24 citations · 32 across the 13 of their papers we have counts for
4 papers · 1 filter
Augmenting Neural Networks with First-order Logic
Tao Li, Vivek Srikumar
Today, the dominant paradigm for training neural networks involves minimizing task loss on a large dataset. Using world knowledge to inform a model, and yet retain the ability to p…
Learning In Practice: Reasoning About Quantization
Annie Cherkaev, Waiming Tai, Jeff Phillips +1
There is a mismatch between the standard theoretical analyses of statistical machine learning and how learning is used in practice. The foundational assumption supporting the theor…
Learning to Speed Up Structured Output Prediction
Xingyuan Pan, Vivek Srikumar
Predicting structured outputs can be computationally onerous due to the combinatorially large output spaces. In this paper, we focus on reducing the prediction time of a trained bl…
Newton: Gravitating Towards the Physical Limits of Crossbar Acceleration
Anirban Nag, Ali Shafiee, Rajeev Balasubramonian +2
Many recent works have designed accelerators for Convolutional Neural Networks (CNNs). While digital accelerators have relied on near data processing, analog accelerators have furt…