Gradient-Based Learning Applied to Document Recognition.¶
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278-2324.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Convolution
- Neural computation. A neuron computes a weighted sum of its inputs over time; convolutional networks replace per-position parameters with a shared kernel to encode translation invariance, and early visual receptive fields are well modelled as oriented convolutions.
This sourceConvolutional networks share a fixed kernel across positions to encode translation invariance, modeled on local receptive fields.
- Neural computation. A neuron computes a weighted sum of its inputs over time; convolutional networks replace per-position parameters with a shared kernel to encode translation invariance, and early visual receptive fields are well modelled as oriented convolutions.
- Neighborhood
- The neuroscience notion of a receptive field as a bounded input neighborhood transferred into convolutional networks and then into graph neural networks, the same primitive carrying through three substrates with the same role.
This sourceEstablishes convolutional receptive fields — bounded input neighborhoods — carrying the receptive-field primitive from neuroscience into neural networks.
- The neuroscience notion of a receptive field as a bounded input neighborhood transferred into convolutional networks and then into graph neural networks, the same primitive carrying through three substrates with the same role.
- Receptive Field
- Receptive field licenses several characteristic moves. Coverage analysis: given the units and their fields, compute the union (covered region) and the complement (gaps), where gaps predict silence and overlaps predict contention. Tiling design: choose unit count and field size under a budget — many small fields give high resolution at high cost, few large fields are cheap but blur — a trade-off that is universal across substrates. Hierarchical composition: stacking units whose fields pool over many lower-level fields produces larger effective fields with selectivity for higher-level features, the move shared by convolutional depth, organisational hierarchy, regulatory federation, and visual cortex.
This sourceConvolutional architecture with local receptive fields drawn directly from visual neuroscience; each unit responds to a local input patch and deeper layers pool over many lower fields to build larger effective fields.
- Receptive field licenses several characteristic moves. Coverage analysis: given the units and their fields, compute the union (covered region) and the complement (gaps), where gaps predict silence and overlaps predict contention. Tiling design: choose unit count and field size under a budget — many small fields give high resolution at high cost, few large fields are cheap but blur — a trade-off that is universal across substrates. Hierarchical composition: stacking units whose fields pool over many lower-level fields produces larger effective fields with selectivity for higher-level features, the move shared by convolutional depth, organisational hierarchy, regulatory federation, and visual cortex.
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