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ML News
Medium
1d
30
Image Credit: Medium
Career Insight: Navigating your Future Path
- Career Insight is an intelligent platform that helps college students navigate the world of emerging technologies.
- The platform provides tools, guidance, and direction for students to become career-ready in the rapidly evolving job market.
- It offers modules for learning, building projects, and job-hunting, adapting to each user's journey.
- Career Insight was created by Meet Dodiya, Kashish Bhanushali, and Ramesh Yadav, mentored by Prof. Chandan Kolvankar.
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Arxiv
1d
366
Image Credit: Arxiv
A Novel Graph Transformer Framework for Gene Regulatory Network Inference
- A novel Graph Transformer Framework (GT-GRN) for Gene Regulatory Network (GRN) inference is introduced.
- The GT-GRN model incorporates autoencoder embeddings, prior knowledge from GRN structures, and positional information of genes.
- Using raw data, the autoencoder captures gene expression patterns to preserve biological signals.
- Experimental results demonstrate that GT-GRN outperforms existing GRN inference methods in terms of accuracy.
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Arxiv
1d
101
Image Credit: Arxiv
STFM: A Spatio-Temporal Information Fusion Model Based on Phase Space Reconstruction for Sea Surface Temperature Prediction
- The sea surface temperature (SST) prediction is crucial for optimizing production planning and research.
- Challenges exist due to the inherent nonlinearity of the marine dynamic system.
- This study introduces the Spatio-Temporal Fusion Mapping (STFM) for accurate SST prediction.
- STFM uses phase space reconstruction and achieves high accuracy with minimal training data.
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Arxiv
1d
218
Image Credit: Arxiv
Safety Pretraining: Toward the Next Generation of Safe AI
- Large language models (LLMs) are being deployed in high-stakes settings, but generating harmful or toxic content is a major concern.
- A data-centric pretraining framework is proposed to address this challenge by incorporating safety measures from the start.
- Key contributions include a safety classifier, a large synthetic safety dataset, and Harmfulness-Tag annotations to flag unsafe content.
- The safety-pretrained models successfully reduce attack success rates and maintain performance on standard LLM safety benchmarks.
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Arxiv
1d
210
Image Credit: Arxiv
(Im)possibility of Automated Hallucination Detection in Large Language Models
- Automated hallucination detection in large language models (LLMs) is analyzed in a theoretical framework.
- The study establishes an equivalence between hallucination detection and language identification, concluding that detection is fundamentally impossible for most language collections if the detector is trained using only correct examples.
- The use of expert-labeled feedback, including negative examples, makes automated hallucination detection possible for all countable language collections.
- These findings support the importance of expert-labeled examples and feedback-based methods for reliable deployment of LLMs.
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Arxiv
1d
82
Image Credit: Arxiv
Democracy of AI Numerical Weather Models: An Example of Global Forecasting with FourCastNetv2 Made by a University Research Lab Using GPU
- This paper demonstrates the feasibility of democratizing AI-driven global weather forecasting models among university research groups.
- The paper highlights the use of NVIDIA's FourCastNetv2, an advanced neural network for weather prediction trained on a subset of the ECMWF ERA5 dataset.
- The training of FourCastNetv2 involved 64 A100 GPUs and took 16 hours to complete, offering significant time and cost reductions compared to traditional NWP.
- The paper provides insights on data management, training efficiency, and model validation, serving as a guide for other university research groups to develop AI weather forecasting programs.
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Arxiv
1d
56
Image Credit: Arxiv
Statistical Guarantees in Synthetic Data through Conformal Adversarial Generation
- The generation of high-quality synthetic data presents significant challenges in machine learning research, particularly regarding statistical fidelity and uncertainty quantification.
- A novel framework has been proposed that incorporates conformal prediction methodologies into Generative Adversarial Networks (GANs) to address the lack of statistical guarantees in generative models.
- The framework, Conformalized GAN (cGAN), integrates multiple conformal prediction paradigms, enabling distribution-free uncertainty quantification in generated samples.
- The approach demonstrates enhanced calibration properties and provides provable statistical guarantees, making the use of synthetic data reliable in high-stakes domains.
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Arxiv
1d
165
Image Credit: Arxiv
Whence Is A Model Fair? Fixing Fairness Bugs via Propensity Score Matching
- Fairness-aware learning aims to mitigate discrimination against specific protected social groups.
- Training and test data sampling can affect the reliability of reported fairness metrics.
- FairMatch, a post-processing method, utilizes propensity score matching to evaluate and mitigate bias.
- Experimental results show that FairMatch improves fairness evaluation and mitigation without sacrificing predictive performance.
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9 Likes
Arxiv
1d
172
Image Credit: Arxiv
In-Context Learning can distort the relationship between sequence likelihoods and biological fitness
- Language models have emerged as powerful predictors of the viability of biological sequences.
- In-context learning can distort the relationship between fitness and likelihood scores of sequences.
- This distortion is prominently seen in sequences containing repeated motifs.
- The phenomenon affects transformer-based models and is mediated by a look-up operation.
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Arxiv
1d
270
Image Credit: Arxiv
Sparse Phased Array Optimization Using Deep Learning
- Antenna arrays are widely used in wireless communication, radar systems, radio astronomy, and military defense to enhance signal strength, directivity, and interference suppression.
- A deep learning-based optimization approach is introduced to enhance the design of sparse phased arrays by reducing grating lobes.
- Neural networks are used to approximate the non-convex cost function, allowing for cost function minimization through gradient descent.
- The method demonstrates significant cost reductions, ranging from 411% to 643%, with an average improvement of 552% in ten array configurations.
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Arxiv
1d
274
Image Credit: Arxiv
Conditional Diffusion-Based Retrieval of Atmospheric CO2 from Earth Observing Spectroscopy
- Satellite-based estimates of greenhouse gas (GHG) properties from observations of reflected solar spectra are important for understanding and monitoring terrestrial systems and the carbon cycle.
- A computationally expensive algorithm called Optimal Estimation (OE) is currently used for GHG concentration estimation, but it has convergence issues and provides unrealistic uncertainty estimates.
- To address this, a diffusion-based approach is proposed for retrieving a Gaussian or non-Gaussian posterior, while significantly improving computational speed.
- This approach is aimed at enabling near continuous real-time global monitoring of carbon sources and sinks for better climate impact assessment and policy making.
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Arxiv
1d
293
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A Novel Hybrid Approach Using an Attention-Based Transformer + GRU Model for Predicting Cryptocurrency Prices
- A novel deep learning hybrid model is introduced for predicting cryptocurrency prices.
- The model integrates attention Transformer and Gated Recurrent Unit (GRU) architectures.
- The hybrid model combines the strength of Transformer in capturing long-range patterns and GRU's ability to model short-term and sequential trends.
- The model outperforms four other machine learning models, demonstrating its accuracy and effectiveness in financial prediction tasks.
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Arxiv
1d
372
Image Credit: Arxiv
OUI Need to Talk About Weight Decay: A New Perspective on Overfitting Detection
- A new perspective on overfitting detection in deep neural networks (DNNs) has been introduced.
- The Overfitting-Underfitting Indicator (OUI) is a tool for monitoring DNN training dynamics and selecting optimal regularization hyperparameters.
- OUI helps in identifying whether a model is overfitting or underfitting without requiring validation data.
- Experiments show that maintaining OUI within a specific interval leads to improved generalization and validation scores.
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22 Likes
Arxiv
1d
368
Image Credit: Arxiv
A Double-Norm Aggregated Tensor Latent Factorization Model for Temporal-Aware Traffic Speed Imputation
- Researchers propose a Temporal-Aware Traffic Speed Imputation model for intelligent transportation systems.
- The model uses a combination of L2-norm and smooth L1-norm in its loss function.
- It adopts a single latent factor-dependent, nonnegative, and multiplicative update approach.
- Empirical studies demonstrate that the model achieves accurate imputations for missing traffic speed data.
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Arxiv
1d
52
Image Credit: Arxiv
Enhancing Variational Autoencoders with Smooth Robust Latent Encoding
- Variational Autoencoders (VAEs) have been enhanced with Smooth Robust Latent VAE (SRL-VAE) framework.
- SRL-VAE introduces adversarial training to improve generation quality and robustness.
- By smoothing the latent space via adversarial perturbations, SRL-VAE promotes generalizable representations.
- SRL-VAE improves both image robustness and fidelity with minimal computational overhead.
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