Kuiz Pengkomputeran Rangkaian Negeri Gema dan Takungan

Jawapan dan penjelasan muncul semasa anda mengambil kuiz.

1. Dalam Rangkaian Negeri Echo, pemberat yang manakah sebenarnya dilatih?
   • Hanya berat keluaran (bacaan).
   • Semua pemberat berulang di dalam takungan
   • Hanya berat input
   • Tiada beban pernah dilatih
2. Which condition expresses the echo state property for two runs with the same input history?
   • Their recurrent weights must all be trained by backpropagation
   • Their reservoir states eventually become independent of different initial states
   • Their output weights must have spectral radius exactly one
   • Their hidden states must equal the raw input at every step
3. Mengapakah latihan ESN jauh lebih pantas daripada latihan LSTM standard?
   • ESN tidak menggunakan neuron sama sekali
   • ESN berjalan hanya pada komputer kuantum
   • ESN mengelakkan perambatan balik melalui masa dengan hanya menyelesaikan bacaan linear
   • ESN tidak memerlukan data input
4. How is spectral radius commonly used when tuning an echo-state reservoir?
   • It directly sets the number of readout classes
   • It guarantees fading memory for every recurrent matrix
   • It measures the number of trainable reservoir weights
   • It is a recurrent-feedback scale heuristic for balancing rich dynamics and stability, not a complete guarantee of the echo state property
5. Mesin Keadaan Cecair, bentuk spiking-neuron pengkomputeran takungan, telah diperkenalkan oleh siapa?
   • Wolfgang Maass
   • Geoffrey Hinton
   • Andrew Ng
   • Yoshua Bengio
6. What does the fixed reservoir compute before the ESN readout produces an output?
   • A normalized probability over all possible weights
   • A high-dimensional dynamic state representation of recent inputs
   • A learned transition table for every future sequence
   • A separate trained policy for each output class
7. What method is commonly used to fit an Echo State Network’s readout weights?
   • Backpropagation through all fixed recurrent weights
   • Dynamic routing by agreement
   • Linear least-squares regression, often with ridge regularization
   • A softmax over reservoir eigenvalues
8. What does the spectral radius of a recurrent weight matrix mean?
   • The sum of all matrix entries
   • The number of nonzero output weights
   • The average magnitude of the input sequence
   • The largest absolute value among its eigenvalues