π» DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation
DaisyRec-v2.0 is a Python toolkit developed for benchmarking top-N recommendation task.
Github: https://github.com/recsys-benchmark/daisyrec-v2.0
Command Generator : https://daisyrecguicommandgenerator.pythonanywhere.com/
Paper: https://arxiv.org/abs/2206.10848v1
Tutorial: https://github.com/recsys-benchmark/DaisyRec-v2.0/blob/main/DaisyRec-v2.0-Tutorial.ipynb
@ArtificialIntelligencedl
DaisyRec-v2.0 is a Python toolkit developed for benchmarking top-N recommendation task.
Github: https://github.com/recsys-benchmark/daisyrec-v2.0
Command Generator : https://daisyrecguicommandgenerator.pythonanywhere.com/
Paper: https://arxiv.org/abs/2206.10848v1
Tutorial: https://github.com/recsys-benchmark/DaisyRec-v2.0/blob/main/DaisyRec-v2.0-Tutorial.ipynb
@ArtificialIntelligencedl
π7
Frequency Dynamic Convolution-Recurrent Neural Network (FDY-CRNN) for Sound Event Detection
Frequency Dynamic Convolution applied kernel that adapts to each freqeuncy bin of input, in order to remove tranlation equivariance of 2D convolution along the frequency axis.
Github: https://github.com/frednam93/FDY-SED
Paper: https://arxiv.org/abs/2206.11645v1
Dataset: https://paperswithcode.com/dataset/desed
@ArtificialIntelligencedl
Frequency Dynamic Convolution applied kernel that adapts to each freqeuncy bin of input, in order to remove tranlation equivariance of 2D convolution along the frequency axis.
Github: https://github.com/frednam93/FDY-SED
Paper: https://arxiv.org/abs/2206.11645v1
Dataset: https://paperswithcode.com/dataset/desed
@ArtificialIntelligencedl
π₯2
π
Retrosynthetic Planning with Retro*
graph-based search policy that eliminates the redundant explorations of any intermediate molecules.
Github: https://github.com/binghong-ml/retro_star
Paper: https://arxiv.org/abs/2206.11477v1
Dataset: https://www.dropbox.com/s/ar9cupb18hv96gj/retro_data.zip?dl=0
@ArtificialIntelligencedl
graph-based search policy that eliminates the redundant explorations of any intermediate molecules.
Github: https://github.com/binghong-ml/retro_star
Paper: https://arxiv.org/abs/2206.11477v1
Dataset: https://www.dropbox.com/s/ar9cupb18hv96gj/retro_data.zip?dl=0
@ArtificialIntelligencedl
π₯4
DDPM-CD: Remote Sensing Change Detection using Denoising Diffusion Probabilistic Models
Github: https://github.com/wgcban/ddpm-cd
Project: https://www.wgcban.com/research#h.ar24vwqlm021
Paper: https://arxiv.org/abs/2206.11892
Dataset: https://paperswithcode.com/dataset/fmow
@ArtificialIntelligencedl
Github: https://github.com/wgcban/ddpm-cd
Project: https://www.wgcban.com/research#h.ar24vwqlm021
Paper: https://arxiv.org/abs/2206.11892
Dataset: https://paperswithcode.com/dataset/fmow
@ArtificialIntelligencedl
π2
SETR - Pytorch
Github: https://github.com/920232796/setr-pytorch
Paper: https://arxiv.org/abs/2206.11520v1
Dataset: https://www.kaggle.com/c/carvana-image-masking-challenge/data
@ArtificialIntelligencedl
Github: https://github.com/920232796/setr-pytorch
Paper: https://arxiv.org/abs/2206.11520v1
Dataset: https://www.kaggle.com/c/carvana-image-masking-challenge/data
@ArtificialIntelligencedl
π3
Complementary datasets to COCO for object detection
Github: https://github.com/aliborji/coco_oi
Paper: https://arxiv.org/abs/2206.11473v1
Dataset: https://paperswithcode.com/dataset/coco
@ArtificialIntelligencedl
Github: https://github.com/aliborji/coco_oi
Paper: https://arxiv.org/abs/2206.11473v1
Dataset: https://paperswithcode.com/dataset/coco
@ArtificialIntelligencedl
π₯°1
β‘οΈ K-CAI NEURAL API
KCAI NEURAL API Keras based neural network API that will allow you to prototype
Github: https://github.com/joaopauloschuler/k-neural-api
Colab: https://colab.research.google.com/github/joaopauloschuler/k-neural-api/blob/master/examples/jupyter/simple_image_classification_with_any_dataset.ipynb
Paper: https://www.researchgate.net/publication/360226228_Grouped_Pointwise_Convolutions_Reduce_Parameters_in_Convolutional_Neural_Networks
Dataset: https://paperswithcode.com/dataset/plantdoc
@ArtificialIntelligencedl
KCAI NEURAL API Keras based neural network API that will allow you to prototype
Github: https://github.com/joaopauloschuler/k-neural-api
Colab: https://colab.research.google.com/github/joaopauloschuler/k-neural-api/blob/master/examples/jupyter/simple_image_classification_with_any_dataset.ipynb
Paper: https://www.researchgate.net/publication/360226228_Grouped_Pointwise_Convolutions_Reduce_Parameters_in_Convolutional_Neural_Networks
Dataset: https://paperswithcode.com/dataset/plantdoc
@ArtificialIntelligencedl
π₯4
π‘ Denoised MDPs: Learning World Models Better Than the World Itself
Github: https://github.com/facebookresearch/denoised_mdp
Project: https://ssnl.github.io/denoised_mdp
Paper: https://arxiv.org/abs/2206.15477v1
Dataset: https://paperswithcode.com/dataset/deepmind-control-suite
@ArtificialIntelligencedl
Github: https://github.com/facebookresearch/denoised_mdp
Project: https://ssnl.github.io/denoised_mdp
Paper: https://arxiv.org/abs/2206.15477v1
Dataset: https://paperswithcode.com/dataset/deepmind-control-suite
@ArtificialIntelligencedl
π4β€1
π² Forecasting Future World Events with Neural Networks
Github: https://github.com/andyzoujm/autocast
Paper: https://arxiv.org/abs/2206.15474v1
Dataset: https://people.eecs.berkeley.edu/~hendrycks/intervalqa.tar.gz
@ArtificialIntelligencedl
Github: https://github.com/andyzoujm/autocast
Paper: https://arxiv.org/abs/2206.15474v1
Dataset: https://people.eecs.berkeley.edu/~hendrycks/intervalqa.tar.gz
@ArtificialIntelligencedl
π4
BigBIO: Biomedical Datasets
Currently BigBIO provides support for
more than 120 biomedical datasets
10 languages
Harmonized dataset schemas by task type
Metadata on licensing, coarse/fine-grained task types, domain, and more!
Github: https://github.com/bigscience-workshop/biomedical
Paper: https://arxiv.org/abs/2206.15076v1
Dataset: https://paperswithcode.com/dataset/bioasq
@ArtificialIntelligencedl
Currently BigBIO provides support for
more than 120 biomedical datasets
10 languages
Harmonized dataset schemas by task type
Metadata on licensing, coarse/fine-grained task types, domain, and more!
Github: https://github.com/bigscience-workshop/biomedical
Paper: https://arxiv.org/abs/2206.15076v1
Dataset: https://paperswithcode.com/dataset/bioasq
@ArtificialIntelligencedl
π2π₯1
π MMFN: Multi-Modal Fusion Net for End-to-End Autonomous Driving
A novel approach to efficiently extract features from vectorized High-Definition (HD) maps and utilize them in the end-to-end driving tasks.
Github: https://github.com/Kin-Zhang/mmfn
Paper: https://arxiv.org/abs/2207.00186v1
Dataset: https://github.com/carla-simulator/leaderboard/issues/81
@ArtificialIntelligencedl
A novel approach to efficiently extract features from vectorized High-Definition (HD) maps and utilize them in the end-to-end driving tasks.
Github: https://github.com/Kin-Zhang/mmfn
Paper: https://arxiv.org/abs/2207.00186v1
Dataset: https://github.com/carla-simulator/leaderboard/issues/81
@ArtificialIntelligencedl
π4
AlphaCode Explained: AI Code Generation
AlphaCode is DeepMind's new massive language model for generating code. It is similar to OpenAI Codex, except for in the paper they provide a bit more analysis. The field of NLP within AI and ML has exploded get a lot more papers all the time. This video can help you understand how AlphaCode works and what some of the key takeaways are.
youtube: https://www.youtube.com/watch?v=t3Yh56efKGI
blog post: https://deepmind.com/blog/article/Competitive-programming-with-AlphaCode
paper: https://storage.googleapis.com/deepmind-media/AlphaCode/competition_level_code_generation_with_alphacode.pdf
AlphaCode is DeepMind's new massive language model for generating code. It is similar to OpenAI Codex, except for in the paper they provide a bit more analysis. The field of NLP within AI and ML has exploded get a lot more papers all the time. This video can help you understand how AlphaCode works and what some of the key takeaways are.
youtube: https://www.youtube.com/watch?v=t3Yh56efKGI
blog post: https://deepmind.com/blog/article/Competitive-programming-with-AlphaCode
paper: https://storage.googleapis.com/deepmind-media/AlphaCode/competition_level_code_generation_with_alphacode.pdf
π7π3π€1
PIAFusion: A progressive infrared and visible image fusion network based on illumination aware
Github: https://github.com/Linfeng-Tang/PIAFusion
Paper: https://www.sciencedirect.com/science/article/abs/pii/S156625352200032X
Pytorch: https://github.com/linklist2/PIAFusion_pytorch
@ArtificialIntelligencedl
Github: https://github.com/Linfeng-Tang/PIAFusion
Paper: https://www.sciencedirect.com/science/article/abs/pii/S156625352200032X
Pytorch: https://github.com/linklist2/PIAFusion_pytorch
@ArtificialIntelligencedl
π5
Disentangling Random and Cyclic Effects in Time-Lapse Sequences
Github: https://github.com/harskish/tlgan
Paper: https://arxiv.org/abs/2207.01413v1
Dataset: https://github.com/harskish/tlgan/blob/master/docs/PREPROC.md
Pre-trained models: https://drive.google.com/drive/folders/1ZA7Gk2OIFI2cANHEHHAm3AdWLMjJCExE?usp=sharing
@ArtificialIntelligencedl
Github: https://github.com/harskish/tlgan
Paper: https://arxiv.org/abs/2207.01413v1
Dataset: https://github.com/harskish/tlgan/blob/master/docs/PREPROC.md
Pre-trained models: https://drive.google.com/drive/folders/1ZA7Gk2OIFI2cANHEHHAm3AdWLMjJCExE?usp=sharing
@ArtificialIntelligencedl
π6π₯2
Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation
Github: https://github.com/haomo-ai/motionseg3d
Paper: https://arxiv.org/abs/2207.02201v1
Dataset: https://paperswithcode.com/dataset/lidar-mos
@ArtificialIntelligencedl
Github: https://github.com/haomo-ai/motionseg3d
Paper: https://arxiv.org/abs/2207.02201v1
Dataset: https://paperswithcode.com/dataset/lidar-mos
@ArtificialIntelligencedl
π5
β¨ Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification
Tip-Adapter is a training-free adaption method for CLIP to conduct few-shot classification.
Github: https://github.com/gaopengcuhk/tip-adapter
Paper: https://arxiv.org/abs/2207.09519v1
Dataset: https://paperswithcode.com/dataset/oxford-102-flower
Tip-Adapter is a training-free adaption method for CLIP to conduct few-shot classification.
Github: https://github.com/gaopengcuhk/tip-adapter
Paper: https://arxiv.org/abs/2207.09519v1
Dataset: https://paperswithcode.com/dataset/oxford-102-flower
π1π₯1
Using Activation Functions in Neural Networks
https://machinelearningmastery.com/using-activation-functions-in-neural-networks/
@ArtificialIntelligencedl
https://machinelearningmastery.com/using-activation-functions-in-neural-networks/
@ArtificialIntelligencedl
π6
FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling
Github: https://github.com/timothyhtimothy/fast-vqa
Paper: https://arxiv.org/abs/2207.02595v1
Dataset: https://paperswithcode.com/dataset/kinetics
@ArtificialIntelligencedl
Github: https://github.com/timothyhtimothy/fast-vqa
Paper: https://arxiv.org/abs/2207.02595v1
Dataset: https://paperswithcode.com/dataset/kinetics
@ArtificialIntelligencedl
π8
Pre-training helps Bayesian optimization too
Github: https://github.com/google-research/hyperbo
Paper: https://arxiv.org/abs/2207.03084v1
Tensorflow Probability : https://www.tensorflow.org/probability
@ArtificialIntelligencedl
Github: https://github.com/google-research/hyperbo
Paper: https://arxiv.org/abs/2207.03084v1
Tensorflow Probability : https://www.tensorflow.org/probability
@ArtificialIntelligencedl
π9π₯1π₯°1
π Domain Adaptive Video Segmentation via Temporal Pseudo Supervision
Github: https://github.com/xing0047/tps
Paper: https://arxiv.org/abs/2207.02372v1
Dataset : https://paperswithcode.com/dataset/cityscapesprobability
@ArtificialIntelligencedl
Github: https://github.com/xing0047/tps
Paper: https://arxiv.org/abs/2207.02372v1
Dataset : https://paperswithcode.com/dataset/cityscapesprobability
@ArtificialIntelligencedl
π9