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
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
👍6
📌 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
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
👍4
🔹 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
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
👍5
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
Github: https://github.com/google/BIG-bench
Paper: https://arxiv.org/abs/2206.04615v1
Dataset: https://paperswithcode.com/dataset/glue
@ArtificialIntelligencedl
❤8
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
Github: https://github.com/bzhangGo/zero
Paper: https://arxiv.org/abs/2206.04571v1
Dataset: https://paperswithcode.com/dataset/must-c
@ArtificialIntelligencedl
👍6
🔊 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
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
Github: https://github.com/facebookresearch/meta-ot
Paper: https://arxiv.org/abs/2206.05262v1
@ArtificialIntelligencedl
👍2
🔦 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
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
👍4
UniSRec
The proposed approach utilizes the associated denoscription 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
The proposed approach utilizes the associated denoscription 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
🔥3
🌑 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
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
👍2
Forwarded from Machinelearning
🖊 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
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
👍5
DeepFormableTag: End-to-end Generation and Recognition of Deformable Fiducial Markers
Github: https://github.com/KAIST-VCLAB/DeepFormableTag
Project: http://vclab.kaist.ac.kr/siggraph2021/index.html
Paper: http://vclab.kaist.ac.kr/siggraph2021/DeepFormableTag-main-screen.pdf
Dataset: https://drive.google.com/drive/folders/1picphIb6Hbj6pM3Wu_Vxu53wzKBV0jdV
@ArtificialIntelligencedl
Github: https://github.com/KAIST-VCLAB/DeepFormableTag
Project: http://vclab.kaist.ac.kr/siggraph2021/index.html
Paper: http://vclab.kaist.ac.kr/siggraph2021/DeepFormableTag-main-screen.pdf
Dataset: https://drive.google.com/drive/folders/1picphIb6Hbj6pM3Wu_Vxu53wzKBV0jdV
@ArtificialIntelligencedl
👍5
🏙 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
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
👍5
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
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
🔥6👍2
🦾 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
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
👍6
🕹 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
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
👍5
🔎 Object Structural Points Representation for Graph-based Semantic Monocular Localization and Mapping
Github:https://github.com/airlab-polimi/c-slam
Tutorial: http://ros.org/wiki/catkin/Tutorials/create_a_workspace
Paper: https://arxiv.org/abs/2206.10263v1
@ArtificialIntelligencedl
Github:https://github.com/airlab-polimi/c-slam
Tutorial: http://ros.org/wiki/catkin/Tutorials/create_a_workspace
Paper: https://arxiv.org/abs/2206.10263v1
@ArtificialIntelligencedl
👍5
🧠 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
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
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
👍7
💻 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 : http://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 : http://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
👍7
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
🔥2