The $5M figure for the last training run should not be your basis for how much frontier AI models cost.| www.interconnects.ai
Although most popular and successful model architectures are designed by human experts, it doesn’t mean we have explored the entire network architecture space and settled down with the best option. We would have a better chance to find the optimal solution if we adopt a systematic and automatic way of learning high-performance model architectures. Automatically learning and evolving network topologies is not a new idea (Stanley & Miikkulainen, 2002). In recent years, the pioneering work by ...| lilianweng.github.io
[Updated on 2020-01-09: add a new section on Contrastive Predictive Coding]. [Updated on 2020-04-13: add a “Momentum Contrast” section on MoCo, SimCLR and CURL.] [Updated on 2020-07-08: add a “Bisimulation” section on DeepMDP and DBC.] [Updated on 2020-09-12: add MoCo V2 and BYOL in the “Momentum Contrast” section.] [Updated on 2021-05-31: remove section on “Momentum Contrast” and add a pointer to a full post on “Contrastive Representation Learning”] Given a task and enoug...| lilianweng.github.io
In my earlier post on meta-learning, the problem is mainly defined in the context of few-shot classification. Here I would like to explore more into cases when we try to “meta-learn” Reinforcement Learning (RL) tasks by developing an agent that can solve unseen tasks fast and efficiently. To recap, a good meta-learning model is expected to generalize to new tasks or new environments that have never been encountered during training.| lilianweng.github.io
[Updated on 2019-02-14: add ULMFiT and GPT-2.] [Updated on 2020-02-29: add ALBERT.] [Updated on 2020-10-25: add RoBERTa.] [Updated on 2020-12-13: add T5.] [Updated on 2020-12-30: add GPT-3.] [Updated on 2021-11-13: add XLNet, BART and ELECTRA; Also updated the Summary section.] Fig. 0. I guess they are Elmo & Bert? (Image source: here) We have seen amazing progress in NLP in 2018. Large-scale pre-trained language modes like OpenAI GPT and BERT have achieved great performance on a variety of l...| lilianweng.github.io
[Updated on 2019-10-01: thanks to Tianhao, we have this post translated in Chinese!] A good machine learning model often requires training with a large number of samples. Humans, in contrast, learn new concepts and skills much faster and more efficiently. Kids who have seen cats and birds only a few times can quickly tell them apart. People who know how to ride a bike are likely to discover the way to ride a motorcycle fast with little or even no demonstration.| lilianweng.github.io
