AI - AI Dynamics

DNAGPT: Generalized Pretrained Tool for DNA Sequence Analysis

By

–

13 July 2023 6h35

DNAGPT: A Generalized Pretrained Tool for Multiple DNA Sequence Analysis Tasks paper page: https://
huggingface.co/papers/2307.05
628
… The success of the GPT series proves that GPT can extract general information from sequences, thereby benefiting all downstream tasks. This motivates us to use

Instruction Mining: High-Quality Data Selection for LLMs

By

–

13 July 2023 6h33

Instruction Mining: High-Quality Instruction Data Selection for Large Language Models paper page: https://
huggingface.co/papers/2307.06
290
… Large language models typically undergo two training stages, pretraining and finetuning. Despite that large-scale pretraining endows the model with strong

Robots Learning Manipulation from Human Video Demonstrations with Eye-in-Hand Cameras

By

–

13 July 2023 6h24

Giving Robots a Hand: Learning Generalizable Manipulation with Eye-in-Hand Human Demonstrations paper page: https://
huggingface.co/papers/2307.05
959
… Eye-in-hand cameras have shown promise in enabling greater sample efficiency and generalization in vision-based robotic manipulation.

NaViT: Vision Transformer for Flexible Aspect Ratios and Resolutions

By

–

13 July 2023 6h20

Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution paper page: https://
huggingface.co/papers/2307.06
304
… The ubiquitous and demonstrably suboptimal choice of resizing images to a fixed resolution before processing them with computer vision models has not yet been

→ View original post on X — @rasbt

Sarcastic critique of AI alignment solution timeline claims

By

@rasbt

–

13 July 2023 6h18

Congrats! You should join forces with the other company who just announced that they will solve AI alignment in <4 years. So you can both work on “solving the universe” once your are done with these more near-term projects.

→ View original post on X — @sudalairajkumar

LightGBM Early Stopping Callback for sklearn API

By

@sudalairajkumar

–

13 July 2023 5h45

We need to have early stopping as a callback in the sklearn api. es = lgb.early_stopping(stopping_rounds=200, verbose=False) http://
lgb.fit(train_X, train_y, eval_set=(val_X, val_y), callbacks=[es])