# Dynamic Relevance-Weighting-Based Width-Adaptive Auto-Encoder
## 3 Width-Adaptive Autoencoder
### Adding and Pruning Nodes
Add nodes, or prune non-relevant nodes using Algorithm 2  Until the weights of the \(AE\) model converge
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**Algorithm 3**The Proposed Algorithm  
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Input: input instances \(\



## 4 Experiments

### Datasets

To validate the adaptability and effectiveness of the proposed WAAE, we selected a diverse set of benchmarks (MNIST, CIFAR-10) and real-world (Parkinson, Epilepsy) datasets. These datasets were chosen to cover a wide range of data characteristics and application domains, from low-dimensional grayscale images to high-dimensional temporal medical signals. This allows us to assess WAAE's generalization capability across varying complexities, input modalities, and classification tasks. Experiments were implemented using Google Colaboratory Pro+. The neural network was implemented with TensorFlow and

Figure 4: Flowchart of the proposed algorithm. Keras libraries. Training was accelerated using an NVIDIA A100 Tensor Core GPU. Dataset-specific details and evaluation results are presented in the corresponding subsections. * MNIST dataset [36] comprises 60 K training samples and 10 K test samples evenly distributed over 10 classes. Each sample comprises a 28 \(\times\) 28 grayscale image depicting a handwritten digit (0 to 9). Figure 5a shows sample instances from the MNIST dataset. * CIFAR-10 dataset [37] has 60 K low resolution color images of dimensions 32 \(\times\) 32, evenly distributed over 10 categories. The dataset has 50 K training samples and 10 K test samples. Figure 5b shows sample instances from the CIFAR-10 dataset. * Parkinson dataset [38; 39] was gathered from 188 patients (107 men and 81 women) diagnosed with [PERSON]'s disease at the Department of Neurology at the Cerrahpasa Faculty of Medicine, Istanbul University. Additionally, 64 healthy subjects (23 men and 41 women) are included in the dataset. Each individual was recorded pronouncing the letter /a/ three times. Subsequently, audio features are extracted from the recorded utterances. In particular, Time-Frequency Features, Mel Frequency Cepstral Coefficients (MFCCs), Wavelet Transform-based Features, Vocal Fold Features, and Tunable Q-factor Wavelet Transform (TQWT) features were extractable. This leads to an audio feature that is 752-dimensional. * Epilepsy dataset [40; 41] comprises a recording of the brain activity of 500 individuals over 23.5 s. The corresponding time series is sampled into 4097 data points. Every data point represents the value of the Electroencephalogram (EEG) recording at a distinct moment in time. Each of these 4097 data points is divided and shuffled into 23 segments, each containing 178 data points for 1 s. This generates 11,500 instances that are described by a 178-dimensional feature vector. There are five categories of the 178-dimensional input vector; \"eyes open\", \"eyes closed\", \"recording from the healthy area\", \"recording from the tumor area\", and \"seizure activity.\" Table 1 shows an overview of the characteristics of the considered datasets. Figure 5: Sample images from benchmark datasets from (**a**) MNIST dataset, and (**b**) CIFAR-10 dataset. ### Experimental Results and Analysis

During the training phase, the encoder's width was initially set to 100 nodes. It was incrementally increased by 10 nodes per batch until the first pruning occurred. The initial number of 100 hidden nodes and the incremental addition of 10 nodes per batch were selected as practical and computationally efficient defaults, consistent with common practice in adaptive architecture methods. These values allow the network to grow gradually while maintaining manageable training times. Importantly, due to the built-in relevance-weighting and pruning mechanisms, the final architecture is not highly sensitive to these choices. The model adaptively converges to a suitable width regardless of the initial or incremental size. Thus, these settings primarily affect convergence speed rather than the final performance. Following this, pruning was progressively applied until the model converged and the optimal width was achieved.