Transformers Learn Faster with Semantic Focus | Paper Citation Record

Observation 62771866-b7fa-4835-8861-4c85f607bed9 · outbound

This paper cites Attention is all you need.

Transformers Learn Faster with Semantic Focus Attention is all you need

Reference 1

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Observation 22bb8517-1758-44a3-89d5-f49376359346 · outbound

This paper cites Are transformers universal approximators of sequence-to-sequence functions? In International Conference on Learning Representations, 2020 a.

Transformers Learn Faster with Semantic Focus Are transformers universal approximators of sequence-to-sequence functions? In International Conference on Learning Representations, 2020 a

Reference 2

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Observation 8e9223ce-1562-43b5-a3cb-18c05d591716 · outbound

This paper cites Efficient transformers: A survey.

Transformers Learn Faster with Semantic Focus Efficient transformers: A survey

Reference 3

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Observation 1af2cb21-31bf-4ab7-b069-681e4b50bdc6 · outbound

This paper cites Long range arena: A benchmark for efficient transformers.

Transformers Learn Faster with Semantic Focus Long range arena: A benchmark for efficient transformers

Reference 4

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Observation 59ba69a1-cbbe-4543-97ca-d997251586a1 · outbound

This paper cites Cognitive Mechanisms Associated with Auditory Sensory Gating.

Transformers Learn Faster with Semantic Focus Cognitive Mechanisms Associated with Auditory Sensory Gating

Reference 5

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Observation 44f6baf5-da5e-402a-83b1-a523095912f5 · outbound

This paper cites The Senses: A Comprehensive Reference.

Transformers Learn Faster with Semantic Focus The Senses: A Comprehensive Reference

Reference 6

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Observation 21ace225-81de-41d0-821b-fd63afaeb096 · outbound

This paper cites Sensory gating deficits in schizophrenia: new results.

Transformers Learn Faster with Semantic Focus Sensory gating deficits in schizophrenia: new results

Reference 7

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Observation f4952284-fd8e-4da6-b4c6-88f49606b297 · outbound

This paper cites The Consciousness Prior.

Transformers Learn Faster with Semantic Focus The Consciousness Prior

Reference 8

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Observation b2232304-32ec-42e8-a934-7a304b29b1c2 · outbound

This paper cites URL https://neurosymbolic.github.io/nsss2024.

Transformers Learn Faster with Semantic Focus URL https://neurosymbolic.github.io/nsss2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.845025Z

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Observation 14e85bea-67d9-43a4-a785-ab7f32ae9204 · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Transformers Learn Faster with Semantic Focus Neural Machine Translation by Jointly Learning to Align and Translate

Reference 10

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Observation a9fff3c4-0fbf-4b1b-a1f7-c3f9fc251544 · outbound

This paper cites Neural networks and the chomsky hierarchy.

Transformers Learn Faster with Semantic Focus Neural networks and the chomsky hierarchy

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.830872Z

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Observation a66a7ba5-854b-43ff-9bbe-e3ff87789bc5 · outbound

This paper cites O(n) connections are expressive enough: Universal approximability of sparse transformers.

Transformers Learn Faster with Semantic Focus O(n) connections are expressive enough: Universal approximability of sparse transformers

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.812327Z

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Observation 61af9724-2b54-4a43-8217-c53f99349493 · outbound

This paper cites Etc: Encoding long and structured inputs in transformers.

Transformers Learn Faster with Semantic Focus Etc: Encoding long and structured inputs in transformers

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.794142Z

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Observation 84ce2eca-690d-4bb7-a2aa-9ea0dff21b3f · outbound

This paper cites Big bird: Transformers for longer sequences.

Transformers Learn Faster with Semantic Focus Big bird: Transformers for longer sequences

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.778932Z

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Observation 32f6bc68-148f-4bae-a696-780ff6753dc3 · outbound

This paper cites Memory-efficient transformers via top-k attention.

Transformers Learn Faster with Semantic Focus Memory-efficient transformers via top-k attention

Reference 15

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Observation 2f42847e-789a-4545-ad74-dbd1c9a601cb · outbound

This paper cites ZETA : Leveraging z -order curves for efficient top- k attention.

Transformers Learn Faster with Semantic Focus ZETA : Leveraging z -order curves for efficient top- k attention

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.764128Z

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Observation 672ff6cb-90c5-4c82-99a3-f10cd42cd3a6 · outbound

This paper cites Algorithmic stability and generalization performance.

Transformers Learn Faster with Semantic Focus Algorithmic stability and generalization performance

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.750849Z

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Observation 8495391a-4fb7-4739-ac0c-aec66433f718 · outbound

This paper cites Train faster, generalize better: Stability of stochastic gradient descent.

Transformers Learn Faster with Semantic Focus Train faster, generalize better: Stability of stochastic gradient descent

Reference 18

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Observation 9bb6606d-3f55-472b-a431-4d0742abc47c · outbound

This paper cites Formal Algorithms for Transformers.

Transformers Learn Faster with Semantic Focus Formal Algorithms for Transformers

Reference 19

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Observation dd34a3bd-096a-4936-9e29-698e87b2df1d · outbound

This paper cites A survey of transformers.

Transformers Learn Faster with Semantic Focus A survey of transformers

Reference 20

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Observation c1bfa5c9-f0c9-4ec2-9862-5b35f64ee4ca · outbound

This paper cites Image transformer.

Transformers Learn Faster with Semantic Focus Image transformer

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.723660Z

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Observation 79b46b3c-40a9-4820-be5e-130bc2e701cb · outbound

This paper cites Blockwise self-attention for long document understanding.

Transformers Learn Faster with Semantic Focus Blockwise self-attention for long document understanding

Reference 22

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Observation f63968ca-efc8-419d-878a-ddb05d57f944 · outbound

This paper cites Longformer: The Long-Document Transformer.

Transformers Learn Faster with Semantic Focus Longformer: The Long-Document Transformer

Reference 23

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Observation 067581ba-98c3-4144-b6bb-576ff06f2877 · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Transformers Learn Faster with Semantic Focus Generating Long Sequences with Sparse Transformers

Reference 24

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Observation de423e8c-14cf-431b-bb13-0d340ddd288b · outbound

This paper cites Sparse sinkhorn attention.

Transformers Learn Faster with Semantic Focus Sparse sinkhorn attention

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.693446Z

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Observation 8cee7eee-810c-4efa-84d8-49e5f85f3321 · outbound

This paper cites Efficient content-based sparse attention with routing transformers.

Transformers Learn Faster with Semantic Focus Efficient content-based sparse attention with routing transformers

Reference 26

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Observation 2e7434e7-0ad7-45cb-b59f-e14f2167eae9 · outbound

This paper cites Reformer: The efficient transformer.

Transformers Learn Faster with Semantic Focus Reformer: The efficient transformer

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.678445Z

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Observation d84cf548-c9c4-4cf2-8365-00a069fe0a17 · outbound

This paper cites COGS : A compositional generalization challenge based on semantic interpretation.

Transformers Learn Faster with Semantic Focus COGS : A compositional generalization challenge based on semantic interpretation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.664182Z

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Observation cf06c895-63e9-4ecd-978e-e546c4c82dde · outbound

This paper cites Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks.

Transformers Learn Faster with Semantic Focus Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.649685Z

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Observation 47980f98-7844-48dd-9fd5-0af02175dc94 · outbound

This paper cites When can transformers ground and compose: Insights from compositional generalization benchmarks.

Transformers Learn Faster with Semantic Focus When can transformers ground and compose: Insights from compositional generalization benchmarks

Reference 30

Resolution: verified exact; doi, observed 2026-08-07T00:26:31.339636Z

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Observation 3ceab004-66f3-41e6-84d4-824a19b41134 · outbound

This paper cites The devil is in the detail: Simple tricks improve systematic generalization of transformers.

Transformers Learn Faster with Semantic Focus The devil is in the detail: Simple tricks improve systematic generalization of transformers

Reference 31

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Observation a13d6515-33e0-4e1d-91c8-7e541b43e13d · outbound

This paper cites Making transformers solve compositional tasks.

Transformers Learn Faster with Semantic Focus Making transformers solve compositional tasks

Reference 32

Resolution: verified exact; doi, observed 2026-08-07T00:26:31.312207Z

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Observation 2496269a-85e9-44eb-a5db-d03f7139143a · outbound

This paper cites Inducing transformer ' s compositional generalization ability via auxiliary sequence prediction tasks.

Transformers Learn Faster with Semantic Focus Inducing transformer ' s compositional generalization ability via auxiliary sequence prediction tasks

Reference 33

Resolution: verified exact; doi, observed 2026-08-07T00:26:31.295252Z

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This paper cites a rli, Ekin Aky \.

Transformers Learn Faster with Semantic Focus a rli, Ekin Aky \

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.635646Z

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Observation f5b2bfd4-f7c9-4872-a2ad-f00fdeacdc96 · outbound

This paper cites What formal languages can transformers express? a survey.

Transformers Learn Faster with Semantic Focus What formal languages can transformers express? a survey

Reference 35

Resolution: verified exact; doi, observed 2026-08-07T00:26:31.279003Z

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Observation 6020b2e6-aa1c-47ce-8d90-8afc314dd586 · outbound

This paper cites On the ability and limitations of transformers to recognize formal languages.

Transformers Learn Faster with Semantic Focus On the ability and limitations of transformers to recognize formal languages

Reference 36

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Observation 1604f60a-e988-4af0-8874-2c4f77626b61 · outbound

This paper cites Theoretical limitations of self-attention in neural sequence models.

Transformers Learn Faster with Semantic Focus Theoretical limitations of self-attention in neural sequence models

Reference 37

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Observation 2ece83c2-9f63-418c-b1d5-c8ae82a60982 · outbound

This paper cites Formal language recognition by hard attention transformers: Perspectives from circuit complexity.

Transformers Learn Faster with Semantic Focus Formal language recognition by hard attention transformers: Perspectives from circuit complexity

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.605490Z

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Observation bb1ab5b5-9b8c-400a-b686-8b77247e18c6 · outbound

This paper cites Saturated transformers are constant-depth threshold circuits.

Transformers Learn Faster with Semantic Focus Saturated transformers are constant-depth threshold circuits

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.589814Z

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Observation f3fe64ff-55f2-46e6-80d0-57bf72a7230a · outbound

This paper cites Overcoming a theoretical limitation of self-attention.

Transformers Learn Faster with Semantic Focus Overcoming a theoretical limitation of self-attention

Reference 40

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Observation 5f80651a-9042-41df-8507-c06510ced51d · outbound

This paper cites Tighter bounds on the expressivity of transformer encoders.

Transformers Learn Faster with Semantic Focus Tighter bounds on the expressivity of transformer encoders

Reference 41

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Observation e1a1abc0-80ea-4abc-a8b7-530e386ed594 · outbound

This paper cites Transformers as algorithms: Generalization and stability in in-context learning.

Transformers Learn Faster with Semantic Focus Transformers as algorithms: Generalization and stability in in-context learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.543429Z

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Observation af5cac0e-1cef-4924-a631-3d6f7a36699f · outbound

This paper cites Transformers learn in-context by gradient descent.

Transformers Learn Faster with Semantic Focus Transformers learn in-context by gradient descent

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.529452Z

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Observation bc5a2ec6-5e4e-4209-8bbe-586adb9acbe7 · outbound

This paper cites The emergence of clusters in self-attention dynamics.

Transformers Learn Faster with Semantic Focus The emergence of clusters in self-attention dynamics

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.514809Z

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Observation 4842f956-d2c0-4cd2-bf07-6a05324354e0 · outbound

This paper cites Transformers learn to implement preconditioned gradient descent for in-context learning.

Transformers Learn Faster with Semantic Focus Transformers learn to implement preconditioned gradient descent for in-context learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.495826Z

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Observation 0676e91a-49e1-44af-9ffe-d180a49e2d1e · outbound

This paper cites Trained transformers learn linear models in-context.

Transformers Learn Faster with Semantic Focus Trained transformers learn linear models in-context

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.480464Z

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Observation f1208b8e-f112-4011-bd87-dd3159dc5249 · outbound

This paper cites Why are adaptive methods good for attention models? Advances in Neural Information Processing Systems, 33: 0 15383--15393, 2020.

Transformers Learn Faster with Semantic Focus Why are adaptive methods good for attention models? Advances in Neural Information Processing Systems, 33: 0 15383--15393, 2020

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.464242Z

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Observation da9fc738-70f8-48b4-8259-7545c05251a4 · outbound

This paper cites Toward understanding why adam converges faster than SGD for transformers.

Transformers Learn Faster with Semantic Focus Toward understanding why adam converges faster than SGD for transformers

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.446017Z

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Observation c9f67186-e199-4d0c-9ad6-15fa02f759f1 · outbound

This paper cites How does adaptive optimization impact local neural network geometry? Advances in Neural Information Processing Systems, 36: 0 8305--8384, 2023.

Transformers Learn Faster with Semantic Focus How does adaptive optimization impact local neural network geometry? Advances in Neural Information Processing Systems, 36: 0 8305--8384, 2023

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.431099Z

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Observation dbc1b7cc-1d95-47c1-85fa-4b50bf0a234a · outbound

This paper cites Noise is not the main factor behind the gap between sgd and adam on transformers, but sign descent might be.

Transformers Learn Faster with Semantic Focus Noise is not the main factor behind the gap between sgd and adam on transformers, but sign descent might be

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.414833Z

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Observation 884fe00e-92a9-460e-848e-cb3bd47f188e · outbound

This paper cites Linear attention is (maybe) all you need (to understand transformer optimization).

Transformers Learn Faster with Semantic Focus Linear attention is (maybe) all you need (to understand transformer optimization)

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.399313Z

Source-reported events for the cited work

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Observation 0b632e26-88f9-40fd-b30c-2f0591d3f489 · outbound

This paper cites On the optimization and generalization of two-layer transformers with sign gradient descent.

Transformers Learn Faster with Semantic Focus On the optimization and generalization of two-layer transformers with sign gradient descent

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:26:33.383165Z

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Observation 7d1005fb-2019-4208-b0c1-d3163e3f2a97 · outbound

This paper cites Layer Normalization.

Transformers Learn Faster with Semantic Focus Layer Normalization

Reference 53

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Observation 46115e48-be86-45ce-b49a-e9ce4bdda4f9 · outbound

This paper cites Root mean square layer normalization.

Transformers Learn Faster with Semantic Focus Root mean square layer normalization

Reference 54

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Observation ee4e4e51-cd62-4925-9254-1f342c52ca72 · outbound

This paper cites BERT : Pre-training of deep bidirectional transformers for language understanding.

Transformers Learn Faster with Semantic Focus BERT : Pre-training of deep bidirectional transformers for language understanding

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T00:26:31.123158Z

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Observation d1ad7295-411a-42bf-9db6-42b3ff685421 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Transformers Learn Faster with Semantic Focus Gaussian Error Linear Units (GELUs)

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T00:26:31.128361Z

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Observation d521b3a9-96a1-4475-9a3a-d3942c18b279 · outbound

This paper cites Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).

Transformers Learn Faster with Semantic Focus Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Reference 57

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This paper cites Listops: A diagnostic dataset for latent tree learning.

Transformers Learn Faster with Semantic Focus Listops: A diagnostic dataset for latent tree learning

Reference 58

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Transformers Learn Faster with Semantic Focus Mish: A Self Regularized Non-Monotonic Activation Function

Reference 59

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Transformers Learn Faster with Semantic Focus Adam: A Method for Stochastic Optimization

Reference 60

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Transformers Learn Faster with Semantic Focus The Lipschitz Constant of Self-Attention

Reference 61

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Transformers Learn Faster with Semantic Focus Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection

Reference 62

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Transformers Learn Faster with Semantic Focus Visualizing the loss landscape of neural nets

Reference 63

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Transformers Learn Faster with Semantic Focus Never train from scratch: Fair comparison of long-sequence models requires data-driven priors

Reference 64

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This paper cites Learning overparameterized neural networks via stochastic gradient descent on structured data.

Transformers Learn Faster with Semantic Focus Learning overparameterized neural networks via stochastic gradient descent on structured data

Reference 65

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This paper cites A convergence theory for deep learning via over-parameterization.

Transformers Learn Faster with Semantic Focus A convergence theory for deep learning via over-parameterization

Reference 66

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Transformers Learn Faster with Semantic Focus Gradient descent optimizes over-parameterized deep relu networks

Reference 67

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This paper cites Convergence rates for the stochastic gradient descent method for non-convex objective functions.

Transformers Learn Faster with Semantic Focus Convergence rates for the stochastic gradient descent method for non-convex objective functions

Reference 68

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