# Famous Deep Learning Papers List by David Bau

> Published 2026-08-27 · https://www.promptzone.com/samir_hansen/famous-deep-learning-papers-list-by-david-bau-2lgg

David Bau published a curated list of famous deep learning papers at [papers.baulab.info](https://papers.baulab.info/). The post appeared on Hacker News and collected 30 points with 5 comments.

The resource organizes landmark papers by topic and impact rather than chronology. Readers can scan sections on foundational architectures, optimization methods, and scaling laws without assembling their own bibliography.

## What the Collection Covers

The list groups papers into categories such as early neural networks, convolutional models, transformers, and generative techniques. Each entry includes the title, authors, and year, with direct links to the original publications.

No summaries or commentary appear on the page itself. Users must open the linked papers to read abstracts or full text.

## How to Access and Use It

Visit [papers.baulab.info](https://papers.baulab.info/) directly in any browser. The page loads as a single static list with no login or download required.

Practitioners typically open the page alongside arXiv searches or PDF readers. Some copy the listed titles into reference managers such as Zotero or Mendeley for later retrieval.

## Community Reaction on Hacker News

The thread received 30 points and 5 comments. Participants noted the list's focus on high-citation works and its utility for students building reading lists.

One comment questioned the absence of recent scaling papers. Another suggested adding links to official implementations where available.

## Pros and Cons

- Pros: Single-page format, no paywalls, clear categorization, free access.
- Cons: No abstracts or code links, limited to pre-2023 selections in some sections, no search function.

## Alternatives and Comparisons

| Resource | Papers Listed | Updates | Code Links | Format |
|----------|---------------|---------|------------|--------|
| papers.baulab.info | ~80 | Static | No | Web list |
| arXiv Sanity | Thousands | Daily | Partial | Search engine |
| Papers With Code | 100k+ | Weekly | Yes | Leaderboards |

The Bau list is shorter and more selective than Papers With Code. It lacks the implementation tracking that arXiv Sanity provides.

## Who Should Use This

Graduate students assembling a core reading list benefit most. Researchers refreshing knowledge of seminal works before new projects also find it efficient. Practitioners seeking the latest methods or production code should start elsewhere.

## Bottom Line / Verdict

The page delivers a compact, no-frills index of influential deep learning papers that matches the needs of readers who already know which titles matter.

David Bau's list is likely to remain a quick reference for training cohorts and literature reviews even as new papers appear daily.