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| <h1 class="title is-1 publication-title">AI Now Lives in Time: Temporal DenseNet</h1> |
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| <span class="author-block">Independent Research</span> |
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| <span>Code</span> |
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| <h2 class="title is-3">Abstract</h2> |
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| This network is a fully connected “temporal” architecture where each neuron sees not only the neurons in earlier layers of the current computation, but also all neurons from the previous computation step (tick). |
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| <p> |
| By spreading information across layers and time, no single neuron can memorize an input-output pair directly. Instead, the network learns patterns in a distributed way, naturally favoring generalization over memorization. This makes it useful for tasks where datasets are small or where overfitting is a risk, because the architecture itself prevents simple lookup-table memorization and encourages learning the underlying rules. |
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| <h2 class="title is-3">Temporal Accumulation Results</h2> |
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| The model demonstrates extreme precision on the XOR dataset by utilizing <strong>time ticks</strong> to accumulate state. |
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| <ul style="list-style-type: square; margin-left: 20px;"> |
| <li><strong>Final Loss:</strong> 0.000002</li> |
| <li><strong>Ticks:</strong> 3 iterative steps</li> |
| <li><strong>Structure:</strong> 8x8x8 Hidden layers</li> |
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| <tr><th>Epoch</th><th>MSE Loss</th></tr> |
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| <tbody> |
| <tr><td>200</td><td>0.000091</td></tr> |
| <tr><td>1000</td><td>0.000008</td></tr> |
| <tr><td>2000</td><td>0.000002</td></tr> |
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| <h3 class="title is-4">Final Prediction Accuracy</h3> |
| <p>The network achieves near-perfect separation for non-linear logic:</p> |
| <pre style="background: #232323; color: #00ff00; padding: 15px; border-radius: 8px;"> |
| Raw Predictions: |
| [[7.0460670e-04] [9.9793684e-01] [9.9922490e-01] [2.1156450e-03]] |
| Rounded: [0, 1, 1, 0]</pre> |
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| <h2 class="title is-3">PyTorch Implementation</h2> |
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| <p>The architecture uses <code>nn.ModuleList</code> to manage current tick layers ($U$) and previous tick recurrence ($W$).</p> |
| <pre style="background-color: #f5f5f5; padding: 20px; border-radius: 10px; border: 1px solid #ddd; font-size: 0.9em;"><code> |
| import torch |
| import torch.nn as nn |
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| class TemporalDenseNet(nn.Module): |
| def __init__(self, input_size, hidden_sizes, output_size): |
| super().__init__() |
| self.num_layers = len(hidden_sizes) |
| self.hidden_sizes = hidden_sizes |
| self.prev_concat_size = sum(hidden_sizes) |
| |
| # Current-tick linear layers U[i] |
| self.U = nn.ModuleList() |
| for i in range(self.num_layers): |
| in_size = input_size if i == 0 else sum(hidden_sizes[:i]) |
| self.U.append(nn.Linear(in_size, hidden_sizes[i])) |
| |
| # Previous-tick linear layers W[i] |
| self.W = nn.ModuleList([nn.Linear(self.prev_concat_size, hidden_sizes[i]) |
| for i in range(self.num_layers)]) |
| |
| self.out = nn.Linear(self.prev_concat_size, output_size) |
| self.activation = torch.tanh |
| |
| def forward(self, x, prev_outputs=None): |
| layer_outputs = [] |
| prev_cat = torch.cat(prev_outputs, dim=1) if prev_outputs is not None else None |
| |
| for i in range(self.num_layers): |
| current_input = x if i == 0 else torch.cat(layer_outputs, dim=1) |
| out = self.U[i](current_input) |
| if prev_cat is not None: |
| out = out + self.W[i](prev_cat) |
| out = self.activation(out) |
| layer_outputs.append(out) |
| |
| final_cat = torch.cat(layer_outputs, dim=1) |
| return layer_outputs, torch.sigmoid(self.out(final_cat)) |
| </code></pre> |
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| <p>Research integrated from <code>timeBasedAIDense.py</code>.</p> |
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