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Official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting" (ICLR 2024 Spotlight), https://openreview.net/forum?id=JePfAI8fah
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Ongoing research training transformer language models at scale, including: BERT & GPT-2
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深度学习经典、新论文逐段精读
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本项目旨在分享大模型相关技术原理以及实战经验。
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The Triton Inference Server provides an optimized cloud and edge inferencing solution.
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Development repository for the Triton language and compiler
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Decentralized Autonomous Regulated Company (DARC), a company virtual machine that runs on any EVM-compatible blockchain, with on-chain law system, multi-level tokens and dividends mechanism.
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Official release of InternLM2 7B and 20B base and chat models. 200K context support
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Unify Efficient Fine-Tuning of 100+ LLMs
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GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction
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This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model.
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STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction
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Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018
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A pytorch implementation for BPR (Bayesian Personalized Ranking).
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time series forecasting with TCN and RNN neural networks in Darts
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Sequence modeling benchmarks and temporal convolutional networks
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主权个体(中译版)
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Some recommendation models and retrieval models
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Awesome Deep Learning papers for industrial Search, Recommendation and Advertising. They focus on Embedding, Matching, Ranking (CTR and CVR prediction), Post Ranking, Multi-task Learning, Graph Neural Networks, Transfer Learning, Reinforcement Learning, Self-supervised Learning and so on.
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chatgpt代理服务,用于给内网小伙伴体验
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A framework for large scale recommendation algorithms.
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Better Discord enhances Discord desktop app with new features.
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3rd ranked model in M5 accuracy competition
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Probabilistic time series modeling in Python
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GluonTS Implementation of Intermittent Demand Forecasting with Deep Renewal Processes arXiv:1911.10416v1 [cs.LG]
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