Sampling with Path Integral MD

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Related lectures (30)

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Signals & Systems I: Sampling and Reconstruction

Explores ideal sampling, Fourier transformation, spectral repetition, and analog signal reconstruction.

Monte Carlo Techniques: Sampling and Simulation

Explores Monte Carlo techniques for sampling and simulation, covering integration, importance sampling, ergodicity, equilibration, and Metropolis acceptance.

Signals, Instruments, and Systems

Explores signals, instruments, and systems, covering ADC, Fourier Transform, sampling, signal reconstruction, aliasing, and anti-alias filters.

Metrics for Classification

Covers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.

Approximate Query Processing: BlinkDB

Introduces BlinkDB, a framework for approximate query processing using data samples to provide fast answers.

Wireless Receivers: Parameter Estimation

Covers parameter estimation in wireless receivers and phase ambiguity in signal modeling.

Filtering and Sampling of Signals

Explores filtering signals with a moving average filter and the process of sampling, emphasizing the importance of signal reconstruction from samples.

Signal Sampling: Bandwidth and Spectrum

Introduces signals, frequencies, bandwidth, filtering, and sampling in signal processing.

Diffusion: Data Denoising and Generative Modeling

Explores Data Denoising Diffusion Models, training objectives, sampling techniques, and challenges in applying diffusion to text.

Data Representations & Processing

Explores data representations, overfitting, model selection, Bag of Words, and learning with imbalanced data.