The Cat is on the Mat

Interesting approach to be combined with Ngram embeddings when span boundaries are fuzzy.

I guess can be used downstream with existing sentence parsers.

Such models can be rough and dirty, cheap to train and robust.

- https://explosion.ai/blog/spancat

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🔊 SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning

We introduce SoundSpaces 2.0, a platform for on-the-fly geometry-based audio rendering for 3D environments.

Github: https://github.com/facebookresearch/sound-spaces

Paper: https://arxiv.org/pdf/2206.08312v1

Dataset: https://paperswithcode.com/dataset/librispeech

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🦾 Bi-DexHands: Bimanual Dexterous Manipulation via Reinforcement Learning

Bi-DexHands provides a collection of bimanual dexterous manipulations tasks and reinforcement learning algorithms.

Github: https://github.com/pku-marl/dexteroushands

Isaac Gym: https://developer.nvidia.com/isaac-gym

Paper: https://arxiv.org/pdf/2206.08686

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🗾 Insubstantial Object Detection

Dataset comprised of 600 videos (141,017 frames) covering various distances, sizes, visibility, and scenes captured by different spectral ranges.

Github: https://github.com/calayzhou/iod-video

Project: https://calayzhou.github.io/

Paper: https://arxiv.org/pdf/2206.11459v1

Dataset: https://paperswithcode.com/dataset/coco

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🧠 Identifying and Combating Bias in Segmentation Networks by leveraging multiple resolutions

Github: https://github.com/Deep-MI/FastSurfer

Colab: https://colab.research.google.com/github/Deep-MI/FastSurfer/blob/master/Tutorial/Tutorial_FastSurferCNN_QuickSeg.ipynb

Paper: https://arxiv.org/pdf/2206.14919v1

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🔥 SeqDeepFake: Detecting and Recovering Sequential DeepFake Manipulation

First Seq-DeepFake dataset, where face images are manipulated sequentially with corresponding annotations of sequential facial manipulation vectors.

Github: https://github.com/rshaojimmy/seqdeepfake

Project: https://rshaojimmy.github.io/Projects/SeqDeepFake

Paper: https://arxiv.org/pdf/2207.02204.pdf

Dataset: https://paperswithcode.com/dataset/imagenet

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Deep Learning with Python
Second Edition Version 4

#book #DL #python


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Introduction to Data Science CS109A course materials by Harvard University are free and open for everyone!

1. Lecture notes
2. R code, Python notebooks
3. Lab material
4. Advanced sections

Learn here: https://harvard-iacs.github.io/2019-CS109A/pages/syllabus.html

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👀 Object Centric Open Vocabulary Detection

Object-centric alignment of the language embeddings from the CLIP model.

Github: https://github.com/hanoonaR/object-centric-ovd

Paper: https://arxiv.org/pdf/2207.03482v1

Dataset: https://paperswithcode.com/dataset/imagenet

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🚀 Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting

Github: https://github.com/hikopensource/davar-lab-ocr

Paper: https://arxiv.org/pdf/2207.06694v1

Dataset: https://paperswithcode.com/dataset/total-text

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Data Visualization Cheat sheets

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Language Modelling with Pixels

PIXEL is a language model that operates on text rendered as images, fully removing the need for a fixed vocabulary.

Github: https://github.com/xplip/pixel

Paper: https://arxiv.org/pdf/2207.06991v1

Dataset: https://paperswithcode.com/dataset/glue

Pretrained: https://huggingface.co/Team-PIXEL/pixel-base

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Comprehensive Guide on Hierarchical Clustering

#ML

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⚡️ CLOSE: Curriculum Learning On the Sharing Extent Towards Better One-shot NAS

Github: https://github.com/walkerning/aw_nas

Paper: https://arxiv.org/pdf/2207.07868v1

Dataset: https://paperswithcode.com/dataset/nas-bench-201

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A classification model using KNN algorithm to identify correct labels based on the fashion images. Fashion-MNIST is a dataset of Zalando's article images consisting of a training set of 60,000 examples and a test set of 10,000 examples.

Each example is a 28x28 grayscale image, associated with a label from 10 classes. Each training and test example is assigned to one of the following labels: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle boot.

#machinelearning #ml #classification #zalando #fashion #tech

Credit: Fazil Mohammed


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KGI (Knowledge Graph Induction) for slot filling

Github: https://github.com/ibm/kgi-slot-filling

KILT data and knowledge source: https://github.com/facebookresearch/KILT

Paper: https://arxiv.org/pdf/2207.06300v1

Dataset: https://paperswithcode.com/dataset/natural-questions

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Essential Python Tools

Free Book: Click Me

#python #tools #cookbook

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