JAX 的速度比 NumPy 快了 N 个数量级 rl-vigen: A Reinforcement Learning Benchmark for Visual Generalization Zhecheng Yuan, Sizhe Yang, Pu Hua, Can Chang, Kaizhe Hu, Huazhe Xu(清华、电子科大、上海期智研究院、上海人工智能实验室(书生大模型)) https://arxiv.org/abs/2307.102249/23p https://gi...
JAX 的速度比 NumPy 快了 N 个数量级 rl-vigen: A Reinforcement Learning Benchmark for Visual Generalization Zhecheng Yuan, Sizhe Yang, Pu Hua, Can Chang, Kaizhe Hu, Huazhe Xu(清华、电子科大、上海期智研究院、上海人工智能实验室(书生大模型)) https://arxiv.org/abs/2307.102249/23p https://gi...
nips datasets and benchmark NIPS Datasets and Benchmarks NIPS datasets and benchmarks are a set of open source datasets created by the Neural Information Processing Systems (NIPS) team. They provide a platform for research in deep learning and artificial intelligence (AI). These datasets and ...
Datasets and Benchmarks Track 是CCFA嘛？NIPS DB含金量怎么样呀？跟main同等吗？手头上有一些bench的...
与同学交流后,我们发现确实存在可改进的空间,并且查阅了网上其他人对benchmark的复现,确实是这样做的!因此,我们决定修改代码。 关于这个定义问题,我与合作者进行了长时间的讨论。由于该领域的历史遗留问题,后门攻击是否可以修改主体信息、修改范围等都没有严格的数学定义,因此很难对齐。但总的来说,修改代码后,我们的...
7.Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model 本文由中国香港科技大学与中国香港天文台合作完成。文中解决了降水即时预报问题。在该问题中,卷积长短记忆网络被证实优于传统的光流法,说明了深度学习在解决该问题上有巨大潜力。然而,卷积递归结构具有位置不变性,而自然的变换(例如旋转)往...
UDA: A Benchmark Suite for Retrieval Augmented Generation in Real-World Document Analysis Ad Auctions for LLMs via Retrieval Augmented Generation eFIR: Grounding Large Restoration Models with Retrieval Augmentation TableRAG: Million-Token Table Understanding with Language Models ...
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems【Tenrec:推荐系统的大规模多用途基准数据集】 APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction【APG:点击率预测的自适应参数生成网络】 因果效应
fromnowcasting.configimportcfgmodel=INITIALIZE_YOUR_MODELmode="fixed"# Can also be "online"env=HKOBenchmarkEnv(pd_path=cfg.HKO_PD.RAINTY_TEST,mode=mode)whilenotenv.done:# Get the observationin_frame_dat,in_mask_dat,in_datetime_clips,out_datetime_clips,begin_new_episode,need_upload_prediction...
fromnowcasting.configimportcfgmodel=INITIALIZE_YOUR_MODELmode="fixed"# Can also be "online"env=HKOBenchmarkEnv(pd_path=cfg.HKO_PD.RAINTY_TEST,mode=mode)whilenotenv.done:# Get the observationin_frame_dat,in_mask_dat,in_datetime_clips,out_datetime_clips,begin_new_episode,need_upload_prediction...