Machine Learning (ML) Latest News and Trending articles from all top sources only on Techminis

A naukri.com initiative

New

Home

ML News

Arxiv

Image Credit: Arxiv

DREMnet: An Interpretable Denoising Framework for Semi-Airborne Transient Electromagnetic Signal

The semi-airborne transient electromagnetic method (SATEM) is a useful surveying technique for challenging terrains.
DREMnet is an interpretable decoupled representation learning framework that enhances the denoising process of SATEM signals.
DREMnet disentangles data into content and context factors, leading to robust and interpretable denoising results in complex conditions.
Experimental results demonstrate that DREMnet outperforms existing techniques, improving the accuracy of identifying subsurface electrical structures.

Read Full Article

5 Likes

Arxiv

206

Image Credit: Arxiv

Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback

Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning large language models with human preferences.
This paper explores data-driven bottlenecks in RLHF performance scaling, focusing on reward hacking and decreasing response diversity.
The hybrid reward system, combining reasoning task verifiers (RTV) and a generative reward model (GenRM), is introduced to mitigate reward hacking.
The novel prompt-selection method, Pre-PPO, is proposed to maintain response diversity and enhance learning effectiveness.

Read Full Article

12 Likes

Arxiv

226

Image Credit: Arxiv

WeatherMesh-3: Fast and accurate operational global weather forecasting

WeatherMesh-3 (WM-3) is a transformer-based global weather forecasting system that improves accuracy and computational efficiency.
It introduces a latent rollout for arbitrary-length predictions in latent space and a modular architecture for blended initial conditions.
WM-3 generates 14-day global forecasts at 0.25-degree resolution in 12 seconds on a single RTX 4090, achieving superior accuracy and significant speedup.
This model aims to democratize weather forecasting and push the performance boundaries of machine learning-based prediction.

Read Full Article

13 Likes

Arxiv

Image Credit: Arxiv

DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentation

DynaGraph is an interpretable contrastive graph model that learns the dynamics of multivariate time-series electronic health records (EHRs) as part of optimization.
DynaGraph captures the hidden dependencies of the multivariate time-series using dynamic graph construction.
Compared to state-of-the-art models, DynaGraph achieves significant improvements in accuracy and sensitivity in various clinical datasets.
DynaGraph provides clinical validation by indicating the importance of clinical covariates over time.

Read Full Article

5 Likes

Discover more

Arxiv

190

Image Credit: Arxiv

FLIP: Towards Comprehensive and Reliable Evaluation of Federated Prompt Learning

The paper introduces a framework called FLIP to evaluate federated prompt learning algorithms.
FLIP assesses the performance of 8 state-of-the-art federated prompt learning methods across various scenarios.
The findings demonstrate that prompt learning maintains strong generalization performance with minimal resource consumption.
This work emphasizes the effectiveness of federated prompt learning in data scarcity and cross-domain distributional shift scenarios.

Read Full Article

11 Likes

Arxiv

325

Image Credit: Arxiv

Machine Learning Models for Soil Parameter Prediction Based on Satellite, Weather, Clay and Yield Data

The AgroLens project aims to develop Machine Learning-based methodologies for predicting soil nutrient levels without relying on laboratory tests.
The project utilizes the LUCAS Soil dataset and Sentinel-2 satellite imagery to estimate key soil properties including phosphorus, potassium, nitrogen, and pH levels.
Supplementary features like weather data, harvest rates, and Clay AI-generated embeddings are integrated to enhance the soil nutrient prediction model.
Implementation of advanced algorithms such as Random Forests, Extreme Gradient Boosting (XGBoost), and Fully Connected Neural Networks (FCNN) results in robust model performance and accurate nutrient prediction.

Read Full Article

19 Likes

Arxiv

214

Image Credit: Arxiv

Hybrid Time-Domain Behavior Model Based on Neural Differential Equations and RNNs

Researchers propose a novel continuous-time domain hybrid modeling paradigm for nonlinear dynamics system identification.
The hybrid model integrates neural network differential models with recurrent neural networks (RNNs).
Theoretical analysis demonstrates advantages in event-driven dynamic mutation response and gradient propagation stability.
Validation using real data shows improved fitting accuracy and effectiveness in capturing nonlinear memory effects.

Read Full Article

12 Likes

Arxiv

305

Image Credit: Arxiv

MASCOTS: Model-Agnostic Symbolic COunterfactual explanations for Time Series

Counterfactual explanations provide an intuitive way to understand model decisions by identifying minimal changes required to alter an outcome.
MASCOTS is a method that leverages the Bag-of-Receptive-Fields representation alongside symbolic transformations to enhance interpretability while preserving fidelity to the original data and model.
MASCOTS directly generates meaningful and diverse counterfactual observations in a model-agnostic manner, operating on both univariate and multivariate data.
MASCOTS improves interpretability and sparsity, allowing for explanations in visual, natural language, or semantic representations.

Read Full Article

18 Likes

Arxiv

Image Credit: Arxiv

On-site estimation of battery electrochemical parameters via transfer learning based physics-informed neural network approach

This paper presents a novel physical parameter estimation framework for on-site model characterization, using a two-phase modelling strategy with Physics-Informed Neural Networks (PINNs) and transfer learning (TL).
In the first phase, a PINN is trained using only the physical principles of the single particle model (SPM) equations.
In the second phase, the majority of the PINN parameters are frozen, while critical electrochemical parameters are set as trainable and adjusted using real-world voltage profile data.
The proposed approach significantly reduces computational costs, making it suitable for real-time implementation on Battery Management Systems (BMS).

Read Full Article

1 Like

Arxiv

325

Image Credit: Arxiv

Generative Reliability-Based Design Optimization Using In-Context Learning Capabilities of Large Language Models

Large Language Models (LLMs) have in-context learning capabilities for reasoning, problem-solving, and pattern recognition.
A generative design method is proposed that combines LLMs with metaheuristic algorithms for reliability-based design optimization.
Reliability analysis is performed using LLMs and Kriging surrogate modeling to reduce computational burden.
Experimental results demonstrate the effectiveness of the proposed approach in identifying feasible solutions.

Read Full Article

19 Likes

Arxiv

146

Image Credit: Arxiv

STADE: Standard Deviation as a Pruning Metric

Large Language Models (LLMs) are widely used for various tasks, but they require long training times and large model sizes.
Pruning methods like Wanda can reduce computational demands without retraining and are effective in maintaining performance.
This study provides a theoretical explanation of the effectiveness of Wanda and introduces a new pruning method called STADE based on the standard deviation of the input.
Experiments on Llama and Open Pre-trained Transformers (OPT) models validate the theoretical findings, demonstrating the variability of Wanda's optimal performance depending on training conditions.

Read Full Article

8 Likes

Arxiv

158

Image Credit: Arxiv

A Causal Framework to Measure and Mitigate Non-binary Treatment Discrimination

Fairness analyses of algorithmic decision-making should include non-binary treatment decisions.
A causal framework is proposed to measure treatment disparity and its impact on outcomes.
Loan approval datasets are analyzed to reveal potential discrimination in non-binary treatment decisions.
The framework can mitigate treatment discrimination and ensure fair decision-making processes.

Read Full Article

9 Likes

Arxiv

Image Credit: Arxiv

DeepOFormer: Deep Operator Learning with Domain-informed Features for Fatigue Life Prediction

Researchers propose DeepOFormer, a deep operator learning framework for fatigue life prediction.
It addresses the challenge of overfitting using a transformer-based encoder and a mean L2 relative error loss function.
Domain-informed features, such as Stussi, Weibull, and Pascual and Meeker (PM), are considered to improve prediction accuracy.
DeepOFormer achieves superior performance compared to state-of-the-art deep/machine learning methods in predicting fatigue life in aluminum alloys.

Read Full Article

Arxiv

115

Image Credit: Arxiv

Probabilistic Uncertain Reward Model: A Natural Generalization of Bradley-Terry Reward Model

Reinforcement Learning from Human Feedback (RLHF) has emerged as a critical technique for training large language models.
The Probabilistic Uncertain Reward Model (PURM) is proposed as a natural generalization of the classical Bradley-Terry reward model.
PURM learns reward distributions directly from preference data and quantifies per-sample uncertainty.
Experiments demonstrate that PURM significantly delays reward hacking and improves final reward performance.

Read Full Article

6 Likes

Arxiv

135

Image Credit: Arxiv

SPDNet: Seasonal-Periodic Decomposition Network for Advanced Residential Demand Forecasting

Residential electricity demand forecasting is important for efficient energy management and grid stability.
A novel deep learning framework called SPDNet is proposed to tackle the challenges of capturing intricate temporal dynamics in electricity demand data.
SPDNet consists of two main modules: Seasonal-Trend Decomposition Module (STDM) and Periodical Decomposition Module (PDM).
Extensive experiments show that SPDNet outperforms traditional and advanced models in both accuracy and efficiency.

Read Full Article

8 Likes

For uninterrupted reading, download the app