papers - ClearSignal

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Academic papers

NOTES: Can LLMs truly reason?

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NOTES: Can LLMs truly reason?

Can LLMs actually reason, or are they just “probabilistic pattern matchers” ? This paper (GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models. by Mirzadeh et al.) attempts to answer that question. Motivation: * GSM8K benchmark is widely used to assess the mathematical reasoning of models on grade-school-level questions. * While

NOTES: Retrieval-Augmented Generation for Large Language Models

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NOTES: Retrieval-Augmented Generation for Large Language Models

Here is a short compilation of bullet points gathered while reading the paper " Retrieval-Augmented Generation for Large Language Models: A Survey". The authors did a reasonably good job summarizing the landscape of current RAG systems.

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Yolo-X Notes

The main motivation behind YOLOX was to update the YOLO series with the recent advancements at the time, particularly anchor-free detection.

Multi-task Loss

Multi-task Loss

This is a short review of the paper titled "Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics" by Kendall et al, 2018 .

PolyLoss : A new framework for loss functions

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PolyLoss : A new framework for loss functions

Original Paper: [https://arxiv.org/pdf/2204.12511.pdf] Authors: Zhaoqi Leng, Mingxing Tan , Chenxi Liu , Ekin Dogus Cubuk , Xiaojie Shi, Shuyang Cheng ,Dragomir Anguelov [Waymo And Google] Summary Authors propose a new framework of loss functions, motivated by the Taylor series expansion of commonly used functions like cross entropy

Short NLP Overview!

Short NLP Overview!

**Natural language processing (NLP)** is a branch of science sitting at the intersection of computer science, artificial intelligence, and computational linguistics. To put it simply, it gives computers the ability to read, understand and generate text and speech just like we do

EfficientNet (V1 & V2) - A smart heuristic

EfficientNet (V1 & V2) - A smart heuristic

EfficientNet tries to come up with a smart heuristic to scale a CNN, relating resolution, width, and depth of a CNN.

Semantic Segmentation - DeepLab V3+

Semantic Segmentation - DeepLab V3+

Semantic Segmentation Semantic segmentation involves partitioning/marking regions in the image belonging to different objects/classes. Deep learning methods have made a remarkable improvement in this field within the past few years. This short article summarises DeepLab V3+, an elegant extension of DeepLab v3 proposed by the same authors (Chen