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Deep Double Descent
بایگانی مجموعه ها ("فیدهای غیر فعال" status)
When? This feed was archived on February 21, 2025 21:08 (
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Manage episode 424087967 series 3498845
We show that the double descent phenomenon occurs in CNNs, ResNets, and transformers: performance first improves, then gets worse, and then improves again with increasing model size, data size, or training time. This effect is often avoided through careful regularization. While this behavior appears to be fairly universal, we don’t yet fully understand why it happens, and view further study of this phenomenon as an important research direction.
Source:
https://openai.com/research/deep-double-descent
Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.
---
A podcast by BlueDot Impact.
Learn more on the AI Safety Fundamentals website.
فصل ها
1. Deep Double Descent (00:00:00)
2. Model-wise double descent (00:02:28)
3. Sample-wise non-monotonicity (00:04:39)
4. Epoch-wise double descent (00:06:14)
85 قسمت
بایگانی مجموعه ها ("فیدهای غیر فعال" status)
When?
This feed was archived on February 21, 2025 21:08 (
Why? فیدهای غیر فعال status. سرورهای ما، برای یک دوره پایدار، قادر به بازیابی یک فید پادکست معتبر نبوده اند.
What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.
Manage episode 424087967 series 3498845
We show that the double descent phenomenon occurs in CNNs, ResNets, and transformers: performance first improves, then gets worse, and then improves again with increasing model size, data size, or training time. This effect is often avoided through careful regularization. While this behavior appears to be fairly universal, we don’t yet fully understand why it happens, and view further study of this phenomenon as an important research direction.
Source:
https://openai.com/research/deep-double-descent
Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.
---
A podcast by BlueDot Impact.
Learn more on the AI Safety Fundamentals website.
فصل ها
1. Deep Double Descent (00:00:00)
2. Model-wise double descent (00:02:28)
3. Sample-wise non-monotonicity (00:04:39)
4. Epoch-wise double descent (00:06:14)
85 قسمت
همه قسمت ها
×
1 Introduction to Mechanistic Interpretability 11:45

1 We Need a Science of Evals 20:12

1 Illustrating Reinforcement Learning from Human Feedback (RLHF) 22:32

1 Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback 32:19

1 Constitutional AI Harmlessness from AI Feedback 1:01:49

1 Intro to Brain-Like-AGI Safety 1:02:10

1 Chinchilla’s Wild Implications 24:57

1 Deep Double Descent 8:27

1 Eliciting Latent Knowledge 1:00:27

1 Empirical Findings Generalize Surprisingly Far 11:32

1 Low-Stakes Alignment 13:56

1 Two-Turn Debate Doesn’t Help Humans Answer Hard Reading Comprehension Questions 16:39

1 Least-To-Most Prompting Enables Complex Reasoning in Large Language Models 16:08

1 ABS: Scanning Neural Networks for Back-Doors by Artificial Brain Stimulation 16:08

1 Imitative Generalisation (AKA ‘Learning the Prior’) 18:14
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