183 citations · 373 across the 3 of their papers we have counts for
4 papers
A Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networks
Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja +1
We present a survey of ways in which existing scientific knowledge are included when constructing models with neural networks. The inclusion of domain-knowledge is of special inter…
Widening Access to Applied Machine Learning with TinyML
Vijay Janapa Reddi, Brian Plancher, Susan Kennedy +21
Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloe…
Incorporating Domain Knowledge into Deep Neural Networks
Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja +1
We present a survey of ways in which domain-knowledge has been included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not…
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning
Srivatsan Krishnan, Maximilian Lam, Sharad Chitlangia +4
Deep reinforcement learning continues to show tremendous potential in achieving task-level autonomy, however, its computational and energy demands remain prohibitively high. In thi…