A naukri.com initiative
ML News
Arxiv
15h
62
Image Credit: Arxiv
Time-Series Forecasting via Topological Information Supervised Framework with Efficient Topological Feature Learning
- Topological Data Analysis (TDA) is used for extracting features from complex data structures.
- Integration of TDA with time-series prediction faces challenges related to temporal dependencies and computational bottlenecks.
- The Topological Information Supervised (TIS) Prediction framework proposes using neural networks and CGANs to generate synthetic topological features.
- TIS models, TIS-BiGRU and TIS-Informer, outperform conventional predictors in capturing short-term and long-term temporal dependencies.
Read Full Article
3 Likes
Arxiv
15h
171
Image Credit: Arxiv
Accelerating High-Efficiency Organic Photovoltaic Discovery via Pretrained Graph Neural Networks and Generative Reinforcement Learning
- A new framework combining graph neural networks and reinforcement learning has been proposed to design high-efficiency organic photovoltaic (OPV) molecules.
- The integrated approach includes large-scale pretraining of graph neural networks and a Generative Pretrained Transformer 2 (GPT-2) based reinforcement learning strategy.
- The proposed approach has predicted efficiencies approaching 21%, and provides design guidelines for enhancing power conversion efficiency (PCE).
- To support further discovery, the largest open-source OPV dataset is being built, and collaboration with experimental teams is planned for synthesizing and characterizing AI-designed molecules.
Read Full Article
10 Likes
Arxiv
15h
186
Image Credit: Arxiv
An extension of linear self-attention for in-context learning
- In-context learning is a key characteristic of transformers.
- Self-attention mechanism in transformers lacks flexibility in certain tasks.
- Linear self-attention is extended by introducing a bias matrix.
- The extended linear self-attention enables flexible matrix manipulations.
Read Full Article
11 Likes
Arxiv
15h
204
Image Credit: Arxiv
Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics
- Deep learning based diagnostic AI systems based on medical images are starting to provide similar performance as human experts.
- Lack of transparency in complex AI systems hinders their adoption in high-risk applications like healthcare.
- Conformal analysis is being deployed to address the problem of lack of transparency in foundation models for skin lesion classification.
- The method of conformal analysis provides coverage guarantee at population level and uncertainty score for each individual, making it a helpful fairness metric for evaluating AI models.
Read Full Article
12 Likes
Arxiv
15h
285
Image Credit: Arxiv
When Counterfactual Reasoning Fails: Chaos and Real-World Complexity
- Counterfactual reasoning is often seen as the 'holy grail' of causal learning, but its reliability in real-world complex settings is largely unexplored.
- This work investigates the limitations of counterfactual reasoning within the framework of Structural Causal Models.
- Realistic assumptions, such as model uncertainty and chaotic dynamics, can lead to counterintuitive outcomes.
- This study encourages caution when applying counterfactual reasoning in chaotic and uncertain situations.
Read Full Article
17 Likes
Arxiv
15h
40
Image Credit: Arxiv
An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU function
- Nonlinear matrix decomposition with the ReLU function finds application in various fields.
- The standard ReLU-NMD model minimizes the least squares error while the Latent-ReLU-NMD model introduces a latent variable to achieve a different low-rank solution.
- The 3B-ReLU-NMD model allows elimination of the rank constraint in Latent-ReLU-NMD.
- A novel extrapolated variant, eBCD-NMD, of block coordinate descent (BCD) for 3B-ReLU-NMD is proven to be convergent and offers significant acceleration.
Read Full Article
2 Likes
Arxiv
15h
40
Image Credit: Arxiv
Communication-Efficient and Personalized Federated Foundation Model Fine-Tuning via Tri-Matrix Adaptation
- A new method called CE-LoRA (communication-efficient federated LoRA adaptation) has been introduced to address challenges in fine-tuning pre-trained foundation models in federated learning.
- CE-LoRA utilizes a tri-factorization low-rank adaptation approach with personalized model parameter aggregation.
- By introducing a small-size dense matrix and considering client similarity, CE-LoRA reduces communication cost and achieves comparable empirical performance.
- Experiments show that CE-LoRA significantly reduces communication overhead, improves performance under non-iid data conditions, and enhances data privacy protection.
Read Full Article
2 Likes
Arxiv
15h
259
Image Credit: Arxiv
An End-to-End Comprehensive Gear Fault Diagnosis Method Based on Multi-Scale Feature-Level Fusion Strategy
- An integrated intelligent method of fault diagnosis for gears using acceleration signals is proposed.
- The method is based on Gabor-based Adaptive Short-Time Fourier Transform (Gabor-ASTFT) and Dual-Tree Complex Wavelet Transform(DTCWT) algorithms.
- The proposed method incorporates a dilated residual structure and a feature fusion layer for multi-scale analysis of fault features.
- Comparative experiments demonstrate the effectiveness of the proposed method for end-to-end fault diagnosis of gears.
Read Full Article
15 Likes
Arxiv
15h
358
Image Credit: Arxiv
DiffScale: Continuous Downscaling and Bias Correction of Subseasonal Wind Speed Forecasts using Diffusion Models
- Renewable resources can benefit from skillful subseasonal to seasonal (S2S) wind speed forecasts.
- DiffScale is a diffusion model that enhances S2S wind speed predictions by downscaling and correcting forecast errors.
- DiffScale can super-resolve spatial information for continuous downscaling factors and lead times, without auto-regression or sequence prediction.
- Synthetic experiments showed that DiffScale significantly improves wind speed prediction quality, outperforming baselines up to week 3.
Read Full Article
21 Likes
Arxiv
15h
270
Image Credit: Arxiv
Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations
- This study presents an empirical investigation into the energy consumption of Discriminative and Generative AI models within real-world MLOps pipelines.
- For Discriminative models, the study examines various architectures and hyperparameters during training and inference and identifies energy-efficient practices.
- For Generative AI, the study focuses on Large Language Models (LLMs) and assesses energy consumption across different model sizes and varying service requests.
- The results indicate that optimizing architectures, hyperparameters, and hardware can significantly reduce energy consumption for Discriminative models without sacrificing performance.
Read Full Article
16 Likes
Arxiv
15h
351
Image Credit: Arxiv
Noise-based reward-modulated learning
- Recent advances in reinforcement learning (RL) have led to significant improvements in task performance.
- Noise-based alternatives like reward-modulated Hebbian learning (RMHL) have been proposed, but their performance has been limited in scenarios with delayed rewards.
- A novel noise-based learning rule has been derived, which combines directional derivative theory and Hebbian-like updates, enabling efficient, gradient-free learning in RL.
- The proposed method significantly outperforms RMHL and is competitive with backpropagation-based baselines, making it suitable for low-power and real-time applications.
Read Full Article
21 Likes
Arxiv
15h
69
Image Credit: Arxiv
Rethinking Key-Value Cache Compression Techniques for Large Language Model Serving
- Key-Value cache ( exttt{KV} exttt{cache}) compression has emerged as a promising technique to optimize Large Language Model (LLM) serving.
- The paper comprehensively reviews existing algorithmic designs and benchmark studies, identifying missing performance measurement aspects that hinder practical adoption.
- Representative exttt{KV} exttt{cache} compression methods are evaluated, uncovering issues that affect computational efficiency and end-to-end latency.
- Tools are provided to aid future exttt{KV} exttt{cache} compression studies and facilitate practical deployment in production.
Read Full Article
4 Likes
Arxiv
15h
95
Image Credit: Arxiv
CITRAS: Covariate-Informed Transformer for Time Series Forecasting
- CITRAS is a patch-based Transformer that addresses challenges in covariate-informed time series forecasting.
- It leverages multiple targets and covariates, considering both past and future forecasting horizons.
- CITRAS introduces two novel mechanisms: Key-Value (KV) Shift and Attention Score Smoothing.
- Experimental results show that CITRAS achieves state-of-the-art performance in both covariate-informed and multivariate forecasting.
Read Full Article
5 Likes
Arxiv
15h
204
Image Credit: Arxiv
Bayesian Predictive Coding
- Bayesian Predictive Coding (BPC) is a Bayesian extension to the influential theory of Predictive Coding (PC) in information processing in the brain.
- BPC estimates a posterior distribution over network parameters, allowing for better quantification of epistemic uncertainty.
- Compared to PC, BPC converges in fewer epochs in the full-batch setting and remains competitive in the mini-batch setting.
- BPC provides a biologically plausible method for Bayesian learning in the brain and offers attractive uncertainty quantification in deep learning.
Read Full Article
12 Likes
Arxiv
15h
277
Image Credit: Arxiv
Accelerated Airfoil Design Using Neural Network Approaches
- This paper demonstrates the use of Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) to predict airfoil shapes from targeted pressure distribution and vice versa.
- The dataset used in this study consists of 1600 airfoil shapes simulated at various Reynolds numbers and angles of attack.
- The refined models show improved efficiency and reduced training time compared to the CNN model for complex datasets.
- The proposed CNN and DNN models show promising results and have the potential to accelerate aerodynamic optimization and design of high-performance airfoils.
Read Full Article
16 Likes
For uninterrupted reading, download the app