Concept

Statistical machine translation

Related lectures (32)

Covers coreference resolution, models, applications, challenges, and advancements in natural language processing.

By Antoine Bosselut introduces Natural Language Processing and its challenges, advancements in neural models, and course goals.

Sequence to Sequence Models: Overview and Applications

Covers sequence to sequence models, their architecture, applications, and the role of attention mechanisms in improving performance.

Non conceptual knowledge systems

Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.

Transformer: Pre-Training

Explores the Transformer model, from recurrent models to attention-based NLP, highlighting its key components and significant results in machine translation and document generation.

Memory-Efficient Adaptive Optimization

Explores memory-efficient adaptive optimization for large-scale learning and the challenges of memory overhead in training large models.

Transformers: Revolutionizing Attention Mechanisms in NLP

Covers the development of transformers and their impact on attention mechanisms in NLP.

Charged Particle in Magnetic Field: Landau Levels

Explores charged particles in a magnetic field, focusing on Landau levels and translation generators.

Transformers: Pretraining and Decoding Techniques

Covers advanced transformer concepts, focusing on pretraining and decoding techniques in NLP.

Automatic Synthesis of OS Code

Covers the automatic synthesis of OS code to ease the burden of developers in configuring translation hardware.

Translation Groups on [0,1]

Covers the study of translation groups on the interval [0,1] with different phases and the Riesz representation theorem on Hilbert space.

Asymmetric Parametric Models: Extremal Coefficient

Explores asymmetric parametric models and the extremal coefficient in extreme value dependence.