LeapLab@THU @LeapLabTHU
LeapLab@THU Tsinghua University, China Joined December 2023-
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Following44
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LeapLab@THU retweeted
Microsoft releases ART Anonymous Region Transformer for Variable Multi-Layer Transparent Image Generation
LeapLab@THU retweeted
very fantastic work done with @ALucyBrilliant @RayLu_THU @AndrewZ45732491. direct parameter editing can modulate LLM’s behavior
very fantastic work done with @ALucyBrilliant @RayLu_THU @AndrewZ45732491. direct parameter editing can modulate LLM’s behavior
LeapLab@THU retweeted
ConvLLaVA Hierarchical Backbones as Visual Encoder for Large Multimodal Models High-resolution Large Multimodal Models (LMMs) encounter the challenges of excessive visual tokens and quadratic visual complexity. Current high-resolution LMMs address the quadratic
LeapLab@THU retweeted
📢Excited to share our recent work on Large Multimodal Models: ConvLLaVA. Without the encoding multiple image patches and multiple encoders, we use a hierarchical backbone, ConvNeXt, realizing high resolution understanding. arxiv.org/pdf/2405.15738
EfficientTrain++ is accepted by TPAMI2024🤩 🔥An off-the-shelf, easy-to-implement algorithm for training foundation visual backbones efficiently! 🔥1.5−3.0× lossless training/pre-training speedup on ImageNet-1K/22K! Paper&Code: arxiv.org/abs/2405.08768 github.com/LeapLabTHU/Eff…
Our recent work: Agent Attention! [High Performance & Linear Complexity] [Double the speed of SD and enhance generation quality, no additional fine-tuning is required] Paper and code: huggingface.co/papers/2312.08… arxiv.org/abs/2312.08874 github.com/LeapLabTHU/Age…
Excited to share our #NeurIPS2023 spotlight paper! 🌟 It proposes a novel offline-to-online RL algorithm, efficiently utilizing collected samples by training a family of policies offline and selecting suitable ones online. Check out our paper for details! arxiv.org/abs/2310.17966
LeapLab@THU retweeted
ExpeL is now accepted at #AAAI24 ! The code and camera ready version will be updated promptly. Thanks for all the collaborators and see you in Vancouver!
ExpeL is now accepted at #AAAI24 ! The code and camera ready version will be updated promptly. Thanks for all the collaborators and see you in Vancouver!
LeapLab@THU retweeted
Check us out at #NeurIPS2023 poster！We investigate into Q-value divergence phenomenon in offline RL and find self-excitation to be the main reason. Using layernorm in RL models can fundamentally prevent this from happening. arxiv.org/pdf/2310.04411…
Our recent work: Agent Attention! [High Performance & Linear Complexity] [Accelerate and improve Stable Diffusion, no additional fine-tuning is required] The paper and code have been released: arxiv.org/abs/2312.08874 github.com/LeapLabTHU/Age…
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