claim

active

claim:to-transform-a-function-in-time-or-in-space-apply-the-transform-point-wise-to-the-output-and-apply-the-inverse-transform-point-wise-to-the-input

To transform a function (in time or in space), apply the transform point-wise to the output, and apply the inverse transform point-wise to the input.

General principle for transforming functions that enables spatial transformation of GUIs without reimplementation; connects to Pan's hyperfilters.

Source paper

extracted_from

Genuinely Functional User Interfaces

Courtney, Antony · Elliott, Conal

Neighborhood — ranked by edge-count

Findings (1)

finding

Fruit provides continuous zooming capability without toolkit restructuring, demonstrated by minipb example with 0.5 uniform scaling.
supports
Empirical demonstration that Fruit's denotational model enables novel features like continuous zooming without reimplementation.

Communities (2)

community

Design principles for care-centered systems
members_of
Cross-scale frameworks linking spatial patterns, diagrams, and simplicity as expressions of care in design.
Explicit state & compositional GUI design
members_of
Systems prioritizing visible program state and modular component composition over hidden complexity, exemplified by Oberon (1986-1989) and Fruit toolkit approaches.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Different introspective tasks may preferentially use different path distributions in the transformer.claim0.748
Interpretive claim connecting exponential path combinatorics to Lindsey's layer-dependent findings.
Transformers learn in-context by gradient descent, functioning as mesa-optimizers that learn internal models in real timefinding0.740
Evidence that in-context learning is not mere pattern matching but genuine optimization, relevant to applying the thesis to inference
How Does Recognition Of Nonself And Emptiness Transformquestion0.738
probably helps not only with faithful reconstruction but also creates interference patterns that encode nuanced information about the deltas and convergences between states.quote0.737
Key quote connecting path redundancy to interferometric information encoding.
Transformers use an anti-Markovian solution that recomputes relevant numeric information at each step in the Multi-Object taskclaim0.736
Prior finding from Grant et al. 2025 used to interpret low MAS IIA for GRU-Transformer hidden state comparisons.
Producing the exact shape from personal vision brings the thing to lifeclaim0.734
The styrofoam method allows the exact shape felt right to be produced, and that personal exactness yields spiritual quality.
Transformers are recurrent through autoregression because K/V stream provides horizontal information flow across positions.claim0.733
Claim formalizing the Anima Labs idea that transformers are effectively recurrent due to K/V stream.
A Mathematical Framework for Transformer Circuits (Elhage et al., 2021)concept0.729
Foundational mechanistic interpretability paper on transformer circuit analysis