Transfer Learning for Neural Parameter Estimation applied to Building RC Models

Parameter estimation for dynamical systems remains challenging due to non-convexity and sensitivity to initial parameter guesses. Recent deep learning approaches enable accurate and fast parameter estimation but do not exploit transferable knowledge across systems. To address this, we introduce a transfer-learning-based neural parameter estimation framework based on a pretraining-fine-tuning paradigm. This approach improves accuracy and eliminates the need for an initial parameter guess. We apply this framework to building RC thermal models, evaluating it against a Genetic Algorithm and a from-scratch neural baseline across eight simulated buildings, one real-world building, two RC model configurations, and four training data lengths. Results demonstrate an 18.6-24.0% performance improvement with only 12 days of training data and up to 49.4% with 72 days. Beyond buildings, the proposed method represents a new paradigm for parameter estimation in dynamical systems.

LipDiffuser: Lip-to-Speech Generation with Conditional Diffusion Models

We present LipDiffuser, a conditional diffusion model for lip-to-speech generation synthesizing natural and intelligible speech directly from silent video recordings. Our approach leverages the magnitude-preserving ablated diffusion model (MP-ADM) architecture as a denoiser model. To effectively condition the model, we incorporate visual features using magnitude-preserving feature-wise linear modulation (MP-FiLM) alongside speaker embeddings. A neural vocoder then reconstructs the speech waveform from the generated mel-spectrograms. Evaluations on LRS3 and TCD-TIMIT demonstrate that LipDiffuser outperforms existing lip-to-speech baselines in perceptual speech quality and speaker similarity, while remaining competitive in downstream automatic speech recognition (ASR). These findings are also supported by a formal listening experiment. Extensive ablation studies and cross-dataset evaluation confirm the effectiveness and generalization capabilities of our approach.