[2302.14045] Language Is Not All You Need: Aligning Perception with Language Models
https://arxiv.org/abs/2302.14045
#paper New generation
https://arxiv.org/abs/2302.14045
#paper New generation
https://arxiv.org/abs/2207.06881 #Paper Recurrent Memory Transformer. Scaling transformer architecture to long sequences.
https://huggingface.co/collections/osanseviero/model-merging-65097893623330a3a51ead66
Model Merging: papers
#Paper
Model Merging: papers
#Paper
huggingface.co
Model Merging - a osanseviero Collection
Model Merging is a very popular technique nowadays in LLM. Here is a chronological list of papers on the space that will help you get started with it!
🔥1
SOTA in unsupervised semantic segmentation:
1. STEGO: Unsupervised Semantic Segmentation by Distilling Feature Correspondences - 2022 https://arxiv.org/abs/2203.08414
2. HP: Leveraging Hidden Positives for Unsupervised Semantic Segmentation -2023 https://arxiv.org/abs/2303.15014
3. CAUSE: Causal Unsupervised Semantic Segmentation - 2023 https://arxiv.org/abs/2310.07379
#Paper
1. STEGO: Unsupervised Semantic Segmentation by Distilling Feature Correspondences - 2022 https://arxiv.org/abs/2203.08414
2. HP: Leveraging Hidden Positives for Unsupervised Semantic Segmentation -2023 https://arxiv.org/abs/2303.15014
3. CAUSE: Causal Unsupervised Semantic Segmentation - 2023 https://arxiv.org/abs/2310.07379
#Paper
arXiv.org
Unsupervised Semantic Segmentation by Distilling Feature Correspondences
Unsupervised semantic segmentation aims to discover and localize semantically meaningful categories within image corpora without any form of annotation. To solve this task, algorithms must produce...
🔥1
https://arxiv.org/pdf/2408.04840v1
mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models
#Paper
mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models
#Paper
https://arxiv.org/html/2405.18886v1 Compressing Large Language Models using Low Rank and Low Precision Decomposition #paper
https://arxiv.org/abs/2412.11768
https://github.com/AnonymousAlethiometer/SGD_SaI/
#Paper #Frameworks
https://github.com/AnonymousAlethiometer/SGD_SaI/
#Paper #Frameworks
arXiv.org
No More Adam: Learning Rate Scaling at Initialization is All You Need
In this work, we question the necessity of adaptive gradient methods for training deep neural networks. SGD-SaI is a simple yet effective enhancement to stochastic gradient descent with momentum...