Byte Sized Breakthroughs

Byte Sized Breakthroughs

Por: Arjun Srivastava

Byte-Sized Breakthroughs offers concise audio summaries of recent AI research papers. Each episode breaks down a single paper in areas like machine learning, computer vision, or natural language processing, making it easier to stay current with AI advancements.

The podcast covers topics such as large language models, mechanistic interpretability, and in-context learning. Episodes feature clear explanations of complex concepts, designed for efficient listening.

Ideal for researchers, engineers, and AI enthusiasts with limited time, Byte-Sized Breakthroughs provides a starting point for exploring cutting-edge AI research. While offering overviews, listeners are encouraged to refer to original papers for comprehensive understanding.

Curated by Arjun Srivastava, an engineer in the field, this podcast transforms spare moments into opportunities for learning about the latest in AI. Note: The voices you hear are not real people, but the content is carefully curated and reviewed.
92 episodios disponibles

Latest episodes of the podcast Byte Sized Breakthroughs

Mostrando página 4 de 5

Generalization Patterns of Transformers in In-Weights Learning and In-Context Learning 10/08/2024
Spider2-V: Automated Multimodal Agents for Data Science Workflows 10/08/2024
In-Context Learning Capabilities of Transformers 10/08/2024
How Transformers Learn In-Context Beyond Simple Functions 10/08/2024
Decision-Pretrained Transformer: Bridging Supervised Learning and Reinforcement Learning 10/08/2024
Supervised Pretraining for In-Context Reinforcement Learning with Transformers 10/08/2024
ScreenAgent: A Vision Language Model-driven Computer Control Agent 10/08/2024
Efficient Compression of Large Language Models using LLM-Pruner 11/08/2024
SparseGPT: One-shot Pruning of Large Language Models 11/08/2024
AutoPruner: End-to-End Trainable Filter Pruning for Efficient Deep Neural Networks 11/08/2024
Optimizing Quantization of Large Language Models for Efficiency and Accuracy 12/08/2024
In-Context Policy Iteration: Enhancing Reinforcement Learning with Large Language Models 14/08/2024
Enhancing Language Models with a Massive Datastore 14/08/2024
Efficient Inference for Large Language Models with LLM.int8() 14/08/2024
Comprehensive Guide to Real-Time Bidding (RTB): Challenges and Opportunities 31/08/2024
LiNR: Revolutionizing Large-Scale Retrieval for Recommendation Systems 31/08/2024
Scaling User Modeling for Personalized Advertising at Meta 31/08/2024
Deep Retrieval: Learning Efficient Structures for Large-Scale Recommendation Systems 31/08/2024
Efficient Deep Learning Parallelization using SOAP Search Space and FlexFlow Framework 31/08/2024
Trust Region Policy Optimization 18/01/2025
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