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GDC- Generalized Distribution Calibration for Few-Shot Learning
Few shot learning is an important problem in machine learning as large labelled datasets take considerable time and effort to assemble.
Learning Robust Latent Representations for Controllable Speech Synthesis
State-of-the-art Variational Auto-Encoders (VAEs) for learning disentangled latent representations give impressive results in discovering features like pitch, pause duration, and accent in speech data, leading to highly controllable text-to-speech (TTS) synthesis.
A Financial Service Chatbot based on Deep Bidirectional Transformers
Our main novel contribution is the discussion about uncertainty measure for BERT, where three different approaches are systematically compared on real problems.
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