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Register a new userConsistent Accelerated Inference via Confident Adaptive Transformers
الاستدلال المتسابقين متسقين عبر المحولات التكيفية الواثقة
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نقوم بتطوير نهج رواية للاستدلال بثقة في المحولات متعددة الطبقات الكبيرة والمكلفة الآن في كل مكان في معالجة اللغة الطبيعية (NLP).تؤدي الأساليب الحسابية المطفأة أو التقريبية إلى زيادة الكفاءة، ولكن يمكن أن تأتي مع تكاليف أداء غير متوقعة.في هذا العمل، نقدم القطط - محولات تكيفية واثقة - حيث نزيد في وقت واحد من الكفاءة الحسابية، مع ضمان درجة تحديد الحاسمة مع النموذج الأصلي بثقة عالية.تقوم طريقةنا بتدريب رؤوس التنبؤ الإضافية على رأس الطبقات الوسيطة، وتقريرها بشكل حيوي عند إيقاف تخصيص الجهود الحسابية لكل إدخال باستخدام مصنف تناسق التعريف.لمعايرة التوقعات المبكرة لدينا الحكم، نقوم بصياغة امتداد فريد من التنبؤ المطابق.نوضح فعالية هذا النهج في أربعة مهام التصنيف والانحدار.
We develop a novel approach for confidently accelerating inference in the large and expensive multilayer Transformers that are now ubiquitous in natural language processing (NLP). Amortized or approximate computational methods increase efficiency, but can come with unpredictable performance costs. In this work, we present CATs -- Confident Adaptive Transformers -- in which we simultaneously increase computational efficiency, while guaranteeing a specifiable degree of consistency with the original model with high confidence. Our method trains additional prediction heads on top of intermediate layers, and dynamically decides when to stop allocating computational effort to each input using a meta consistency classifier. To calibrate our early prediction stopping rule, we formulate a unique extension of conformal prediction. We demonstrate the effectiveness of this approach on four classification and regression tasks.
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This work demonstrates the development process of a machine learning architecture for inference that can scale to a large volume of requests. We used a BERT model that was fine-tuned for emotion analysis, returning a probability distribution of emoti
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