Search: Softmax - ai.jp.net

research #softmax 📝 BlogAnalyzed: Jan 10, 2026 05:39

Softmax Implementation: A Deep Dive into Numerical Stability

Published:Jan 7, 2026 04:31

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1 min read

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MarkTechPost

Analysis

The article hints at a practical problem in deep learning – numerical instability when implementing Softmax. While introducing the necessity of Softmax, it would be more insightful to provide the explicit mathematical challenges and optimization techniques upfront, instead of relying on the reader's prior knowledge. The value lies in providing code and discussing workarounds for potential overflow issues, especially considering the wide use of this function.

Key Takeaways

•Softmax function converts raw scores to probability distributions.
•Numerical instability can occur during Softmax implementation.
•Article likely focuses on techniques to avoid overflow issues.

Reference

“Softmax takes the raw, unbounded scores produced by a neural network and transforms them into a well-defined probability distribution...”

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research #mlp 📝 BlogAnalyzed: Jan 5, 2026 08:19

Implementing a Multilayer Perceptron for MNIST Classification

Published:Jan 5, 2026 06:13

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1 min read

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Qiita ML

Analysis

The article focuses on implementing a Multilayer Perceptron (MLP) for MNIST classification, building upon a previous article on logistic regression. While practical implementation is valuable, the article's impact is limited without discussing optimization techniques, regularization, or comparative performance analysis against other models. A deeper dive into hyperparameter tuning and its effect on accuracy would significantly enhance the article's educational value.

Key Takeaways

•The article implements a Multilayer Perceptron (MLP).
•The task is MNIST handwritten digit classification.
•It builds upon a previous logistic regression implementation.

Reference

“前回こちらでロジスティック回帰（およびソフトマックス回帰）でMNISTの0から9までの手書き数字の画像データセットを分類する記事を書きました。”

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research #classification 📝 BlogAnalyzed: Jan 4, 2026 13:03

MNIST Classification with Logistic Regression: A Foundational Approach

Published:Jan 4, 2026 12:57

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1 min read

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Qiita ML

Analysis

The article likely covers a basic implementation of logistic regression for MNIST, which is a good starting point for understanding classification but may not reflect state-of-the-art performance. A deeper analysis would involve discussing limitations of logistic regression for complex image data and potential improvements using more advanced techniques. The business value lies in its educational use for training new ML engineers.

Key Takeaways

•MNIST is a standard dataset for handwritten digit recognition.
•Logistic regression can be used as a baseline model for MNIST classification.
•The article likely provides a basic introduction to machine learning classification.

Reference

“MNIST（エムニスト）は、0から9までの手書き数字の画像データセットです。”

Permalink Qiita ML

research #llm 📝 BlogAnalyzed: Jan 3, 2026 15:15

Focal Loss for LLMs: An Untapped Potential or a Hidden Pitfall?

Published:Jan 3, 2026 15:05

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1 min read

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r/MachineLearning

Analysis

The post raises a valid question about the applicability of focal loss in LLM training, given the inherent class imbalance in next-token prediction. While focal loss could potentially improve performance on rare tokens, its impact on overall perplexity and the computational cost need careful consideration. Further research is needed to determine its effectiveness compared to existing techniques like label smoothing or hierarchical softmax.

Key Takeaways

•Focal loss is designed to address class imbalance by focusing on hard examples.
•LLM training involves predicting the next token, which can be viewed as a highly imbalanced classification task.
•The effectiveness of focal loss in LLM pretraining remains largely unexplored.

Reference

“Now i have been thinking that LLM models based on the transformer architecture are essentially an overglorified classifier during training (forced prediction of the next token at every step).”

Permalink r/MachineLearning

Research Paper #Large Language Models, Conformal Prediction, Uncertainty Quantification 🔬 ResearchAnalyzed: Jan 3, 2026 16:22

Conformal Prediction for LLM Next-Token Prediction

Published:Dec 27, 2025 19:08

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1 min read

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ArXiv

Analysis

This paper addresses the critical need for uncertainty quantification in large language models (LLMs), particularly in high-stakes applications. It highlights the limitations of standard softmax probabilities and proposes a novel approach, Vocabulary-Aware Conformal Prediction (VACP), to improve the informativeness of prediction sets while maintaining coverage guarantees. The core contribution lies in balancing coverage accuracy with prediction set efficiency, a crucial aspect for practical deployment. The paper's focus on a practical problem and the demonstration of significant improvements in set size make it valuable.

Key Takeaways

•Addresses the problem of poorly calibrated probabilities in LLMs.
•Proposes Vocabulary-Aware Conformal Prediction (VACP) to improve prediction set efficiency.
•Demonstrates significant reduction in prediction set size while maintaining coverage guarantees.
•Provides a practical solution for uncertainty quantification in LLMs.

Reference

“VACP achieves 89.7 percent empirical coverage (90 percent target) while reducing the mean prediction set size from 847 tokens to 4.3 tokens -- a 197x improvement in efficiency.”

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Research #llm 📝 BlogAnalyzed: Dec 25, 2025 22:26

[P] The Story Of Topcat (So Far)

Published:Dec 24, 2025 16:41

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1 min read

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r/MachineLearning

Analysis

This post from r/MachineLearning details a personal journey in AI research, specifically focusing on alternative activation functions to softmax. The author shares experiences with LSTM modifications and the impact of the Golden Ratio on tanh activation. While the findings are presented as somewhat unreliable and not consistently beneficial, the author seeks feedback on the potential merit of publishing or continuing the project. The post highlights the challenges of AI research, where many ideas don't pan out or lack consistent performance improvements. It also touches on the evolving landscape of AI, with transformers superseding LSTMs.

Key Takeaways

•Exploration of alternative activation functions in neural networks.
•Challenges in achieving consistent performance improvements in AI research.
•The rapid evolution of AI architectures (LSTMs vs. Transformers).

Reference

“A story about my long-running attempt to develop an output activation function better than softmax.”

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 10:46

BLASST: Dynamic BLocked Attention Sparsity via Softmax Thresholding

Published:Dec 12, 2025 23:30

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1 min read

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ArXiv

Analysis

This article introduces BLASST, a method for achieving dynamic blocked attention sparsity using softmax thresholding. The focus is on improving the efficiency of attention mechanisms in large language models (LLMs). The approach likely aims to reduce computational costs by selectively activating attention weights. Further details on the specific implementation, performance gains, and limitations would be needed for a complete analysis.

Key Takeaways

Reference

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Research #LLM 🔬 ResearchAnalyzed: Jan 10, 2026 11:40

Softmax as Linear Attention in Large Prompts: A Measure-Based Analysis

Published:Dec 12, 2025 18:54

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1 min read

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ArXiv

Analysis

This research paper explores the relationship between softmax and linear attention mechanisms within large language models, providing a measure-based perspective. It likely investigates performance characteristics and potential optimizations in the context of large prompt inputs.

Key Takeaways

•Investigates the behavior of softmax and linear attention with large prompts.
•Employs a measure-based analysis approach.
•Potentially reveals insights for model optimization in large-prompt scenarios.

Reference

“The paper focuses on the relationship between softmax and linear attention in the large-prompt regime.”

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 08:50

Universal Adversarial Suffixes Using Calibrated Gumbel-Softmax Relaxation

Published:Dec 9, 2025 00:03

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1 min read

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ArXiv

Analysis

This article likely presents a novel approach to generating adversarial suffixes for large language models (LLMs). The use of Gumbel-Softmax relaxation suggests an attempt to make the suffix generation process more robust and potentially more effective at fooling the models. The term "calibrated" implies an effort to improve the reliability and predictability of the adversarial attacks. The source being ArXiv indicates this is a research paper, likely detailing the methodology, experiments, and results.

Key Takeaways

•Focuses on adversarial attacks against LLMs.
•Employs Gumbel-Softmax relaxation for suffix generation.
•Aims to improve the robustness and effectiveness of attacks.
•Likely a research paper detailing a new method.

Reference

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Research #llm 📝 BlogAnalyzed: Dec 29, 2025 18:31

Transformers Need Glasses! - Analysis of LLM Limitations and Solutions

Published:Mar 8, 2025 22:49

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1 min read

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ML Street Talk Pod

Analysis

This article discusses the limitations of Transformer models, specifically their struggles with tasks like counting and copying long text strings. It highlights architectural bottlenecks and the challenges of maintaining information fidelity. The author, Federico Barbero, explains these issues are rooted in the transformer's design, drawing parallels to over-squashing in graph neural networks and the limitations of the softmax function. The article also mentions potential solutions, or "glasses," including input modifications and architectural tweaks to improve performance. The article is based on a podcast interview and a research paper.

Key Takeaways

•Transformers struggle with tasks requiring precise information retention, like counting and copying long text.
•Architectural limitations, including the softmax function, contribute to these failures.
•Potential solutions involve input modifications and architectural adjustments to improve performance.

Reference

“Federico Barbero explains how these issues are rooted in the transformer's design, drawing parallels to over-squashing in graph neural networks and detailing how the softmax function limits sharp decision-making.”

Permalink ML Street Talk Pod

Softmax Implementation: A Deep Dive into Numerical Stability

Analysis

Key Takeaways

Implementing a Multilayer Perceptron for MNIST Classification

Analysis

Key Takeaways

MNIST Classification with Logistic Regression: A Foundational Approach

Analysis

Key Takeaways

Focal Loss for LLMs: An Untapped Potential or a Hidden Pitfall?

Analysis

Key Takeaways

Conformal Prediction for LLM Next-Token Prediction

Analysis

Key Takeaways

[P] The Story Of Topcat (So Far)

Analysis

Key Takeaways

BLASST: Dynamic BLocked Attention Sparsity via Softmax Thresholding

Analysis

Key Takeaways

Softmax as Linear Attention in Large Prompts: A Measure-Based Analysis

Analysis

Key Takeaways

Universal Adversarial Suffixes Using Calibrated Gumbel-Softmax Relaxation

Analysis

Key Takeaways

Transformers Need Glasses! - Analysis of LLM Limitations and Solutions

Analysis

Key Takeaways

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