53 citations · 91 across the 7 of their papers we have counts for
7 papers
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"
Saeed Amizadeh, Hamid Palangi, Oleksandr Polozov +2
Visual reasoning tasks such as visual question answering (VQA) require an interplay of visual perception with reasoning about the question semantics grounded in perception. However…
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim +4
Classical Machine Learning (ML) pipelines often comprise of multiple ML models where models, within a pipeline, are trained in isolation. Conversely, when training neural network m…
Machine Learning at Microsoft with ML .NET
Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko +31
Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate t…
PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers
Saeed Amizadeh, Sergiy Matusevych, Markus Weimer
There have been recent efforts for incorporating Graph Neural Network models for learning full-stack solvers for constraint satisfaction problems (CSP) and particularly Boolean sat…
Coded Elastic Computing
Yaoqing Yang, Matteo Interlandi, Pulkit Grover +3
Cloud providers have recently introduced new offerings whereby spare computing resources are accessible at discounts compared to on-demand computing. Exploiting such opportunity is…
The Bregman Variational Dual-Tree Framework
Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht
Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning. In general, the memory and computational complexity of these methods is quad…
Variational Dual-Tree Framework for Large-Scale Transition Matrix Approximation
Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht
In recent years, non-parametric methods utilizing random walks on graphs have been used to solve a wide range of machine learning problems, but in their simplest form they do not s…