction and management in financial datasets.
A regression-based BNN model is proposed to predict spatiotemporal quantities like hourly rider demand with calibrated uncertainties. The main contributions of this paper are (i) A feed-forward deterministic neural network (DetNN) architecture that predicts cyclical time series data with sensitivity to anomalous foreca…
Humans gain an implicit understanding of physical laws through observing and interacting with the world. Endowing an autonomous agent with an understanding of physical laws through experience and observation is seldom practical: we should seek alternatives. Fortunately, many of the laws of behaviour of the physical wor…
For autonomous vehicles (AVs) to behave appropriately on roads populated by human-driven vehicles, they must be able to reason about the uncertain intentions and decisions of other drivers from rich perceptual information. Towards these capabilities, we present a probabilistic forecasting model of future interactions b…