Restricted Boltzmann machines (RBMs) are energy-based models originating from statistical physics. In this study, we investigate how musical structures are represented in the hidden layer of a Bernoulli-Bernoulli RBM trained on binary piano-roll representations of J.~S.~Bach's music. By examining the visible patterns induced by individual hidden units, we find that the hidden layer primarily captures local temporal and pitch-statistical features rather than explicit musical concepts such as melodies, chords, or keys. Free-energy comparisons show that the trained RBM assigns higher probabilities to the examined musical inputs than to digit or noise patterns. Analysis using t-SNE, cosine similarity, and PCA reveals that hidden representations are sensitive to absolute pitch and input overlap, with transposed versions of the same piece not necessarily mapped close together. Generated samples exhibit local pitch organization but limited long-range coherence. These findings illustrate how a simple statistical-physics model captures certain regularities in structured musical data while highlighting its limitations in representing higher-level musical relationships.
Neural video representations (NVRs) represent videos using neural network parameters and, in hybrid formulations, frame-wise latent embeddings. In conventional hybrid NVRs, the spatial size of frame-wise latent embeddings and the decoder upsampling schedule depend on the target resolution, potentially requiring large and non-uniform upsampling factors. In this paper, we propose a resolution-flexible decoder framework for hybrid NVRs. The decoder is constructed from uniform \(2\times\) upsampling stages, whose target feature sizes are obtained by tracing the spatial resolution backward from the final output resolution. After each upsampling stage, the feature map is aligned with the target size by minimal padding or cropping when necessary. To support this progressive decoding process, we further use intermediate reconstruction supervision and a reconstruction-difficulty-aware frame sampling strategy based on recent frame-wise losses. The framework preserves the basic representation format of hybrid NVRs and can therefore be applied to different backbones. Experiments on the UVG dataset at resolutions of \(1080\times1920\) and \(810\times1440\) show that the proposed approach improves reconstruction quality over the corresponding NVR baselines.