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Built by researchers, for researchers

The Word & LaTeX experience, built for research

Migrate instantly. Import your manuscript and start collaborating in seconds. Our editor offers full Word and LaTeX support, keeping 100% of your formatting perfectly intact.

Anaphora—our state-of-the-art model for academic writing.

Ask Anaphora…

Sure! I found 3 relevant papers in PubMed and 2 in ResearchGate…

Help me address comment #4

Tap into real-time intelligence across 100M+ sources to resolve comments, manage tasks, and find citations.

Intelligent Merge

Collaborate without fear. Anaphora understands what you're writing, not just where. It intelligently combines all co-author edits automatically, eliminating conflicts and broken documents.

Journal Templates

Submit to any journal with ease. Use one of our templates to auto-format your manuscript to fit journal guidelines.

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Confirm the formula for the position-wise feed-forward network

Expand the section on positional encodings to provide more intuition behind the choice of sine and cosine functions

Add a brief note on why d_k and d_v were set to 642

Verify the O(1) sequential operations claim for self-attention in Table 11

Jon Porchet

Confirm the formula for the position-wise feed-forward network

Expand the section on positional encodings to provide more intuition behind the choice of sine and cosine functions

Add a brief note on why d_k and d_v were set to 642

Verify the O(1) sequential operations claim for self-attention in Table 11

Jon Porchet

Verify the 3.5-day training time for the "big" model.

Confirm the P_drop rate of 0.1 for the base model.

Explain intuition behind the learning rate formula & clarify the purpose of warmup_steps before the inverse square root decay begins.

Check the WMT 2014 EN-DE training set size (4.5M pairs).

Saimon Kurō

Verify the 3.5-day training time for the "big" model.

Confirm the P_drop rate of 0.1 for the base model.

Explain intuition behind the learning rate formula & clarify the purpose of warmup_steps before the inverse square root decay begins.

Check the WMT 2014 EN-DE training set size (4.5M pairs).

Saimon Kurō

Clarify the purpose of the decoder's self-attention maskin and explicitly state that the mask prevents positions from attending to subsequent positions.

Note the use of byte-pair encoding for the EN-DE task.

Confirm the parsing results for the WSJ-only 4-layer model.

Check the beam size and length penalty (alpha=0.6) used for translation.

Parbir Pattabiram

Clarify the purpose of the decoder's self-attention maskin and explicitly state that the mask prevents positions from attending to subsequent positions.

Note the use of byte-pair encoding for the EN-DE task.

Confirm the parsing results for the WSJ-only 4-layer model.

Check the beam size and length penalty (alpha=0.6) used for translation.

Parbir Pattabiram

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Confirm the formula for the position-wise feed-forward network

Expand the section on positional encodings to provide more intuition behind the choice of sine and cosine functions

Add a brief note on why d_k and d_v were set to 642

Verify the O(1) sequential operations claim for self-attention in Table 11

Jon Porchet

Verify the 3.5-day training time for the "big" model.

Confirm the P_drop rate of 0.1 for the base model.

Explain intuition behind the learning rate formula & clarify the purpose of warmup_steps before the inverse square root decay begins.

Check the WMT 2014 EN-DE training set size (4.5M pairs).

Saimon Kurō

Clarify the purpose of the decoder's self-attention maskin and explicitly state that the mask prevents positions from attending to subsequent positions.

Note the use of byte-pair encoding for the EN-DE task.

Confirm the parsing results for the WSJ-only 4-layer model.

Check the beam size and length penalty (alpha=0.6) used for translation.