Representation learning on sequential data with latent priors

Jan Chorowski

**Concept Graph & Resume using Claude 3.5 Sonnet | Chat GPT4o | Llama 3:**

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classDef models fill:#d4f9d4, font-weight:bold, font-size:14px
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Main[Representation learning on

sequential data with

latent priors] --> A[Fundamental Concepts] Main --> B[Models and Architectures] Main --> C[Learning Techniques] Main --> D[Challenges and Solutions] Main --> E[Applications and Extensions] A --> A1[Unsupervised learning: represent unlabeled sequential

data 1] A --> A2[Discover units in speech and

handwriting 2] A --> A3[Latent representation: compact, useful data

form 3] A --> A4[Bottleneck forces efficient representations 7] A --> A5[Information filtering retains relevant, discards

irrelevant 10] A --> A6[Zero-shot learning: perform on unseen

data 11] B --> B1[Autoencoder: encode, then reconstruct input 4] B --> B2[VAE: encode data as probability

distributions 5] B --> B3[VQVAE: discrete latent representations via

clustering 6] B --> B4[Autoregressive models predict from past

values 8] B --> B5[Markovian model: probabilistic state transitions 19] B --> B6[Convolutional Deep Markov Model: CNNs

with Markovian dynamics 20] C --> C1[Probe classifiers analyze unsupervised representations 9] C --> C2[Smoothness prior: latent representations change

smoothly 12] C --> C3[Time jittering enforces smoothness without

collapse 14] C --> C4[Constrained optimization enforces desired properties 16] C --> C5[Lagrangian relaxation converts constrained to

unconstrained 17] C --> C6[Greedy algorithm merges latent vectors 18] D --> D1[Latent collapse ignores latent representations 13] D --> D2[Piecewise constant representation within units 15] D --> D3[Variational inference approximates complex distributions 21] D --> D4[Linguistic prior incorporates language structure

knowledge 22] D --> D5[Contrastive coding contrasts related, unrelated

samples 23] D --> D6[Maximize mutual information between inputs,

latents 24] E --> E1[Wave2Vec: self-supervised speech recognition technique 25] E --> E2[MIME-CPC: mutual information and contrastive

coding 26] E --> E3[Pixel CNN generates images pixel-by-pixel 27] E --> E4[WaveNet generates raw audio waveforms 28] E --> E5[Filter bank reconstruction measures spectrogram

reconstruction 29] E --> E6[Tonal information: pitch patterns carry

meaning 30] class Main main class A,A1,A2,A3,A4,A5,A6 basics class B,B1,B2,B3,B4,B5,B6 models class C,C1,C2,C3,C4,C5,C6 techniques class D,D1,D2,D3,D4,D5,D6 challenges class E,E1,E2,E3,E4,E5,E6 applications

sequential data with

latent priors] --> A[Fundamental Concepts] Main --> B[Models and Architectures] Main --> C[Learning Techniques] Main --> D[Challenges and Solutions] Main --> E[Applications and Extensions] A --> A1[Unsupervised learning: represent unlabeled sequential

data 1] A --> A2[Discover units in speech and

handwriting 2] A --> A3[Latent representation: compact, useful data

form 3] A --> A4[Bottleneck forces efficient representations 7] A --> A5[Information filtering retains relevant, discards

irrelevant 10] A --> A6[Zero-shot learning: perform on unseen

data 11] B --> B1[Autoencoder: encode, then reconstruct input 4] B --> B2[VAE: encode data as probability

distributions 5] B --> B3[VQVAE: discrete latent representations via

clustering 6] B --> B4[Autoregressive models predict from past

values 8] B --> B5[Markovian model: probabilistic state transitions 19] B --> B6[Convolutional Deep Markov Model: CNNs

with Markovian dynamics 20] C --> C1[Probe classifiers analyze unsupervised representations 9] C --> C2[Smoothness prior: latent representations change

smoothly 12] C --> C3[Time jittering enforces smoothness without

collapse 14] C --> C4[Constrained optimization enforces desired properties 16] C --> C5[Lagrangian relaxation converts constrained to

unconstrained 17] C --> C6[Greedy algorithm merges latent vectors 18] D --> D1[Latent collapse ignores latent representations 13] D --> D2[Piecewise constant representation within units 15] D --> D3[Variational inference approximates complex distributions 21] D --> D4[Linguistic prior incorporates language structure

knowledge 22] D --> D5[Contrastive coding contrasts related, unrelated

samples 23] D --> D6[Maximize mutual information between inputs,

latents 24] E --> E1[Wave2Vec: self-supervised speech recognition technique 25] E --> E2[MIME-CPC: mutual information and contrastive

coding 26] E --> E3[Pixel CNN generates images pixel-by-pixel 27] E --> E4[WaveNet generates raw audio waveforms 28] E --> E5[Filter bank reconstruction measures spectrogram

reconstruction 29] E --> E6[Tonal information: pitch patterns carry

meaning 30] class Main main class A,A1,A2,A3,A4,A5,A6 basics class B,B1,B2,B3,B4,B5,B6 models class C,C1,C2,C3,C4,C5,C6 techniques class D,D1,D2,D3,D4,D5,D6 challenges class E,E1,E2,E3,E4,E5,E6 applications

**Resume: **

**1.-** Unsupervised learning: Technique to learn representations of sequential data without labeled data, useful for understanding structure in documents like the Voynich manuscript.

**2.-** Unsupervised unit discovery: Finding boundaries and clustering data in speech and handwriting to identify characters or phonemes.

**3.-** Latent representation: Capturing essential information from input data in a more compact and useful form.

**4.-** Autoencoder: Neural network that encodes input data, then decodes it to reconstruct the original input.

**5.-** Variational Autoencoder (VAE): Generative model that learns to encode data as probability distributions in latent space.

**6.-** Vector Quantized VAE (VQVAE): VAE variant that uses discrete latent representations by clustering encoder outputs.

**7.-** Bottleneck: Constraining information flow in a model to force it to learn efficient representations.

**8.-** Autoregressive models: Models that predict future values based on past values, used for reconstructing data from latent representations.

**9.-** Probe classifiers: Small supervised classifiers used to analyze information content in unsupervised model representations.

**10.-** Information filtering: Selectively retaining relevant information (e.g., phonemes) while discarding irrelevant information (e.g., speaker identity).

**11.-** Zero-shot learning: Model's ability to perform tasks on unseen data or in new contexts.

**12.-** Smoothness prior: Assumption that latent representations should change smoothly over time for sequential data.

**13.-** Latent collapse: When a model ignores latent representations and relies solely on autoregressive decoding.

**14.-** Time jittering: Randomly copying latent vectors to enforce smoothness without causing latent collapse.

**15.-** Piecewise constant representation: Latent representation that remains constant within units (e.g., phonemes) and changes abruptly at boundaries.

**16.-** Constrained optimization: Formulating the learning problem with constraints to enforce desired properties in latent representations.

**17.-** Lagrangian relaxation: Converting constrained optimization problems into unconstrained problems with penalty terms.

**18.-** Greedy algorithm: Approach for solving the constrained optimization problem by merging latent vectors.

**19.-** Markovian dynamic model: Probabilistic model for transitions between latent states over time.

**20.-** Convolutional Deep Markov Model: Model combining convolutional neural networks with Markovian dynamics for latent representations.

**21.-** Variational inference: Technique for approximating complex probability distributions, used in VAEs and related models.

**22.-** Linguistic prior: Incorporating knowledge about language structure into latent representation learning.

**23.-** Contrastive coding: Learning technique that contrasts related and unrelated samples to improve representations.

**24.-** Mutual Information Maximization: Approach to learn representations by maximizing mutual information between inputs and latents.

**25.-** Wave2Vec: Self-supervised learning technique for speech recognition.

**26.-** MIME-CPC: Mutual Information Maximization and Contrastive Predictive Coding, techniques for representation learning.

**27.-** Pixel CNN: Autoregressive model for generating images pixel by pixel, used in handwriting generation example.

**28.-** WaveNet: Neural network for generating raw audio waveforms, used as a decoder in speech models.

**29.-** Filter bank reconstruction: Measure of how well a model can reconstruct speech spectrograms from latent representations.

**30.-** Tonal information: Pitch patterns in languages like Mandarin that carry meaning, potentially lost in some unsupervised models.

Knowledge Vault built byDavid Vivancos 2024