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Medium
16h
330
Revision Research on Data Pruning part3(Machine Learning)
- Data pruning is an attractive field of research due to the increasing size of datasets used for training neural networks.
- Most current data pruning algorithms have limitations in preserving accuracy compared to models trained on the full data.
- This paper explores the application of data pruning with knowledge distillation (KD) when training on a pruned subset.
- Using KD, simple random pruning is shown to be comparable or superior to sophisticated pruning methods across all pruning regimes.
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Medium
16h
249
Revision Research on Data Pruning part2(Machine Learning)
- Data pruning is essential to mitigate costs in deep learning models by removing redundant or uninformative samples.
- Existing data pruning algorithms can produce highly biased classifiers.
- Random data pruning with appropriate class ratios has the potential to improve worst-class performance.
- A "fairness-aware" approach to pruning is proposed and empirically demonstrated to improve robustness without significant drop in average performance.
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Medium
16h
11
Different Distillation methods in Machine Learning research part4
- In multi-modal learning, influential modalities are crucial for high accuracy classification/segmentation.
- A novel approach called Meta-learned Cross-modal Knowledge Distillation (MCKD) is proposed to address this issue.
- MCKD dynamically estimates the importance weight of each modality through meta-learning.
- Experimental results show that MCKD outperforms current state-of-the-art models in multiple tasks.
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Medium
16h
44
Different Distillation methods in Machine Learning research part3
- Researchers propose GLiRA, a distillation-guided approach to membership inference attacks on black-box neural networks.
- The study explores the connection between vulnerability to membership inference attacks and distillation-based functionality stealing attacks.
- Knowledge distillation significantly improves the efficiency of likelihood ratio membership inference attacks, especially in the black-box setting.
- The proposed method outperforms the current state-of-the-art membership inference attacks in the black-box setting across multiple image classification datasets and models.
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Medium
16h
242
Different Distillation methods in Machine Learning research part2
- A curriculum-based dataset distillation framework has been introduced in machine learning research.
- The framework distills synthetic images in a curriculum-based approach, transitioning from simple to complex.
- Curriculum evaluation is incorporated to address the issue of homogeneous and simplistic image generation.
- Adversarial optimization is used to improve the representativeness and robustness of the distilled images.
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Medium
16h
341
Research on Stochastic Bridge part6(Machine Learning 2024)
- Researchers have developed a sampling method to quantify rare events in stochastic processes.
- The method constructs stochastic bridges, which are trajectories with fixed start and end points.
- By carefully choosing and weighting these bridges, the method focuses processing power on rare events while preserving their statistics.
- The method is compared to the Wentzel-Kramers-Brillouin (WKB) optimal paths and shows accuracy when noise levels are low.
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Medium
16h
268
Image Credit: Medium
What is the Difference Between Correlation and Causation?
- Correlation measures the relationship between two variables, indicating if they move together or in opposite directions.
- Causation goes beyond correlation, showing that one variable directly influences another.
- Correlation is a starting point for investigating causation, but further analysis is necessary.
- Understanding the difference between correlation and causation is important to avoid erroneous conclusions.
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Medium
17h
275
Research on Stochastic Bridge part4(Machine Learning 2024)
- Parameter-efficient tuning methods (PETs) optimize large pre-trained language models (PLMs).
- PETs focus on optimizing the loss function of the output state, neglecting the running cost dependent on intermediate states.
- This study proposes using latent stochastic bridges as regularizers to model and optimize the intermediate states.
- The effectiveness and generality of this regularization approach are demonstrated across different tasks, PLMs, and PETs.
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Marktechpost
17h
73
Machine Learning Revolutionizes Path Loss Modeling with Simplified Features
- Accurate propagation modeling is crucial for wireless communications.
- Traditional path loss models show degraded accuracy in non-line-of-sight scenarios.
- Researchers explore the use of machine learning-based modeling and simplified features.
- The results show that simple scalar features can train accurate propagation models.
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Medium
18h
177
Comparing DiPhyx and dxflow with Databricks, Google Colab, and SageMaker
- DiPhyx is a platform for scientific computing workflows, emphasizing reproducibility and collaboration.
- dxflow focuses on managing and optimizing scientific computing workflows across different environments.
- Databricks is a unified analytics platform with data processing capabilities.
- Google Colab and SageMaker are also popular platforms in the data science and machine learning space.
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Medium
18h
55
Image Credit: Medium
Why Apache Spark Outperforms Hadoop MapReduce for Iterative Algorithms
- Apache Spark outperforms Hadoop MapReduce for iterative algorithms in big data processing.
- MapReduce involves significant read and write operations to disk, which can be slow and inefficient for iterative algorithms.
- Spark leverages in-memory computing, avoiding disk I/O bottleneck and providing faster processing for iterative algorithms.
- Spark's in-memory computing capability makes it a game-changer for efficient processing of big data with iterative algorithms.
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Medium
19h
222
Image Credit: Medium
Introduction To Robotics
- Robotics involves the design, construction and operations of robots.
- Key components of robotics include sensors, actuators, control systems, power supply, end effectors, and software.
- The history of robotics dates back to the 1920s, and it has evolved significantly over the years.
- Robotics is a field that combines engineering, science, and technology to create machines that enhance human capabilities and improve efficiency in various industries.
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Medium
21h
323
Image Credit: Medium
Gated Exponential Linear Unit
- The Gated Exponential Linear Unit (GELU) is a sophisticated activation function used in neural networks.
- GELU combines linear and non-linear operations, along with a gating mechanism, to enhance learning capabilities.
- It introduces non-linearity and allows the network to learn complex relationships between inputs and outputs.
- GELU is a powerful activation function with smooth nature and adaptive filtering capabilities, contributing to its success in deep learning applications.
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Marktechpost
21h
44
This AI Paper Introduces Rational Transfer Function: Advancing Sequence Modeling with FFT Techniques
- Researchers have introduced the Rational Transfer Function (RTF) approach for efficient sequence modeling.
- RTF leverages transfer functions and Fast Fourier Transform (FFT) techniques.
- The RTF approach eliminates the need for memory-intensive state-space representations.
- RTF demonstrated improved training speed and accuracy across various benchmarks and tasks.
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Marktechpost
22h
138
Enhancing Graph Classification with Edge-Node Attention-based Differentiable Pooling and Multi-Distance Graph Neural Networks GNNs
- Researchers have developed a new hierarchical pooling method for GNNs called Edge-Node Attention-based Differentiable Pooling (ENADPool).
- ENADPool uses hard clustering and attention mechanisms to compress node features and edge strengths, improving graph classification performance.
- They also introduce a Multi-distance GNN (MD-GNN) model to reduce over-smoothing and enhance graph representation.
- ENADPool and MD-GNN outperform other graph deep learning methods in benchmark datasets.
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