Scaling Language Models: Efficiency and Deployment

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Manipulating Objects with Robots: Vision-Language Integration

Discusses how robots manipulate objects using natural language instructions and integrates vision-language models for enhanced performance.

Model Compression Techniques: Enhancing Neural Networks

Covers model compression techniques to enhance the efficiency of large language models in production settings.

Deep Learning: Exploring Vision and Language Transformers

Covers advanced transformer architectures in deep learning, focusing on Swin, HUBERT, and Flamingo models for multimodal applications.

Sequence to Sequence Models: Overview and Applications

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

Transformers: Pretraining and Decoding Techniques

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

Predicting Reaction Yields with Deep Learning

Explores predicting reaction yields with deep learning models and the importance of high-quality data sets in chemistry.

Deep Learning Accelerators: Optimization Strategies

Explores optimization strategies for deep learning accelerators, emphasizing data movement reduction through batching, dataflow optimizations, and compression.

Modern NLP: From GPT to ChatGPT

Explores the evolution of modern NLP from GPT-2 to GPT-3, emphasizing in-context learning and the development of ChatGPT.

Transformers: Unifying Machine Learning Communities

Covers the role of Transformers in unifying various machine learning fields.

Auto-encoder and GANs

Covers auto-encoders for data compression and GANs for data generation.