Zoneout
Algorithms AIMech:Zoneout DRAFT
A recurrent-network regularization procedure that randomly preserves selected state components at their previous values instead of applying their proposed updates during training.
Identity Notes & Concept Scope
Zoneout is a stochastic transition-modification procedure, not a particular RNN architecture or trained sequence model. Its identity branch retains previous activations; it differs from setting recurrent states to zero, dropping only input-gate contributions, fixed periodic update schedules and always skipping an entire recurrent layer. LSTM cells and hidden states can have separate preservation probabilities and masks. No normalized identity match was found across all 442 pinned AIO labels/scoped synonyms and 276 preceding native IDs/names/aliases. Full-checkout fixed-string searches included ignored and hidden files in worktree and main, returning 5 and 5 lines respectively. Every text hit was a prior candidate lead or a closely related variant lead. Nine exact AIO CLASS nodes with scoped synonyms/comments and outgoing edges and three native neighboring mechanisms were inspected. Absence is bounded to this snapshot and semantic neighborhood.
Claims & Grounded Evidence
1 Claim
Mechanism
#method
At a recurrent timestep, form the model’s proposed next-state values and use a Bernoulli selector for each targeted component to choose between that proposal and its previous value. Defining z as the preservation probability, the update has the form mask times previous state plus one-minus-mask times proposed state, with a mask value of1 selecting preservation. This introduces direct identity paths through time for the preserved components. The source’s LSTM account usually uses separate selectors for memory cells and hidden states and also studies shared selectors. At testing, replace the random selector by its expectation, producing a weighted combination of previous and proposed values at each step. Selection probabilities, targeted state components and the underlying recurrent transition are configured choices; the procedure itself does not require a new learned gate.
⚠️ Curated Limitations & Negative Results
- The generic operator formula in Section3.1 uses d for selecting the normal transition, whereas the LSTM formulas in Section3.2 use d for selecting preservation. The record defines its own preservation convention explicitly; those source masks are complements, not interchangeable symbols.
- The printed LSTM hidden-state proposal uses the unzoned cell-update expression inside tanh. An implementation that instead uses the already-mixed cell state can differ when cell and hidden selectors differ. This general method record does not certify all LSTM code as equivalent or invent an uninspected implementation rule.
- Replacing selectors by expectations gives a deterministic local mixture; nonlinear recurrence means it is not generally the exact ensemble-averaged state or output over complete stochastic trajectories. Preserved components pass their old values directly, but this does not guarantee that the entire network avoids vanishing/exploding gradients or processes every input in every component.
- Very high preservation can suppress adaptation to new inputs. State/cell probabilities, sharing and other regularizers matter; the source’s task-specific results and conjectures do not establish universal improvement. No exact trained-model replication, parameter sweep or author-code execution is asserted.
Artificial Intelligence Ontology (AIO) Grounding
Mapped AIO Classes
| AIO Target Class | Mapping Relation | Mapping Rationale |
|---|---|---|
AIO:RecurrentNeuralNetwork
Recurrent Neural Network
|
related_match | Zoneout modifies a recurrent state transition. The AIO target denotes the broader network architecture category, not this stochastic regularization algorithm. |
AIO:TrainingStrategy
TrainingStrategy
|
related_match | Zoneout supplies training-time stochastic state preservation and a corresponding inference rule. The target mixes preprocessing and broad learning strategies, so this is a procedure-to-training association rather than an asserted subclass. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
method-source
|
David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Aaron Courville, Christopher Pal
|
primary paper
full_text
|
ID: arXiv:1606.01305v4
sha256:f82391469757…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-11T12:35:00Z
Updated: 2026-10-11T12:35:00Z
Source File:
data/curated/Zoneout.yaml