Trending Research
Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
We present a foundation model for zero-shot metric monocular depth estimation.
VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models
microsoft/vptq
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Due to the redundancy in LLM weights, recent research has focused on pushing weight-only quantization to extremely low-bit (even down to 2 bits).
MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages
hlt-mt/mosel
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The rise of foundation models (FMs), coupled with regulatory efforts addressing their risks and impacts, has sparked significant interest in open-source models.
"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models
We hope that our study can facilitate the research community and LLM vendors in promoting safer and regulated LLMs.
Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers
liruiw/HPT
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Previous robot learning methods often collect data to train with one specific embodiment for one task, which is expensive and prone to overfitting.
TPI-LLM: Serving 70B-scale LLMs Efficiently on Low-resource Edge Devices
In this paper, we argue that tensor parallelism can be more effective than pipeline on low-resource devices, and present a compute- and memory-efficient tensor parallel inference system, named TPI-LLM, to serve 70B-scale models.
Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration
ohayonguy/PMRF
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Photo-realistic image restoration algorithms are typically evaluated by distortion measures (e. g., PSNR, SSIM) and by perceptual quality measures (e. g., FID, NIQE), where the desire is to attain the lowest possible distortion without compromising on perceptual quality.
Ranked #1 on
Blind Face Restoration
on CelebA-Test
(FID metric)
SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration
Although quantization has proven to be an effective method for accelerating model inference, existing quantization methods primarily focus on optimizing the linear layer.
One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion
nico-bohlinger/one_policy_to_run_them_all
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Our experiments show that URMA can learn a locomotion policy on multiple embodiments that can be easily transferred to unseen robot platforms in simulation and the real world.
MinerU: An Open-Source Solution for Precise Document Content Extraction
Document content analysis has been a crucial research area in computer vision.
