Byte Sized Breakthroughs
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.
- Update frequency
- every day
- Episodes
- 92
- Years Active
- 2024 - 2025
UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
RT-DETR: Real-Time Object Detection with Transformer
Robustness Evaluation of HD Map Constructors under Sensor Corruptions for Autonomous Driving
DriveVLM: Vision-Language Models for Autonomous Driving in Urban Environments
The limits to learning a diffusion model
AutoEmb Automated Embedding Dimensionality Searchg in Streaming Recommendations
NeuralProphet Explainable Forecasting at Scale
No-Transaction Band Network A Neural Network Architecture for Efficient Deep Hedging
Zero Bubble Pipeline Parallelism
A Better Match for Drivers and Riders Reinforcement Learning at Lyft
TransAct Transformer-based Realtime User Action Model for Recommendation at Pinterest