Aziz et al. Paper Summaries

Machine Learning Engineer @ SoundCloud | Sharing My learnings About AI Research. ✨ Follow for updates. https://www.linkedin.com/in/mohamed-aziz-belaweid/

The hottest Substack posts of Aziz et al. Paper Summaries

And their main takeaways

Does ZEPHYR 7B Outperform 70B LLama-2 Chat?

491 implied HN points • 13 Dec 23
  1. Zephyr 7B model by HuggingFace outperforms LLama-2 Chat on MT-Bench dataset.
  2. Zephyr 7B uses techniques like Distilled Supervised Finetuning, AI Feedback, and Distilled Direct Policy Optimization for its training process.
  3. Zephyr 7B excels in alignment but may not surpass 70B LLama-2 Chat in terms of deep knowledge.

How To Create Automatic Music Playlists With RL?

58 implied HN points • 09 Sep 23
  1. Creating automatic music playlists using Reinforcement Learning (RL) helps personalize music recommendations based on user preferences and context.
  2. Current methods like Collaborative Filtering and Sequence Modeling have limitations in understanding user preferences and adapting to changing preferences over time.
  3. Reinforcement Learning (RL) offers a solution by training a user behavior model to predict user satisfaction with recommended tracks, leading to higher playlist personalization and user satisfaction.

Your Complete Guide to RCNN, Fast-RCNN, Faster-RCNN and Mask-RCNN

39 implied HN points • 24 Sep 23
  1. The RCNN family involves different architectures for object detection, each solving obstacles like huge time consumption and fixed algorithms.
  2. Fast-RCNN improves over RCNN by reducing classification time and introducing ROI pooling instead of SVM classifiers.
  3. Faster-RCNN further enhances Fast-RCNN by proposing a Region Proposal Network, reducing the reliance on Selective Search and allowing for shared computations within the network.

Reinforcement Learning With AI Feedback Explained!

39 implied HN points • 16 Sep 23
  1. Reinforcement learning with human feedback does not scale, as it requires expert human labelers and is time-consuming and expensive.
  2. Using another large language model (LLM) for feedback can replace human feedback in reinforcement learning, but some challenges remain in prompt design and model size.
  3. Evaluation metrics like AI labeler alignment and pairwise accuracy help compare the performance of models trained with human feedback versus AI feedback.

Cloze-Driven Pretraining of Self-Attention Networks Summary

19 implied HN points • 29 Sep 23
  1. Introduces a new pre-training method for transformers improving language understanding
  2. Significant performance gains on GLUE tasks and new SOTA in NER and parsing
  3. Model architecture involves two self-attention towers, block structures, and fine-tuning details
Get a weekly roundup of the best Substack posts, by hacker news affinity:

How Speculative Sampling Can Increase Your LLM's Inference Speed!

3 HN points • 07 Oct 23
  1. LLMs are slow due to autoregressive nature, memory bandwidth issues, and communication overhead.
  2. Speculative sampling involves using a smaller 'draft model' to generate tokens quickly and pass them to a larger 'target model'.
  3. This method helps speed up LLM inference by skipping forward passes on easy-to-predict tokens.

Coming soon

0 implied HN points • 05 Sep 23
  1. Aziz Belaweid is launching a Substack newsletter.
  2. The newsletter will feature paper summaries.
  3. Readers can subscribe to stay updated.