Lottery ticket hypothesis¶
In machine learning, the lottery ticket hypothesis is that artificial neural networks with random weights can contain a subnetwork which (entirely by chance) can be tuned to a similar performance as tuning the whole network.
Core Idea¶
Lottery ticket hypothesis is treated here as the recurring machine-learning pruning identity summarized by this source-grounded definition: In machine learning, the lottery ticket hypothesis is that artificial neural networks with random weights can contain a subnetwork which (entirely by chance) can be tuned to a similar performance as tuning the whole network. In machine learning, the lottery ticket hypothesis is that artificial neural networks with random weights can contain a subnetwork which (entirely by chance) can be tuned to a similar performance as tuning the whole network.
Scope of Application¶
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Documented setting. It was found that if instead of m \odot \theta0 , they re-sampled a different random initialization \theta0' , and used m \odot \theta0' instead, the trained f!\left(x ; m \odot.
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Subsequent work. Malach et al. proved a stronger version of the hypothesis, namely that a sufficiently overparameterized untuned network will typically contain a subnetwork that is already an approximation to the given goal.
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Subsequent work. A similar result has been proven for the special case of convolutional neural networks.
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Documented setting. In machine learning, the lottery ticket hypothesis is that artificial neural networks with random weights can contain a subnetwork which (entirely by chance) can be tuned to a similar performance as.
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Documented setting. Magnitude pruning: build a bitmask m by setting to 0 the lowest-magnitude weights in each layer (one-shot), or repeat train → prune across rounds to reach higher sparsity (iterative).
Clarity¶
A clear use of Lottery ticket hypothesis names the carrier, the operative relation, and the conditions under which the source treats the identity as present. The minimal definition is In machine learning, the lottery ticket hypothesis is that artificial neural networks with random weights can contain a subnetwork which (entirely by chance) can be tuned to a similar performance as tuning the whole network.
Manages Complexity¶
Lottery ticket hypothesis compresses multiple machine-learning pruning details into a stable diagnostic relation. The source shows both the central mechanism—magnitude pruning: build a bitmask m by setting to 0 the lowest-magnitude weights in each layer (one-shot), or repeat train → prune across rounds to reach higher sparsity (iterative).—and the practical consequence—reset surviving weights to initialization: use m \odot \theta0 as the starting point.
Abstract Reasoning¶
- Type the carrier. Identify the machine-learning pruning entities to which the claim applies.
- State the relation. Use the source-grounded identity: In machine learning, the lottery ticket hypothesis is that artificial neural networks with random weights can contain a subnetwork which (entirely by chance) can be tuned to a similar performance as tuning the whole network.
- Check operation and conditions. However, after training, these lottery tickets can be discovered by the pruning algorithm.
- Demand recognition evidence.
Knowledge Transfer¶
Within the home domain. Knowledge about Lottery ticket hypothesis transfers literally when a new case preserves the same carrier type, relation, and recognition test. It was found that if instead of m \odot \theta0 , they re-sampled a different random initialization \theta0' , and used m \odot \theta0' instead, the trained f!\left(x ; m \odot \theta0'\right) would perform much worse. Malach et al. proved a stronger version of the hypothesis, namely that a sufficiently overparameterized untuned network will typically contain a subnetwork that is.
Relationships to Other Abstractions¶
Current abstraction Lottery ticket hypothesis Domain-specific
Parents (1) — more general patterns this builds on
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Lottery ticket hypothesis is a kind of Scientific Hypothesis Domain-specific
It is a machine-learning hypothesis with experimental consequences.
Hierarchy path (1) — routes to 1 parentless root
- Lottery ticket hypothesis → Scientific Hypothesis → Falsifiability
Neighborhood in Abstraction Space¶
Lottery ticket hypothesis sits in a sparse region of the domain-specific corpus (91st percentile for distinctiveness): few abstractions share its structure, so a faithful description tends to retrieve it precisely.
Family — Combinatorial Optimization & Discrete Structures (31 abstractions)
Nearest neighbors
- Lottery mathematics — 0.81
- Gambling and information theory — 0.80
- Lottery paradox — 0.79
- Random compact set — 0.79
- Large width limits of neural networks — 0.79
Computed from structural-signature embeddings · 2026-10-08