Tutel: Adaptive Mixture-of-Experts at Scale

Tutel, a highly scalable stack design and implementation for MoE with dynamically adaptive parallelism and pipelining.

Github: https://github.com/microsoft/tutel

Examples: https://github.com/microsoft/tutel/blob/main/tutel/examples

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

Documentation: https://ontomerger.readthedocs.io/

@ArtificialIntelligencedl

📌 Sparse Fusion Mixture-of-Experts are Domain Generalizable Learners

Sparse Fusion Mixture-of-Experts (SF-MoE), which incorporates sparsity and fusion mechanisms into the MoE framework to keep the model both sparse and predictive.

Github: https://github.com/luodian/sf-moe-dg

Paper: https://arxiv.org/abs/2206.04046v1

Documentation: https://paperswithcode.com/dataset/domainnet

@ArtificialIntelligencedl

🔹 PointNeXt & OpenPoints Library

improved training and model scaling strategies to boost PointNet++ to the state-of-the-art level.

Github: https://github.com/guochengqian/pointnext

Paper: https://paperswithcode.com/dataset/shapenet

@ArtificialIntelligencedl

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Github: https://github.com/google/BIG-bench

Paper: https://arxiv.org/abs/2206.04615v1

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

@ArtificialIntelligencedl

Revisiting End-to-End Speech-to-Text Translation From Scratch

Github: https://github.com/bzhangGo/zero

Paper: https://arxiv.org/abs/2206.04571v1

Dataset: https://paperswithcode.com/dataset/must-c

@ArtificialIntelligencedl

🔊 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/abs/2206.08312v1

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

Meta Optimal Transport

Github: https://github.com/facebookresearch/meta-ot

Paper: https://arxiv.org/abs/2206.05262v1



@ArtificialIntelligencedl

🔦 Featurized Query R-CNN

Featurized object queries predicted by a query generation network in the well-established Faster R-CNN framework and develop a Featurized Query R-CN

Github: https://github.com/hustvl/featurized-queryrcnn

Paper: https://arxiv.org/abs/2206.06258v1

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

@ArtificialIntelligencedl

UniSRec

The proposed approach utilizes the associated description 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

@ArtificialIntelligencedl

🌑 LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D Detection

Waymo Open Dataset publicly to aid the research community in making advancements in machine perception and autonomous driving technology.

Github: https://github.com/waymo-research/waymo-open-dataset

Paper: https://arxiv.org/abs/2206.07705v1

Dataset: https://paperswithcode.com/dataset/waymo-open-datasetg

@ArtificialIntelligencedl

🖊 StrengthNet

Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis"

Github: https://github.com/ttslr/strengthnet

Paper: https://arxiv.org/abs/2110.03156

MOSNet: https://github.com/lochenchou/MOSNet

@ai_machinelearning_big_data

DeepFormableTag: End-to-end Generation and Recognition of Deformable Fiducial Markers

Github: https://github.com/KAIST-VCLAB/DeepFormableTag

Project: https://vclab.kaist.ac.kr/siggraph2021/index.html

Paper: https://vclab.kaist.ac.kr/siggraph2021/DeepFormableTag-main-screen.pdf

Dataset: https://drive.google.com/drive/folders/1picphIb6Hbj6pM3Wu_Vxu53wzKBV0jdV

@ArtificialIntelligencedl

🏙 Spatially-Adapive Multilayer (SAM) Inversion

Proposed method automatically selects the latent space tailored for each region to balance the reconstruction quality and editability (3rd row).

Github: https://github.com/adobe-research/sam_inversion

Project: https://www.cs.cmu.edu/~SAMInversion/

Paper: https://arxiv.org/abs/2206.08357

@ArtificialIntelligencedl

Automatic Prosody Annotation with Pre-Trained Text-Speech Model

Github: https://github.com/daisyqk/automatic-prosody-annotation

Project: https://daisyqk.github.io/Automatic-Prosody-Annotation_w/

Paper: https://arxiv.org/abs/2206.07956v1

@ArtificialIntelligencedl

🦾 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: hhttps://arxiv.org/abs/2206.08686

@ArtificialIntelligencedl

🕹 SENSORIUM 2022 Competition

The Sensorium competition on predicting large-scale mouse primary visual cortex activity

Github: https://github.com/sinzlab/sensorium

Website: https://sensorium2022.net/

Paper: https://arxiv.org/abs/2206.08666v1

@ArtificialIntelligencedl

🔎 Object Structural Points Representation for Graph-based Semantic Monocular Localization and Mapping

Github:https://github.com/airlab-polimi/c-slam

Tutorial: https://ros.org/wiki/catkin/Tutorials/create_a_workspace

Paper: https://arxiv.org/abs/2206.10263v1

@ArtificialIntelligencedl

🧠 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/abs/2206.14919v1

🌩 Object Structural Points Representation for Graph-based Semantic Monocular Localization and Mapping

PyGOD is a Python library for graph outlier detection (anomaly detection).

Github: https://github.com/pygod-team/pygod

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

Paper: https://arxiv.org/abs/2206.10071v1

@ArtificialIntelligencedl

💻 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