The Complete Collection of Data Science Books – Part 1
https://www.kdnuggets.com/2022/05/complete-collection-data-science-books-part-1.html
@ai_machinelearning_big_data
https://www.kdnuggets.com/2022/05/complete-collection-data-science-books-part-1.html
@ai_machinelearning_big_data
👍19
📝 Automated Crossword Solving
Pretrained models, precomputed FAISS embeddings, and a crossword clue-answer dataset.
Github: https://github.com/albertkx/berkeley-crossword-solver
Paper: https://arxiv.org/abs/2205.09665v1
Dataset: https://www.xwordinfo.com/JSON/
@ai_machinelearning_big_data
Pretrained models, precomputed FAISS embeddings, and a crossword clue-answer dataset.
Github: https://github.com/albertkx/berkeley-crossword-solver
Paper: https://arxiv.org/abs/2205.09665v1
Dataset: https://www.xwordinfo.com/JSON/
@ai_machinelearning_big_data
👍13❤3
🦄 Imagen unprecedented photorealism × deep level of language understanding From Google
Home: https://gweb-research-imagen.appspot.com/
Paper: https://gweb-research-imagen.appspot.com/paper.pdf
@ai_machinelearning_big_data
Home: https://gweb-research-imagen.appspot.com/
Paper: https://gweb-research-imagen.appspot.com/paper.pdf
@ai_machinelearning_big_data
👍15❤5🔥5
⚙️ Time-series Transformer Generative Adversarial Networks
Github: https://github.com/jsyoon0823/TimeGAN
Paper: https://arxiv.org/abs/2205.11164v1
Stock data: https://finance.yahoo.com/quote/GOOG/history
Energy data: http://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction
@ai_machinelearning_big_data
Github: https://github.com/jsyoon0823/TimeGAN
Paper: https://arxiv.org/abs/2205.11164v1
Stock data: https://finance.yahoo.com/quote/GOOG/history
Energy data: http://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction
@ai_machinelearning_big_data
👍10🔥4
💻 BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework
Github: https://github.com/adlab-autodrive/bevfusion
Paper: https://arxiv.org/abs/2205.13790v1
Dataset: https://paperswithcode.com/dataset/kitti
Github: https://github.com/adlab-autodrive/bevfusion
Paper: https://arxiv.org/abs/2205.13790v1
Dataset: https://paperswithcode.com/dataset/kitti
👍7
📍 Perturbation Augmentation for Fairer NLP
Responsible NLP projects from Meta AI.
Github: https://github.com/facebookresearch/responsiblenlp
Paper: https://arxiv.org/abs/2205.12586v1
Dataset: https://paperswithcode.com/dataset/glue
@ai_machinelearning_big_data
Responsible NLP projects from Meta AI.
Github: https://github.com/facebookresearch/responsiblenlp
Paper: https://arxiv.org/abs/2205.12586v1
Dataset: https://paperswithcode.com/dataset/glue
@ai_machinelearning_big_data
👍18👎1
🟢 Green Hierarchical Vision Transformer for Masked Image Modeling
Github: https://github.com/layneh/greenmim
Paper: https://arxiv.org/abs/2205.13515v1
Dataset: https://paperswithcode.com/dataset/coco
@ai_machinelearning_big_data
Github: https://github.com/layneh/greenmim
Paper: https://arxiv.org/abs/2205.13515v1
Dataset: https://paperswithcode.com/dataset/coco
@ai_machinelearning_big_data
👍13
This media is not supported in your browser
VIEW IN TELEGRAM
🔻 AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
Github: https://github.com/ShoufaChen/AdaptFormer
Paper: https://arxiv.org/abs/2205.13535v1
Dataset: https://paperswithcode.com/dataset/something-something-v2
@ai_machinelearning_big_data
Github: https://github.com/ShoufaChen/AdaptFormer
Paper: https://arxiv.org/abs/2205.13535v1
Dataset: https://paperswithcode.com/dataset/something-something-v2
@ai_machinelearning_big_data
👍15
Surface Vision Transformers
Github: https://github.com/metrics-lab/surface-vision-transformers
Paper: https://arxiv.org/abs/2205.15836v1
@ai_machinelearning_big_data
Github: https://github.com/metrics-lab/surface-vision-transformers
Paper: https://arxiv.org/abs/2205.15836v1
@ai_machinelearning_big_data
👍7👎5
✈️ HIRL: A General Framework for Hierarchical Image Representation Learning
Github: https://github.com/hirl-team/hirl
Paper: https://arxiv.org/abs/2205.13159v1
Dataset: https://paperswithcode.com/dataset/places205
@ai_machinelearning_big_data
Github: https://github.com/hirl-team/hirl
Paper: https://arxiv.org/abs/2205.13159v1
Dataset: https://paperswithcode.com/dataset/places205
@ai_machinelearning_big_data
👍15👎1
🔝 PanopticDepth: A Unified Framework for Depth-aware Panoptic Segmentation
Github: https://github.com/naiyugao/panopticdepth
Paper: http://arxiv.org/abs/2206.00468
Dataset: https://paperswithcode.com/dataset/cityscapes
@ai_machinelearning_big_data
Github: https://github.com/naiyugao/panopticdepth
Paper: http://arxiv.org/abs/2206.00468
Dataset: https://paperswithcode.com/dataset/cityscapes
@ai_machinelearning_big_data
👍10
🦠 MaSIF- Molecular Surface Interaction Fingerprints: Geometric deep learning to decipher patterns in protein molecular surfaces.
MaSIF is a proof-of-concept method to decipher patterns in protein surfaces important for specific biomolecular interactions.
Github: https://github.com/LPDI-EPFL/masif
Paper: https://www.nature.com/articles/s41592-019-0666-6
Data: https://github.com/LPDI-EPFL/masif#MaSIF-data-preparation
@ai_machinelearning_big_data
MaSIF is a proof-of-concept method to decipher patterns in protein surfaces important for specific biomolecular interactions.
Github: https://github.com/LPDI-EPFL/masif
Paper: https://www.nature.com/articles/s41592-019-0666-6
Data: https://github.com/LPDI-EPFL/masif#MaSIF-data-preparation
@ai_machinelearning_big_data
👍15🔥3
🔊A Python library for audio feature extraction, classification, segmentation and applications.
Code: PyAudioAnalysis
Code: PyAudioAnalysis
👍11👎2🔥1
🪄 Investigating the Role of Image Retrieval for Visual Localization -- An exhaustive benchmark.
Github: https://github.com/naver/kapture-localization
Paper: https://arxiv.org/abs/2205.15761v1
Data: https://paperswithcode.com/dataset/inloc
@ai_machinelearning_big_data
Github: https://github.com/naver/kapture-localization
Paper: https://arxiv.org/abs/2205.15761v1
Data: https://paperswithcode.com/dataset/inloc
@ai_machinelearning_big_data
👍9🔥3❤1
This media is not supported in your browser
VIEW IN TELEGRAM
💬 Text2Human - Official PyTorch Implementation
We synthesize full-body human images starting from a given human pose
Github: https://github.com/yumingj/Text2Human
Project: https://yumingj.github.io/projects/Text2Human.html
StyleGAN: https://github.com/stylegan-human/stylegan-human
Paper: https://arxiv.org/abs/2205.15996v1
Dataset: https://github.com/yumingj/DeepFashion-MultiModal
Demo video: https://youtu.be/yKh4VORA_E0
@ai_machinelearning_big_data
We synthesize full-body human images starting from a given human pose
Github: https://github.com/yumingj/Text2Human
Project: https://yumingj.github.io/projects/Text2Human.html
StyleGAN: https://github.com/stylegan-human/stylegan-human
Paper: https://arxiv.org/abs/2205.15996v1
Dataset: https://github.com/yumingj/DeepFashion-MultiModal
Demo video: https://youtu.be/yKh4VORA_E0
@ai_machinelearning_big_data
👍28
🎆 Optimizing Relevance Maps of Vision Transformers Improves Robustness
This code allows to finetune the explainability maps of Vision Transformers to enhance robustness.
Github: https://github.com/hila-chefer/robustvit
Colab: https://colab.research.google.com/github/hila-chefer/RobustViT/blob/master/RobustViT.ipynb
Paper: https://arxiv.org/abs/2206.01161
Dataset: https://github.com/UnsupervisedSemanticSegmentation/ImageNet-S
@ai_machinelearning_big_data
This code allows to finetune the explainability maps of Vision Transformers to enhance robustness.
Github: https://github.com/hila-chefer/robustvit
Colab: https://colab.research.google.com/github/hila-chefer/RobustViT/blob/master/RobustViT.ipynb
Paper: https://arxiv.org/abs/2206.01161
Dataset: https://github.com/UnsupervisedSemanticSegmentation/ImageNet-S
@ai_machinelearning_big_data
👍18🔥2
UniSRec
The proposed approach utilizes the associated denoscription text of items to learn transferable representations across different recommendation scenarios.
Github: https://github.com/rucaibox/unisrec
Paper: https://arxiv.org/abs/2206.05941v1
Google Drive: https://drive.google.com/drive/folders/1Uik0fMk4oquV_bS9lXTZuExAYbIDkEMW?usp=sharing
The proposed approach utilizes the associated denoscription text of items to learn transferable representations across different recommendation scenarios.
Github: https://github.com/rucaibox/unisrec
Paper: https://arxiv.org/abs/2206.05941v1
Google Drive: https://drive.google.com/drive/folders/1Uik0fMk4oquV_bS9lXTZuExAYbIDkEMW?usp=sharing
🔥8👍3
🔘 Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
Github: https://github.com/kssteven418/squeezeformer
Paper: https://arxiv.org/abs/2206.00888v1
Dataset: https://paperswithcode.com/dataset/librispeech
@ai_machinelearning_big_data
Github: https://github.com/kssteven418/squeezeformer
Paper: https://arxiv.org/abs/2206.00888v1
Dataset: https://paperswithcode.com/dataset/librispeech
@ai_machinelearning_big_data
👍12❤3