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# thinking fast and slow | ||
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labels: experimental | ||
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weights = W | ||
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decompose W into W = W1 + W2 s.t. W1 and W2 have same dimension | ||
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set a (alpha) to be a mixing rate, which starts at zero. | ||
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Learn W as W = W1 + a * W2, increasing `a` throughout training in proportion to lr | ||
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W2 are the "slow" weights and are learned conventionally | ||
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W1 will be learned parameterized as a hyperlora, and so are our "fast" weights. | ||
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let W1 = VZ where V is a learnable vector and Z is a fixed, randomly initialized orthonormal matrix (i.e. random projections) | ||
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the "slow" weights are essentially a residual. | ||
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we could "stack" residuals if we wanted higher-order granularity |