Papersy
Read 10 papers in the time it takes to read one.
Papersy reads the papers so you can focus on the research. Paste any paper, get a summary, ask questions.
Researchers drown in papers.
We surface what matters.
Before
47 unread papers
- A Novel Transformer Architecture for Multi-Modal Learning in...
- Revisiting Attention Mechanisms: An Empirical Study Across...
- Scalable Diffusion Models with State Space Representations...
- Towards Efficient In-Context Learning: Benchmarks and...
- Cross-Lingual Transfer in Low-Resource Scenarios Using...
- Self-Supervised Pre-Training for Biomedical Text Mining...
With Papersy
Summaries and highlights
A Novel Transformer Architecture for...
- Proposes a new attention variant that cuts memory use by 40%
- Outperforms baselines on 6 of 8 benchmarks
- Key limitation: only tested on English-language datasets
See it in action.
Paste a paper. Get clarity.
1: Drop in a paper
Paste a URL arxiv.org/pdf/1706.03762
or
Upload a PDF any research paper
2: Get the key points
Attention Is All You Need: Vaswani et al., 2017
- Introduces the Transformer: a model built entirely on attention, no recurrence or convolution
- Achieves state-of-the-art on English-German and English-French translation
- Enables much greater parallelization than RNN-based approaches
3: Ask anything about it
Q What are the limitations of this approach?
A The paper notes that Transformers may struggle with very long sequences due to the quadratic cost of self-attention. The authors acknowledge this is an area for future work.
4: Your data stays yours
Never is your paper be used to train our model.