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
@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