#WeAreHiring! As a System QA Test Sr Manager you'll be part of our product development cycles, collaborating across #Software, #Firmware, #SystemEngineering, and #Silicon. Join and make your mark on our current and future #ML ASIC-based products. Apply at https://
groq.com/careers/?gh_ji
d=5575906003
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MACHINE LEARNING
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Groq Hiring Senior Manager for System QA Testing
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AI in the Public Interest: Why Current Approaches Are Failing
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If We Want AI In The Public Interest, We’re Doing It Wrong
#AI #AIio #BigData #ML #NLU #Futureofwork http://
ow.ly/pP8J30svScP -
Semi-DETR: Transformer-Based Semi-Supervised Object Detection
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We proposed Semi-DETR, the first Transformer-based end-to-end semi-supervised object detector. Results outperform all SOTA methods on COCO & Pascal VOC by clear margins. Read more: https://
openaccess.thecvf.com/content/CVPR20
23/papers/Zhang_Semi-DETR_Semi-Supervised_Object_Detection_With_Detection_Transformers_CVPR_2023_paper.pdf
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Facebook Research MMS Examples on GitHub Repository
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github: https://
github.com/facebookresear
ch/fairseq/tree/main/examples/mms
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Facebook AI Multilingual Speech Recognition Model
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blog: https://ai.facebook.com/blog/multilingual-model-speech-recognition/
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Documentation of MMS Transformers on Hugging Face
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docs: https://huggingface.co/docs/transformers/main/en/model_doc/mms
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Fine-tuning MMS Adapters on Hugging Face
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fine-tuning: https://
huggingface.co/blog/mms_adapt
ers
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Three Core Steps of Exploratory Data Analysis Process
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The process of EDA fundamentally comprises three main tasks: Step 1: Dataset Overview and Descriptive Statistics
Step 2: Feature Assessment and Visualization, and
Step 3: Data Quality Evaluation https://
bit.ly/3PnbX37 -
MMS Integration in Transformers: Easy Inference and Fine-Tuning
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5 / 5 We've integrated MMS into Transformers making the models very easy to use for both inference and fine-tuning. Demo https://
huggingface.co/spaces/faceboo
k/MMS
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Docs https://
huggingface.co/docs/transform
ers/main/en/model_doc/mms
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Fine-Tuning -
MMS Adapters: Training Efficient Multilingual Language Models
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4 / 5 To adapt to 1000+ different vocabularies, MMS uses Adapters – a training method where only a small fraction of model weights are trained Adapter layers act like linguistic bridges, enabling the model to leverage knowledge from one language when deciphering another.