A Smooth Representation of SO(3) for Deep Rotation Learning with Uncertainty
@Machine_learn
Website: https://papers.starslab.ca/bingham-rotation-learning/
Paper: https://arxiv.org/abs/2006.01031
Github: https://github.com/utiasSTARS/bingham-rotation-learn
@Machine_learn
Website: https://papers.starslab.ca/bingham-rotation-learning/
Paper: https://arxiv.org/abs/2006.01031
Github: https://github.com/utiasSTARS/bingham-rotation-learn
@Machine_learn
YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS
https://blog.roboflow.ai/yolov5-is-here/
Github: https://github.com/ultralytics/yolov5
GCP Quickstart: https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart
YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS
https://blog.roboflow.ai/yolov5-is-here/
Github: https://github.com/ultralytics/yolov5
GCP Quickstart: https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart
Roboflow Blog
YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS
Less than 50 days after the release YOLOv4, YOLOv5 improves accessibility for realtime object detection.
June 29, YOLOv5 has released the first official version of the repository. We wrote a new deep dive on YOLOv5.
June 12, 8:08 AM CDT Update: In response…
June 29, YOLOv5 has released the first official version of the repository. We wrote a new deep dive on YOLOv5.
June 12, 8:08 AM CDT Update: In response…
@Machine_learn
AR-Net: A simple autoregressive NN for #timeSeries
blog: https://ai.facebook.com/blog/ar-net-a-simple-autoregressive-neural-network-for-time-series/
paper: https://arxiv.org/abs/1911.03118
AR-Net: A simple autoregressive NN for #timeSeries
blog: https://ai.facebook.com/blog/ar-net-a-simple-autoregressive-neural-network-for-time-series/
paper: https://arxiv.org/abs/1911.03118
@Machine_learn
VirTex: Learning Visual Representations from Textual Annotations
https://kdexd.github.io/virtex/
Github: https://github.com/kdexd/virtex
Paper: arxiv.org/abs/2006.06666
VirTex: Learning Visual Representations from Textual Annotations
https://kdexd.github.io/virtex/
Github: https://github.com/kdexd/virtex
Paper: arxiv.org/abs/2006.06666
GitHub
GitHub - kdexd/virtex: [CVPR 2021] VirTex: Learning Visual Representations from Textual Annotations
[CVPR 2021] VirTex: Learning Visual Representations from Textual Annotations - kdexd/virtex
@Macine_learn
Fine-tuning ResNet with Keras, TensorFlow, and Deep Learning
https://www.pyimagesearch.com/2020/04/27/fine-tuning-resnet-with-keras-tensorflow-and-deep-learning/
Fine-tuning ResNet with Keras, TensorFlow, and Deep Learning
https://www.pyimagesearch.com/2020/04/27/fine-tuning-resnet-with-keras-tensorflow-and-deep-learning/
Segmentation Loss Odyssey
@Machine_learn
Github: https://github.com/JunMa11/SegLoss
Paper: https://arxiv.org/abs/2005.13449v1
@Machine_learn
Github: https://github.com/JunMa11/SegLoss
Paper: https://arxiv.org/abs/2005.13449v1
@Mchine_learn
Neural Manifold Ordinary Differential Equations
Article: https://arxiv.org/abs/2006.10254
Github: https://github.com/CUVL/Neural-Manifold-Ordinary-Differential-Equations
Neural Manifold Ordinary Differential Equations
Article: https://arxiv.org/abs/2006.10254
Github: https://github.com/CUVL/Neural-Manifold-Ordinary-Differential-Equations
@Machine_learn
BentoML
BentoML is an open-source platform for high-performance ML model serving.
https://github.com/bentoml/BentoML
bentoml/BentoML
BentoML
BentoML is an open-source platform for high-performance ML model serving.
https://github.com/bentoml/BentoML
bentoml/BentoML
GitHub
GitHub - bentoml/BentoML: The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi…
The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more! - bentoml/BentoML
Denoising Diffusion Probabilistic Models
@Machine_learn
https://hojonathanho.github.io/diffusion/
Github: https://github.com/hojonathanho/diffusion
Paper: https://arxiv.org/abs/2006.11239
@Machine_learn
https://hojonathanho.github.io/diffusion/
Github: https://github.com/hojonathanho/diffusion
Paper: https://arxiv.org/abs/2006.11239
Adversarial NLI: A New Benchmark for Natural Language Understanding
@Machine_learn
Dataset: https://github.com/facebookresearch/anli
Paper: https://arxiv.org/abs/1910.14599
@Machine_learn
Dataset: https://github.com/facebookresearch/anli
Paper: https://arxiv.org/abs/1910.14599
GitHub
GitHub - facebookresearch/anli: Adversarial Natural Language Inference Benchmark
Adversarial Natural Language Inference Benchmark. Contribute to facebookresearch/anli development by creating an account on GitHub.
cicv_22_losch.pdf
4.2 MB
Semantic Bottleneck Layers: Quantifying and Improving Inspectability of Deep Representations
#paper
@Machin_learn
#paper
@Machin_learn
@Machine_learn
8 Top Books on Data Cleaning and Feature Engineering
https://machinelearningmastery.com/books-on-data-cleaning-data-preparation-and-feature-engineering/
8 Top Books on Data Cleaning and Feature Engineering
https://machinelearningmastery.com/books-on-data-cleaning-data-preparation-and-feature-engineering/