SUI

https://arxiv.org/abs/2306.09539 Block-State Transformer State space models (SSMs) have shown impressive results on tasks that require modeling long-range dependencies and efficiently scale to long sequences owing to their subquadratic runtime complexity. Originally designed for continuous signals, SSMs have sho arxiv.org 1. 상태 공간 모델(SSM)은 장기적인 의존성을 모델링하는 데 필요한 작업에서 뛰어난 결과를 보여주며, 서브쿼드럴릭 실행 시간 복잡..

https://language-to-reward.github.io/ 설명: https://twitter.com/xf1280/status/1669765756823941121 https://arxiv.org/abs/2306.08647 Language to Rewards for Robotic Skill Synthesis Large language models (LLMs) have demonstrated exciting progress in acquiring diverse new capabilities through in-context learning, ranging from logical reasoning to code-writing. Robotics researchers have also explored u..

https://arxiv.org/abs/2306.07915 Image Captioners Are Scalable Vision Learners Too Contrastive pretraining on image-text pairs from the web is one of the most popular large-scale pretraining strategies for vision backbones, especially in the context of large multimodal models. At the same time, image captioning on this type of data is co arxiv.org 1. 웹에서 이미지-텍스트 쌍에 대한 대조적 사전학습은, 특히 대형 다중모달 모델의 맥..

https://www.deepmind.com/blog/alphadev-discovers-faster-sorting-algorithms AlphaDev discovers faster sorting algorithms In our paper published today in Nature, we introduce AlphaDev, an artificial intelligence (AI) system that uses reinforcement learning to discover enhanced computer science algorithms – surpassing those honed by scientists and engineers over decades. www.deepmind.com DeepMind의 ..

abs: https://arxiv.org/abs/2306.00739 SQL-PaLM: Improved Large Language ModelAdaptation for Text-to-SQL One impressive emergent capability of large language models (LLMs) is generation of code, including Structured Query Language (SQL) for databases. For the task of converting natural language text to SQL queries, Text-to-SQL, adaptation of LLMs is of paramo arxiv.org 1. 대형 언어 모델(LLM)의 놀라운 능력 중 ..

abs: https://arxiv.org/abs/2306.00008 Brainformers: Trading Simplicity for Efficiency Transformers are central to recent successes in natural language processing and computer vision. Transformers have a mostly uniform backbone where layers alternate between feed-forward and self-attention in order to build a deep network. Here we investigat arxiv.org 1. 트랜스포머는 자연어 처리와 컴퓨터 비전의 최근 성공에 중심 역할을 하는데, ..

https://arxiv.org/abs/2305.19452 Bigger, Better, Faster: Human-level Atari with human-level efficiency We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on scaling the neural networks used for value estimation, as well as a number of other design choices that enable this sca arxiv.org https://github.com/google-re..

https://arxiv.org/abs/2305.18565 PaLI-X: On Scaling up a Multilingual Vision and Language Model We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture. Our model achieves new levels of performance on a wide-range of arxiv.org PaLI-X라는 다국어 비전 및 언어 모델을 확대 규모로 훈련하..

abs: https://arxiv.org/abs/2305.17552 Online Nonstochastic Model-Free Reinforcement Learning In this work, we explore robust model-free reinforcement learning algorithms for environments that may be dynamic or even adversarial. Conventional state-based policies fail to accommodate the challenge imposed by the presence of unmodeled disturbances in arxiv.org 1. 이 연구에서는 동적이거나 적대적일 수 있는 환경에 대한 강건한 모..

abs: https://arxiv.org/abs/2305.18161 VA-learning as a more efficient alternative to Q-learning In reinforcement learning, the advantage function is critical for policy improvement, but is often extracted from a learned Q-function. A natural question is: Why not learn the advantage function directly? In this work, we introduce VA-learning, which dire arxiv.org 1. 강화 학습에서 이점 함수는 정책 개선에 있어 중요하지만, ..