Published Mar 15, 2018

52 - Sequence-to-Sequence Learning as Beam-Search Optimization, with Sam Wiseman

Sam Wiseman delves into enhancing sequence-to-sequence models with pre-training and constrained training strategies, offering insights into efficient gradient computation, runtime performance, and the transformative potential of Beam Search Optimization in overcoming exposure and label bias in model training.

Episode Highlights

Topics covered

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Episode Highlights

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Dexa/NLP Highlights

52 - Sequence-to-Sequence Learning as Beam-Search Optimization, with Sam Wiseman

Topics covered

Popular Clips

Efficient Beam Search

Loss Function Design

Overcoming Model Training Challenges

Beam Size Impact

Efficient Gradient Computation

Update Strategies

Constrained Training Benefits

Training Strategies

Beam Search Optimization

Episode Highlights

Training Strategies

Algorithm Efficiency

Beam Search Optimization

Related Episodes

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67 - GLUE: A Multi-Task Benchmark and Analysis Platform, with Sam Bowman

02 - Bidirectional Attention Flow for Machine Comprehension

49 - A Joint Sequential and Relational Model for Frame-Semantic Parsing, with Bishan Yang

12 - Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

09 - Learning to Generate Reviews and Discovering Sentiment

63 - Neural Lattice Language Models, with Jacob Buckman

10 - A Syntactic Neural Model for General-Purpose Code Generation

07 - Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks

22 - Deep Multitask Learning for Semantic Dependency Parsing, with Noah Smith

29 - Neural machine translation via binary code prediction, with Graham Neubig

51 - A Regularized Framework for Sparse and Structured Neural Attention, with Vlad Niculae

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104 - Model Distillation, with Victor Sanh and Thomas Wolf

25 - Neural Semantic Parsing over Multiple Knowledge-bases

52 - Sequence-to-Sequence Learning as Beam-Search Optimization, with Sam Wiseman

Topics covered

Popular Clips

Episode Highlights

Training StrategiesSam Wiseman discusses the importance of pre-training and constrained training in enhancing the performance of sequence-to-sequence models. He highlights how these techniques address challenges in model training and improve outcomes, especially in low-resource scenarios.

Training Strategies

Algorithm EfficiencySam Wiseman explores the intricacies of gradient computation and runtime performance in seq2seq models. He provides insights into the challenges of efficient gradient computation and the impact of beam size on model performance.

Algorithm Efficiency

Beam Search OptimizationSam Wiseman explores the complexities of sequence-to-sequence models, addressing challenges like exposure and label bias. He introduces Beam Search Optimization as a solution, highlighting its impact on model performance and training efficiency.

Beam Search Optimization

Related Episodes